DETAILED ACTION
A summary of this action:
Claims 1-20 have been presented for examination.
This action is non-Final.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process or mathematical concept without significantly more.
Step 1: Claims 1-10 are directed to a system, which is a system and is a statutory invention. Claims 11-20 are directed to a method, which is a process and is a statutory category invention. Therefore, claims 1-20 are directed to patent eligible categories of invention.
Claim 1
Step 2A, Prong 1: Independent claims 1 and 20, as drafted, are a process that, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “system,” and “processor,” nothing in the claim element precludes the step from practically being performed in the mind.
Independent claims 1 and 11 similarly recite determine peak temperature values and cooling rate values for each of the points of the weld based on the temperature values, which is an abstract idea and covers mental processes of assessing peak temperature values and cooling rates within the system that predicts microhardness properties of a weld, as described in [0007] of the specification, because the claims are derived from Mental Processes based on concepts performed in the human mind or with the aid of pencil and paper.
Independent claims 1 and 11 similarly recite predict a three-dimensional (3D) distribution of microhardness values of the weld based on a machine learning method that evaluates the peak temperature values and the cooling rate values, which is an abstract idea and covers mental processes of assessing the microhardness values of the weld and evaluating peak temperatures and cooling rate values, as described in [0007] of the specification, because the claims are derived from Mental Processes based on concepts performed in the human mind or with the aid of pencil and paper.
Independent claims 1 and 11 similarly recite generate display data based on the 3D distribution of microhardness values, which is an abstract idea and covers mental processes of assessing the microhardness values of the weld and evaluating peak temperatures and cooling rate values and then displaying that data based on the microhardness values, as described in [0007] of the specification, because the claims are derived from Mental Processes based on concepts performed in the human mind or with the aid of pencil and paper.
Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper.
Dependent claims 2-10 and 12-20 further narrow the abstract ideas, identified in the independent claims. See analysis below.
Step 2A, Prong 2: The judicial exception is not integrated into a practical application. Independent claims 1 and 11 and dependent claims 2-10 recite the additional limitation “system,” independent claims 1 and 11 and dependent claims 2-10 and 12-20 recite the additional limitation “processor,” and dependent claims 4-5 and 14-15 recite the additional limitation “temperature sensor,” this limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). Alternatively, this additional element merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)).
The additional recited claims 1 and 11 similarly recite receive temperature data that includes sensed or simulated temperature values each attributed to a corresponding one of a plurality of points of the weld at corresponding times during a welding process used to produce the weld, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)).
The additional recited claims 2 and 12 similarly recite receive composition data that includes compositions of the at least two workpieces, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)).
The additional recited claims 2 and 12 similarly recite receive material microhardness data that includes microhardness values of base metals of the at least two workpieces, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)).
The additional recited claims 4 and 14 similarly recite transmit the sensed temperature values to the processor as the temperature data, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)).
The additional recited claims 5 and 15 similarly recite transmit the sensed temperature values to the processor as the temperature data, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)).
The additional claim 6 and 16 similarly recite the limitation of provide the 3D distribution of microhardness values to a computer-aided engineering (CAE) tool as input data, can be viewed as is insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception is not integrated into a practical application.
The additional claim 6 and 16 similarly recite the limitation of perform an analysis with the CAE tool using the 3D distribution of microhardness values as input, can be viewed as is insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception is not integrated into a practical application.
The additional recited claims 9 and 19 similarly recite receive composition data that includes compositions the at least two workpieces, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)).
The additional recited claims 9 and 19 similarly recite receive material microhardness data that includes microhardness values of base metals of the at least two workpieces, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)).
Dependent claims 2-10 and 12-20 further narrow the abstract ideas, identified in the independent claims, and do not introduce further additional elements for consideration beyond those addressed above. The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application. Therefore, the dependent claims do not integrate the claimed invention into a practical application.
Step 2B:
The claims do not amount to significantly more. The judicial exception does not amount to significantly more. Independent claims 1 and 11 and dependent claims 2-10 recite the additional limitation “system,” independent claims 1 and 11 and dependent claims 2-10 and 12-20 recite the additional limitation “processor,” and “temperature sensor,” this limitation does not amount to significantly more because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). Alternatively, this additional element merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)).
The additional recited claims 1 and 11 similarly recite receive temperature data that includes sensed or simulated temperature values each attributed to a corresponding one of a plurality of points of the weld at corresponding times during a welding process used to produce the weld, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not amount to significantly more. (MPEP 2106.05(f)(2)).
The additional recited claims 2 and 12 similarly recite receive composition data that includes compositions of the at least two workpieces, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not amount to significantly more. (MPEP 2106.05(f)(2)).
The additional recited claims 2 and 12 similarly recite receive material microhardness data that includes microhardness values of base metals of the at least two workpieces, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not amount to significantly more. (MPEP 2106.05(f)(2)).
The additional recited claims 4 and 14 similarly recite transmit the sensed temperature values to the processor as the temperature data, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not amount to significantly more. (MPEP 2106.05(f)(2)).
The additional recited claims 5 and 15 similarly recite transmit the sensed temperature values to the processor as the temperature data, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not amount to significantly more. (MPEP 2106.05(f)(2)).
The additional claim 6 and 16 similarly recite the limitation of provide the 3D distribution of microhardness values to a computer-aided engineering (CAE) tool as input data, can be viewed as is insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and does not amount to significantly more. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception does not amount to significantly more.
The additional claim 6 and 16 similarly recite the limitation of perform an analysis with the CAE tool using the 3D distribution of microhardness values as input, can be viewed as is insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and does not amount to significantly more. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception does not amount to significantly more.
The additional recited claims 9 and 19 similarly recite receive composition data that includes compositions the at least two workpieces, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not amount to significantly more. (MPEP 2106.05(f)(2)).
The additional recited claims 9 and 19 similarly recite receive material microhardness data that includes microhardness values of base metals of the at least two workpieces, are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. See MPEP (2106.05(f)) use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process) does not amount to significantly more. (MPEP 2106.05(f)(2)).
Dependent claims 2-10 and 12-20 further narrow the abstract ideas, identified in the independent claims, and do not introduce further additional elements for consideration beyond those addressed above. The additional elements have been considered both individually and as an ordered combination in to determine whether they amount to significantly more. Therefore, the dependent claims does not amount to significantly more.
Therefore, the claims as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception.
As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claims 2 and 12 similarly recite “predict the 3D distribution of microhardness values of the weld based on the machine learning method that evaluates the peak temperature values, the cooling rate values, the compositions of the at least two workpieces, and the microhardness values of the base metals,” which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 3 and 13 similarly recite “simulate the temperature values using a welding process simulation model,” which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 3 and 13 similarly recite “generate the temperature data comprising the simulated temperature values,” which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 4 and 14 similarly recite “sense the temperature values of the welding process,” which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 4 and 14 similarly recite “wherein the at least two workpieces are formed of a mild steel,” which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 4 and 14 similarly recite “wherein the processor determines the cooling rate values in a temperature range of between about 800°C and 500°C,” which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 5 and 15 similarly recite “sense the temperature values during the welding process,” which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 5 and 15 similarly recite “wherein the at least two workpieces are formed of an advanced high strength steel,” which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 5 and 15 similarly recite “wherein the processor determines the cooling rate values in a temperature range of between about 750°C and 300°C,” which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 7 and 17 similarly recite “wherein the at least two workpieces are formed of an advanced high strength steel that includes a volume fraction of martensite,” which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 7 and 17 similarly recite “wherein a heat affected zone of the weld includes a tempered zone,” which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 7 and 17 similarly recite “at least some of the microhardness values of the 3D distribution are attributed to points within the tempered zone,” which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 7 and 17 similarly recite “predict the 3D distribution of microhardness values without using martensite tempering kinetics produced experimentally for the tempered zone,” which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 8 and 18 similarly recite “correlate, by the machine learning method, the 2D distribution of microhardness values with the peak temperature values and the cooling rate values of the weld to provide correlation results, which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 8 and 18 similarly recite “train a neural network with the correlation results to predict the 3D distribution of microhardness values of the weld, which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 9 and 19 similarly recite “correlate, by the machine learning method, the 2D distribution of microhardness values with the peak temperature values, the cooling rate values, the compositions, and the microhardness values to provide correlation results, which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 9 and 19 similarly recite “train a neural network with the correlation results to predict the 3D distribution of microhardness values of the weld, which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 10 and 20 similarly recite “measure the 2D distribution of microhardness values of the weld, which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Dependent claim 10 and 20 similarly recite “transmit the 2D distribution of microhardness values to the processor as the 2D distribution data, which further narrows the abstract idea identified in the independent claim, which is directed to a “Mental Process.”
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 6, 11, 13, and 16 is rejected under are rejected under 35 U.S.C. 103 as being unpatentable over DE FLIPPAS (Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network), herein DE FLIPPAS, in view of RAHMAN (Calculation of hardness distribution in the HAZ of micro-alloyed steel), herein RAHMAN, in view of XAVIER (Numerical Predictions for the Thermal History, Microstructure and Hardness Distributions at the HAZ during Welding of Low Alloy Steels), herein XAVIER, and in view of YU (Application of neural network-based hardness prediction method to HAZ of A533B steel produced by laser temper bead welding), herein YU.
Claim 1 is rejected because DE FLIPPAS teaches a system for predicting microhardness properties of a weld that defines a weld joint between at least two workpieces, the system comprising: a processor programmed to DE FLIPPAS ([Introduction] “In these cases, ANN can predict the output parameters after learning from a training data set, where the learning algorithm determines the numeric weights to the link among neurons that produce a robust and correct …ANNs are inspired by natural neural networks so they are systems able to process information (processor programmed to produce robust and correct output) and simulate the behavior of the brains mechanism... the quality of welded joints, evaluated in terms of UTS and micro hardness, was directly connected to the thermal parameters with the use of the ANN, which is the focus of this work. The model tracked with the use of the neural networks allows predicting quantitatively the mechanical behavior of the FSW joints, as shown in Figure 2.”) See also DE FLIPPAS ([Figure 2].)
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DE FLIPPAS Figure 2 Reference
DE FLIPPAS teaches based on a machine learning method DE FLIPPAS ([Introduction | pdf page 3 of 8] “A model based on the adoption of one or more Artificial Neural Networks (ANNs) can help to identify the relation between process parameters and quality of weld (based on machine learning method). In particular, according to Facchini et al. [15], one of the main advantages of this technique is that it can produce good results, even when supplied data are noisy or incomplete. In these cases, ANN can predict the output parameters after learning from a training data set (based on machine learning method), where the learning algorithm determines the numeric weights to the link among neurons that produce a robust and correct output. Recently it is spreading the use of ANN to model various problems in many fields such as materials science and the engineering [16–22]. ANNs are inspired by natural neural networks so they are systems able to process information and simulate the behavior of the brains mechanism (based on machine learning method).”) See also DE FLIPPAS ([Section 3.1 Design and Training ANNs | pdf page 9 of 18] “The simulation model was developed in order to establish a relationship between the mechanical properties of the joints and the technical parameters of the FSW process. Two different ANNs (were adopted; the first network (ANNHV) was used for identify the Vickers micro hardness of HAZ on the bases of five different input parameters (machine learning method) (n, v, p, MSHCRS, and MSHCAS); the second network (ANNUTS) considers the Ultimate Tensile Strength.”)
DE FLIPPAS does not explicitly teach receive temperature data that includes sensed or simulated temperature values each attributed to a corresponding one of a plurality of points of the weld at corresponding times during a welding process used to produce the weld or determine peak temperature values and cooling rate values for each of the points of the weld based on the temperature values.
However, RAHMAN also teaches receive temperature data that includes sensed or simulated temperature values each attributed to a corresponding one of a plurality of points of the weld at corresponding times during a welding process used to produce the weld RAHMAN, ([Section 3 Experimental Procedures] “This work focused on the effects of cooling rates on the transformation behavior and thereof, on the hardness distribution in the HAZ. During cooling, austenite is decomposed into ferrite, bainite, martensite, or a mixture of them, dependent on the cooling rate and the austenitizing temperature from which the cooling started. MAG (corresponding one of a plurality of points of the weld) and electron beam (EB) welding processes were used to weld 6-mm thick plates of steel S700MC applying different heat inputs (kJ/cm) (receive temperature data) which is a major parameter for the control of the cooling rate represented by t8/5 time (corresponding times during a welding process used to produce the weld). In addition, preheating, inter-pass temperature, and sheet thickness are also important factors for controlling the cooling rate (determining cooling rates) in the HAZ [2]. Generally, a higher heat input leads to a wider HAZ and lower cooling rate due to lower temperature gradient from the fusion line to the base material. Maurer [2] estimated the cooling rates (determining cooling rates) according to an analytical equation proposed by Rosenthal [17]. Those data (received temperature data) were used for the validation of the thermal simulation performed in this work (includes sensed or simulated temperature values). The welding processes, heat input (receive temperature data), and corresponding calculated t8/5 times (corresponding times during a welding process used to produce the weld) are shown in Table 2.”) See also RAHMAN ([Table 2].)
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RAHMAN Table 2 Reference
See also RAHMAN ([Section 4.1 Thermal Calculation] “An accurate thermal analysis considering proper thermal boundary condition including traveling heat source, heat transfer in the material due to conduction, convection, and radiation heat losses from the material are very important for determination of the realistic temperature profile [1, 18]. The heat input (corresponding one of a plurality of points of the weld) into the work piece in the case of MAG welding (weld) was modeled by a volumetric double ellipsoidal heat source model proposed by Goldak [19], whereas a conical heat source was used for the simulation of electron beam welding process. The geometrical descriptions of the affected volumes are shown in Fig. 2 and the energy distributions are shown in Eqs. 1 and 2, respectively. The model parameters were estimated, and the simulated cooling rates (receive temperature data that includes sensed or simulated temperature values) were compared with the results presented by Maurer [2]. See also RAHMAN ([Section 4.2 Grain Growth Modeling] “An isothermal grain growth model was developed considering Zener pinning effects [20] of the precipitates or the so called second-phase particles present in the materials. The model was then explicitly discretized and approximated using finite difference method, where the total time (t) was divided into small time steps (Δt) (corresponding times during a welding process used to produce the weld) as shown in Eq. 3. In this work, the nonisothermal grain growth model [21] was considered and the effects of austenite grain size on austenite decomposition during cooling were incorporated.”) See also RAHMAN ([Equation 3].)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of RAHMAN with DE FLIPPAS, as the references deal with predicting microhardness properties of a weld that defines a weld joint. RAHMAN would modify DE FLIPPAS wherein receive temperature data that includes sensed or simulated temperature values each attributed to a corresponding one of a plurality of points of the weld at corresponding times during a welding process used to produce the weld. The benefits of doing allows for the determination of hardness in the HAZ of Ti-Nb micro-alloyed steel. (RAHMAN [Introduction]).
The combination of DE FLIPPAS and RAHMAN does not explicitly teach determine peak temperature values and cooling rate values for each of the points of the weld based on the temperature values.
However, XAVIER teaches determine peak temperature values and cooling rate values for each of the points of the weld based on the temperature values XAVIER ([Introduction] “Thus, this study deals with the issue of kinetics transformation under non-isothermal conditions (Non-isothermal transformation kinetics often involve determining peak temperature values, particularly when using techniques like Differential Scanning Calorimetry (DSC) to analyze crystallization or phase changes), which in turn, is a subject of great practical interest. An attempt to establish a correlation aiming at the use of data obtained from the isothermal transformations in order to calculate non-isothermal transformations (The peak temperature, representing the maximum reaction rate, is used in conjunction with heating rates to determine kinetic parameters (e.g., activation energy) using methods such as the Kissinger Equation) was initially presented by Avrami14, through the definition of an isokinetic reaction by the condition that the nucleation and growth rates are proportional to each other, i.e., they have same temperature variation.”) See also XAVIER ([Section 3.1 Thermal Features] “The numerical model for thermal analysis during welding, based in the mentioned FVM computational code, has been previously validated for the temperature field predictions and previously published17-19 and new features incorporated in this study. In order to demonstrate the accuracy of the prediction model, the Figures 5 (a) and (b) present a comparison between the calculated and measured values for temperature and welding zones in the plate. The temperature acquisition using thermocouples shows good agreement with the calculated results for a steel whose thermophysical and metallurgical features are quite representative of the steel investigated in this study, as shown in Figure 5 (a). As can be observed in Figure 5 (a), close agreement for the measured and calculated thermal history was obtained, allowing the applicability of the model for temperature predictions and welding zone (see Figure 5 (b)) and, accordingly, in providing of reliable data for the calculations of phase volume fractions.”) See also XAVIER ([Figure 5a].)
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XAVIER Figure 5(a) Reference
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of XAVIER with DE FLIPPAS and RAHMAN, as the references deal with predicting microhardness properties of a weld that defines a weld joint. XAVIER would modify DE FLIPPAS and RAHMAN wherein determine peak temperature values and cooling rate values for each of the points of the weld based on the temperature values. The benefits of doing represents a step forward on the challenging task of numerically predicts the complex phenomena and changes taking place during the steels welding and could be used to evaluate new welding procedures for the industrial practice. (XAVIER [Introduction]).
The combination of DE FLIPPAS, RAHMAN, and XAVIER does not explicitly teach predict a three-dimensional (3D) distribution of microhardness values of the weld…that evaluates the peak temperature values and the cooling rate values or generate display data based on the 3D distribution of microhardness values.
However, YU teaches predict a three-dimensional (3D) distribution of microhardness values of the weld…that evaluates the peak temperature values and the cooling rate values YU ([Figure 16] and [5.2 Prediction subsystem of hardness for laser temper bead welding] “Figure 16 presents an example of the hardness prediction (predict) subsystem for 1-cycle+temper. There are three variable parameters: Tp1 and CR1 of the first thermal cycle, and TCTP of the following temper cycles with peak temperature (evaluates the peak temperature values) lower than Ac1. Together with the output hardness, it is a four dimensional space (at least three-dimensional). To enable visualization of the results, one of these parameters must be fixed. Figure 16a, b shows the 3D and 2D-contour relationship figure between hardness and CR1/TCTP, when Tp1 of the first thermal cycle is fixed at 1350 °C. Here, all the 2D-contour figures use the same color hardness scale. It can be found that with increasing TCTP, the hardness decreases significantly.”) See also YU ([Introduction] “Using the neural network based hardness prediction system, the hardness distribution (distribution of the microhardness values) in HAZ of laser temper bead welding (of the weld) was calculated based on the thermal cycles numerically obtained by FEM. The proposed hardness prediction system has been verified with comparing the predicted hardness with the measured one in HAZ when laser temper bead welding is applied. Through this method, the appropriate welding conditions can be selected before the actual repair welding.”) See also YU ([Section 3 Measurement of cooling rate in HAZ of laser temper bead welding] “Through the magnified ranges of the cooling process, the cooling rates in laser welding are measured as 398, 229, and 172 °C/s, respectively. The cooling rate of the laser temper bead welding is much higher than that of typical TIG temper bead welding, which are mainly lower than 100 °C/s. This fact means that the hardness database with cooling rate higher than 100 °C/s are required for hardness prediction (evaluation of cooling rate) in HAZ of laser temper bead welding.”)
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YU Figure 16 Reference
YU also teaches generate display data based on the 3D distribution of microhardness values YU ([Section 5.2 Prediction Subsystem of Hardness for Laser Temper Bead Welding] “Based on plentiful experimental results, the hardness prediction subsystem for these four types of thermal cycles has been constructed, as illustrated in Figs. 15, 16, 17, and 18. Figure 15a, b respectively represents the calculated 3D and 2D-contour figures of the complex relationship between hardness and Tp/CR for 1-cycle. See also YU ([Figure 16].)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of YU with DE FLIPPAS, RAHMAN, and XAVIER as the references deal with microhardness properties of a weld that defines a weld joint. YU would modify DE FLIPPAS and RAHAM wherein generate display data based on the 3D distribution of microhardness values. The benefits of doing so effective for estimating the tempering effect during laser temper bead welding. (YU [Abstract]). Accordingly, claim 1 is rejected based on the combination of these references.
Claim 11
Claim 11 is rejected because it is the method embodiment of claim 1, with similar limitations to claim 1, and is such rejected using the same reasoning found in claim 1.
Claim 3
Claim 3 is rejected because the combination of DE FLIPPAS, RAHMAN, XAIVER, and YU teaches the claim 1 limitations.
DE FLIPPAS does not explicitly teach simulate the temperature values using a welding process simulation model.
However, RAHMAN teaches simulate the temperature values using a welding process simulation model RAHMAN ([Abstract] “This study describes a method for the determination of hardness based on Vickers micro-hardness of the phase constituents in the heat-affected zone (HAZ) of single-layered metal active gas (MAG) and electron beam (EB)-welded components. The finite element (FE) simulation was performed incorporating of austenite grain size (AGS)-dependent γ–α decomposition during cooling from an austenitizing temperature in the HAZ. In this simulation, a 2D symmetric thermo-metallurgical model was conducted using SYSWELD software version 2013. Two welding continuous cooling transformation (CCT) diagrams were incorporated which were experimentally determined for 1000 and 1300 °C peak temperatures using dilatometry.”)
RAHMAN also teaches generate the temperature data comprising the simulated temperature values RAHMAN ([Abstract] “The finite element (FE) simulation was performed incorporating of austenite grain size (AGS)-dependent γ–α decomposition during cooling from an austenitizing temperature in the HAZ. In this simulation, a 2D symmetric thermo-metallurgical model was conducted using SYSWELD software version 2013. Two welding continuous cooling transformation (CCT) diagrams were incorporated which were experimentally determined for 1000 and 1300 °C peak temperatures using dilatometry. The implementation of CCT diagrams was performed as the function of the calculated austenite grain sizes (AGS). It was found that the phase constituents themselves had the most significant contribution to the hardness.”
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of RAHMAN with DE FLIPPAS, as the references deal with predicting microhardness properties of a weld that defines a weld joint. RAHMAN would modify DE FLIPPAS wherein generate the temperature data comprising the simulated temperature values. The benefits of doing allows for the determination of hardness in the HAZ of Ti-Nb micro-alloyed steel. (RAHMAN [Introduction]).
Claim 13
Claim 13 is rejected because it is the method embodiment of claim 3, with similar limitations to claim 3, and is such rejected using the same reasoning found in claim 3
Claim 6
Claim 6 is rejected because the combination of DE FLIPPAS, RAHMAN, XAVIER, and YU teach the claim 1 limitations.
The combination of DE FLIPPAS, RAHMAN, and XAVIER does not explicitly teach provide the 3D distribution of microhardness values to a computer-aided engineering (CAE) tool as input data, or perform an analysis with the CAE tool using the 3D distribution of microhardness values as input.
However, YU teaches provide the 3D distribution of microhardness values to a computer-aided engineering (CAE) tool as input data YU ([Figure 16] and [5.2 Prediction subsystem of hardness for laser temper bead welding] “Figure 16 presents an example of the hardness prediction (predict) subsystem for 1-cycle+temper. There are three variable parameters: Tp1 and CR1 of the first thermal cycle, and TCTP of the following temper cycles with peak temperature (evaluates the peak temperature values) lower than Ac1. Together with the output hardness, it is a four dimensional space (at least three-dimensional). To enable visualization of the results, one of these parameters must be fixed. Figure 16a, b shows the 3D and 2D-contour relationship figure between hardness and CR1/TCTP, when Tp1 of the first thermal cycle is fixed at 1350 °C. Here, all the 2D-contour figures use the same color hardness scale. It can be found that with increasing TCTP, the hardness decreases significantly.”) See also YU ([Introduction] “Using the neural network based hardness prediction system, the hardness distribution (distribution of the microhardness values) in HAZ of laser temper bead welding (of the weld) was calculated based on the thermal cycles numerically obtained by FEM. The proposed hardness prediction system has been verified with comparing the predicted hardness with the measured one in HAZ when laser temper bead welding is applied. Through this method, the appropriate welding conditions can be selected before the actual repair welding.”) See also YU ([Section 3 Measurement of cooling rate in HAZ of laser temper bead welding] “Through the magnified ranges of the cooling process, the cooling rates in laser welding are measured as 398, 229, and 172 °C/s, respectively. The cooling rate of the laser temper bead welding is much higher than that of typical TIG temper bead welding, which are mainly lower than 100 °C/s. This fact means that the hardness database with cooling rate higher than 100 °C/s are required for hardness prediction (evaluation of cooling rate) in HAZ of laser temper bead welding.”)
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YU Figure 16 Reference
YU also teaches perform an analysis with the CAE tool using the 3D distribution of microhardness values as input YU ([Section 4 Experimentally measured hardness database for NN-based prediction of laser temper bead welding] “The hardness and the microstructure in HAZ of laser temper bead welding are determined by the thermal cycles in HAZ; therefore, the multi-thermal cycles in HAZ has been subjected to the following detailed analysis. For one-layer welding, there are two kinds of thermal cycles occurred in HAZ of multi-pass welding: (a) 1-cycle, which is affected by single-pass welding and (b) 2-cycle, in the middle overlapped region of double pass welding. In the temper bead welding produced by consistent layer technique, only tempering thermal cycles are applied from the second layer welding [10]; therefore, the possible thermal cycles after multi-layer welding are classified as 1-cycle+temper and 2 cycle+temper. See also YU ([Figure 15] and [Figure 16].)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of YU with DE FLIPPAS, RAHMAN, and XAVIER as the references deal with microhardness properties of a weld that defines a weld joint. YU would modify DE FLIPPAS, RAHMAN, and XAVIER wherein generate display data based on the 3D distribution of microhardness values. The benefits of doing so effective for estimating the tempering effect during laser temper bead welding. (YU [Abstract]). Accordingly, claim 6 is rejected based on the combination of these references.
Claim 16
Claim 16 is rejected because it is the method embodiment of claim 6, with similar limitations to claim 6, and is such rejected using the same reasoning found in claim 6.
Claims 2, 7, 8, 9, 10, 12, 17, 18, 19 and 20 is rejected under are rejected under 35 U.S.C. 103 as being unpatentable over DE FLIPPAS, in view of RAHMAN, in view of XAVIER, in view of YU, in further view of SHRIKRISHNA (The impact of heat input on the strength, toughness, microhardness, microstructure and corrosion aspects of friction welded duplex stainless steel joints), herein SHRIKRISHNA, and in further view of OH (Deep learning model for predicting hardness distribution in laser heat treatment of AISI H13 tool steel), herein OH.
Claim 2
Claim 2 is rejected because the combination of DE FLIPPAS, RAHMAN, and YU teaches the claim 1 limitations.
The combination of DE FLIPPAS, RAHMAN, XAVIER and YU does not teach receive composition data that includes compositions of the at least two workpieces or receive material microhardness data that includes microhardness values of base metals of the at least two workpiece.
However, SHRIKRISHNA teaches receive composition data that includes compositions of the at least two workpieces SHRIKRISHNA ([Section 2.2 Friction Welding | pdf 3 of 15] “A continuous drive type friction welding KUKA (Maximum of 20 kW and 150 kN upset load) machine was engaged in atomic bonding. It is known very well that heating pressure (HP), upsetting pressure (UP), heating time (HT), upsetting time (UT), are the main input parameters that are utilized en route for the generation of heat related to welding thermal cycles, which can be varied between the series of the machine settings. Spindle speed of 1500 rpm, brake delay time of 0.8 s, upset delay time of 0.3 s and feed rate of 1 mm/s are kept constant. The experimental setup of continuous drive friction welding is shown in Fig. 2.”) See also SHRIKRISHNA ([Figure 2], [Table 3], and [Table 8].)
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SHRIKRISHNA Figure 2 and Table 3 References
SHRIKRISHNA also teaches receive material microhardness data that includes microhardness values of base metals of the at least two workpiece SHRIKRISHNA ([Section 3.3 Total Heat Input and Microhardness] “From Table 8, it is clear that the PDZ had the lowest hardness than weld and the weld was next to it when compared to base material. This was due to the strain hardening effect during the friction process at the interface.”) See also SHRIKRISHNA ([Table 8].)
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SHRIKRISHNA Table 8 Reference
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of SHRIKRISHNA with DE FLIPPAS, RAHMAN, XAVIER and YU as the references deal with predicting microhardness properties of a weld that defines a weld joint. SHRIKRISHNA would modify DE FLIPPAS, RAHMAN, and YU wherein receive material microhardness data that includes microhardness values of base metals of the at least two workpiece. The benefits of doing provides increased with an increase in heat input due to grain refinement in the microhardness process. (SHRIKRISHNA [Introduction]).
The combination of DE FLIPPAS, RAHMAN, XAVIER and YU, and SHRIKRISHNA does not explicitly teach predict the 3D distribution of microhardness values of the weld based on the machine learning method that evaluates the peak temperature values, the cooling rate values, the compositions of the at least two workpieces, and the microhardness values of the base metals.
However, OH teaches predict the 3D distribution of microhardness values of the weld based on the machine learning method that evaluates the peak temperature values, the cooling rate values, the compositions of the at least two workpieces, and the microhardness values of the base metals OH ([Highlights | pdf page 1 of 13] “A deep learning model for laser heat treatment of H13 tool steel was presented. • Hardness distributions were predicted from temperature distributions. • Temperature distributions were calculated from 3-D thermal simulations. • CNN and cGAN were adopted for constructing the predictive model. • Average prediction accuracy was 94.4%.”) See also OH ([Introduction] “With recent advances in artificial intelligence (AI), a new area of machine learning called deep learning has presented an innovative way of solving various engineering problems. Unlike physics-based conventional methods, this data-driven modeling method can make predictions by learning from the data obtained from experiments or simulations.”) See also OH ([Figure 2 (a) and (b)].)
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OH Figure 2(a) and (b) Reference
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of OH with DE FLIPPAS, RAHMAN, XAVIER, YU, and SHRIKRISHNA as the references deal with predicting microhardness properties of a weld that defines a weld joint. OH would modify DE FLIPPAS, RAHMAN, XAVIER, YU, and SHRIKRISHNA wherein predict the 3D distribution of microhardness values of the weld based on the machine learning method that evaluates the peak temperature values, the cooling rate values, the compositions of the at least two workpieces, and the microhardness values of the base metals. The benefits of doing provides a proposed a method for analyzing the laser hardening process by constructing process maps for effective carbon diffusion time and effective cooling time. (OH [Introduction]). Accordingly, claim 2 is rejected based on the combination of these references.
Claim 12
Claim 12 is rejected because it is the method embodiment of claim 2, with similar limitations to claim 2, and is such rejected using the same reasoning found in claim 2.
Claim 7
Claim 7 is rejected because the combination of DE FLIPPAS, RAHMAN, and YU teaches the claim 1 limitations.
DE FLIPPAS does not explicitly teach a volume of fraction martensite.
However, RAHMAN teaches a volume of fraction martensite RAHMAN ([Introduction] “Because of their lean chemistry, in particular, the low carbon content, controlled welding parameters are required in order to achieve adequate strength in the weld heat-affected zone (HAZ). For conventional low-carbon steels and some TMCP pipeline steels such as X70 and X80, HAZ softening is often the result of high heat input in the welding [3]. This reduction in hardness may weaken the welded structure if the ratio of the soft zone (the ratio of the volume of martensite phase to the total volume of the material) to the sheet thickness (X-ratio) reaches the limiting value [2]. Therefore, it is important to know the degree and extension to which this softening takes place in a certain welding process. In steels, austenite grain size (AGS) is a microstructural parameter that highly influences austenite decompositions during cooling. The increase in AGS shifts the continuous cooling transformation (CCT) diagram to longer reaction times, thereby increasing the possibility of martensite formation (a volume of fraction martensite).”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of RAHMAN with DE FLIPPAS, as the references deal with predicting microhardness properties of a weld that defines a weld joint. RAHMAN would modify DE FLIPPAS wherein a volume of fraction martensite. The benefits of doing allows for the determination of hardness in the HAZ of Ti-Nb micro-alloyed steel. (RAHMAN [Introduction]).
The combination of DE FLIPPAS, RAHMAN, and XAVIER does not explicitly teach the processor is programmed to predict the 3D distribution of microhardness values without using martensite tempering kinetics produced experimentally for the tempered zone or wherein a heat affected zone of the weld includes a tempered zone, at least some of the microhardness values of the 3D distribution are attributed to points within the tempered zone.
However, YU teaches the processor is programmed to predict the 3D distribution of microhardness values without using martensite tempering kinetics produced experimentally for the tempered zone YU ([6.2 Temperature analysis in HAZ of laser temper bead welding by FEM] “The temperature distributions produced by multi-pass thermal cycles during laser temper bead welding were calculated using three-dimensional finite element analysis code, developed by the authors specifically for welding simulation. The mesh model is the same size with the experimental welding sample, as presented in Fig. 20. And the welding conditions were the same as the experimental conditions shown in Table 3, which followed the consistent layer technique. Figure 21 presents the calculated peak temperature distribution in the middle section after six layer-30 pass welding. Different peak temperatures are presented in different colors.”) See also YU ([Section 6.3 Hardness prediction in HAZ based on the FEM simulated thermal cycle] “Based on the calculated thermal history of every grid node, the hardness at the grid node was calculated, by feeding the thermal cycle parameters into the proposed NN-based hardness prediction subsystem. On the basis of the predicted hardness at every grid node, the hardness distribution in the HAZ is shown as color chart maps in Fig. 22. Figure 22a illustrates the hardness distribution in HAZ of one layer-5 pass welding.”) See also YU ([Table 2] where YU used thermal cycle parameters for 4 types of simulated thermal cycle tests to determine softening rather than creating new experimental data for that specific zone.
YU also teaches wherein a heat affected zone of the weld includes a tempered zone, at least some of the microhardness values of the 3D distribution are attributed to points within the tempered zone YU ([Introduction] “Therefore, in the present study, the hardness prediction system for laser temper bead welding has been constructed using a neural network based on experimentally obtained hardness database. Using the neural network-based hardness prediction system, the hardness distribution in HAZ (distribution are attributed to points within the tempered zone) of laser temper bead welding was calculated based on the thermal cycles numerically obtained by FEM. The proposed hardness prediction system has been verified with comparing the predicted hardness with the measured one in HAZ when laser temper bead welding (heat affected zone of the weld includes a tempered zone) is applied. Through this method, the appropriate welding conditions can be selected before the actual repair welding.”) See YU ([Section 2 Materials and Experimental Procedures] “And the thermal cycle parameters were controlled directly by the system program. Both the hardness measurement (microhardness values of the 3D) and the microstructure observation were performed in the cross section of the heated samples.”) See also YU ([Introduction] “Using the neural network based hardness prediction system, the hardness distribution (distribution of the microhardness values) in HAZ of laser temper bead welding (of the weld) was calculated based on the thermal cycles numerically obtained by FEM. The proposed hardness prediction system has been verified with comparing the predicted hardness with the measured one in HAZ when laser temper bead welding is applied. Through this method, the appropriate welding conditions can be selected before the actual repair welding.”) See also YU ([6.1 Hardness prediction system for temper bead welding] “according to the calculated hardness of every grid node, the visual hardness distribution in HAZ of temper bead welding can be shown as color charts using Mathematica software. Finally, the predicted hardness is compared with the experimentally measured one to verify the effectiveness of the hardness prediction system.”) See also YU ([Section 3 Measurement of cooling rate in HAZ of laser temper bead welding] “Through the magnified ranges of the cooling process, the cooling rates in laser welding are measured as 398, 229, and 172 °C/s, respectively. The cooling rate of the laser temper bead welding is much higher than that of typical TIG temper bead welding, which are mainly lower than 100 °C/s. This fact means that the hardness database with cooling rate higher than 100 °C/s are required for hardness prediction in HAZ of laser temper bead welding.”) See also YU ([Figure 15] and [Figure 16].)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of YU with DE FLIPPAS, RAHMAN, and XAVIER, as the references deal with microhardness properties of a weld that defines a weld joint. YU would modify DE FLIPPAS and RAHAM wherein a heat affected zone of the weld includes a tempered zone, at least some of the microhardness values of the 3D distribution are attributed to points within the tempered zone. The benefits of doing so effective for estimating the tempering effect during laser temper bead welding. (YU [Abstract]). The combination of DE FLIPPAS, RAHMAN, and YU does not explicitly teach wherein the at least two workpieces are formed of an advanced high strength steel that includes a volume fraction of martensite.
However, SHRIKRISHNA teaches wherein the at least two workpieces are formed of an advanced high strength steel SHRIKRISHNA ([Section 2.2 Friction Welding | pdf 3 of 15] “To produce high quality (advanced high strength steel) joints (at least two workpieces are formed), range of process parameters were chosen based on quantifying the weld samples to free of weld defects and with good mechanical properties (advanced high strength steel). The soundness of weld joints by evaluating the sub surfaces for discontinuities was identified through radiograph interpretation – a non destructive testing method through ASTM E1032 standard. All possible combinations for a given set of factors can be identified from a full factorial design. Most industrial experiments usually involve a significant number of factors. A full factorial design results in a large number of experiments. To minimize the number of experiments to a practical level, Taguchi visualized a new method of conducting the design of experiments (DOE). This method uses a special set of arrays called orthogonal arrays. These standard arrays stipulate the way of conducting the minimal number of experiments. These experiments give the full information of all the factors that affect the performance parameter. Thus this method saves time and resources. Hence, Friction welding was carried out by formation of L9 orthogonal array. Table 3 gives the welding parameters and their work range..”) See also SHRIKRISHNA ([Figure 2], [Table 3], and [Table 8].)
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SHRIKRISHNA Figure 2 and Table 3 References
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of SHRIKRISHNA with DE FLIPPAS, RAHMAN, and XAVIER, and YU as the references deal with predicting microhardness properties of a weld that defines a weld joint. SHRIKRISHNA would modify DE FLIPPAS, RAHMAN, XAVIER, and YU wherein the at least two workpieces are formed of an advanced high strength steel. The benefits of doing provides increased with an increase in heat input due to grain refinement in the microhardness process. (SHRIKRISHNA [Introduction]). Accordingly, claim 7 is rejected based on the combination of these references.
Claim 17
Claim 17 is rejected because it is the method embodiment of claim 7, with similar limitations to claim 7, and is such rejected using the same reasoning found in claim 7.
Claim 8
Claim 8 is rejected because the combination of DE FLIPPAS, RAHMAN, XAVIER, and YU teaches the claim 1 limitations.
DE FLIPPAS does not explicitly teach receive two-dimensional (2D) distribution data that comprises a two-dimensional (2D) distribution of microhardness values each attributed to a corresponding one of the plurality of points of the weld
However, RAHMAN teaches receive two-dimensional (2D) distribution data that comprises a two-dimensional (2D) distribution of microhardness values each attributed to a corresponding one of the plurality of points of the weld RAHMAN ([Section 6 Discussion and Conclusion] “In this study, a method is proposed for the determination of hardness in the HAZ of Ti-Nb micro-alloyed steel. The hardness was calculated based on Vickers microhardness of the phase constituents and the predicted microstructure in the heat-affected zone. The Vickers microhardness HV0.01 was experimentally measured (), and the phase fraction was calculated by conducting a 2D symmetric thermo-metallurgical FE model (receive two-dimensional (2D) distribution data).”) See also RAHMAN ([Section 4 Numerical Simulation] “A 2D symmetric thermo-metallurgical model was conducted in SYWELD software version 2013 employing 4345 four noded elements having 4575 nodes. The element size of about 0.2 mm×0.2 mm was used. The geometry and mesh are presented in the Fig. 1. The simulation was performed considering temperature- and phase-dependent thermos-metallurgical material properties. The grain growth modeling with the consideration of precipitation kinetics and AGS-based solid-state phase transformation were incorporated in this simulation.”) See also RAHMAN ([Introduction] “Yu et al. [14] predicted the hardness distribution (two-dimensional (2D) distribution of microhardness values) in the multi-pass bead on plate-welded region with the help of neural network which is based on experimentally obtained hardness data.”) See also RAHMAN ([Table 4].)
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RAHMAN Table 4 Reference
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of RAHMAN with DE FLIPPAS, as the references deal with predicting microhardness properties of a weld that defines a weld joint. RAHMAN would modify DE FLIPPAS wherein receive two-dimensional (2D) distribution data that comprises a two-dimensional (2D) distribution of microhardness values each attributed to a corresponding one of the plurality of points of the weld. The benefits of doing allows for the determination of hardness in the HAZ of Ti-Nb micro-alloyed steel. (RAHMAN [Introduction]).
The combination of DE FLIPPAS, RAHMAN, XAVIER, YU, and SHRIKRISHNA does not explicitly teach correlate, by the machine learning method, the 2D distribution of microhardness values with the peak temperature values and the cooling rate values of the weld to provide correlation results.
However, OH teaches correlate, by the machine learning method, the 2D distribution of microhardness values with the peak temperature values and the cooling rate values of the weld to provide correlation results OH ([Introduction | pdf page 2 of 13] “So [8] proposed a method for analyzing the laser hardening process by constructing process maps for effective carbon diffusion time and effective cooling time (distribution of microhardness values with the cooling rate values of the weld). Using the process map approach, So and Ki [9] studied how the laser hardening process changes as the specimen thickness is varied, and Ki et al. [10,11] investigated the laser hardening of steel sheets (distribution of microhardness values) by employing a heat sink, where they studied the effects of thermal contact resistance and heat sink thermal conductivity on the hardenability. Although the process map approach was useful in understanding the hardening process on a large process window, due to the 1-D model employed, the prediction accuracy was found to deteriorate at high interaction times. Recently, Oh and Ki [1] improved the accuracy by employing a 3-D thermal simulation (at least 2D distribution) and proposed predictive models for hardness distributions and deflection angles. As presented, the majority of the previous models are physics-based. With recent advances in artificial intelligence (AI), a new area of machine learning called deep learning has presented an innovative way of solving various engineering problems. Unlike physics-based conventional methods, this data-driven modeling method can make predictions (provide correlation results) by learning from the data obtained from experiments or simulations. Once training is finished, predictions (correlations) can be made instantly.”)
OH also teaches train a neural network with the correlation results to predict the 3D distribution of microhardness values of the weld OH ([Figure 1] teaches that both computational and experimental data results were used from the training and testing of the deep neural network learning model to correlate results to predict 3D distributions of microhardness measurement values of the weld.
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OH Figure 1 Reference
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of OH with DE FLIPPAS, RAHMAN, XAVIER, YU, and SHRIKRISHNA as the references deal with predicting microhardness properties of a weld that defines a weld joint. OH would modify DE FLIPPAS, RAHMAN, XAVIER, YU, and SHRIKRISHNA wherein train a neural network with the correlation results to predict the 3D distribution of microhardness values of the weld. The benefits of doing provides a proposed a method for analyzing the laser hardening process by constructing process maps for effective carbon diffusion time and effective cooling time. (OH [Introduction]). Accordingly, claim 8 is rejected based on the combination of these references.
Claim 18
Claim 18 is rejected because it is the method embodiment of claim 8, with similar limitations to claim 8, and is such rejected using the same reasoning found in claim 8.
Claim 9
Claim 9 is rejected because it is the system embodiment of claims 2 and 8.
The combination of DE FLIPPAS, RAHMAN, XAVIER, YU, and SHRIKRISHNA does not explicitly teach the compositions, and the microhardness values to provide correlation results.
However, OH also teaches the compositions, and the microhardness values to provide correlation results OH ([Introduction | pdf page 2 of 13] “So [8] proposed a method for analyzing the laser hardening process by constructing process maps for effective carbon diffusion time and effective cooling time (distribution of microhardness values). Using the process map approach, So and Ki [9] studied how the laser hardening process changes as the specimen thickness is varied, and Ki et al. [10,11] investigated the laser hardening of steel sheets (distribution of microhardness values) by employing a heat sink, where they studied the effects of thermal contact resistance and heat sink thermal conductivity on the hardenability. Although the process map approach was useful in understanding the hardening process on a large process window, due to the 1-D model employed, the prediction accuracy was found to deteriorate at high interaction times. Recently, Oh and Ki [1] improved the accuracy by employing a 3-D thermal simulation (at least 2D distribution) and proposed predictive models for hardness distributions and deflection angles. As presented, the majority of the previous models are physics-based. With recent advances in artificial intelligence (AI), a new area of machine learning called deep learning has presented an innovative way of solving various engineering problems. Unlike physics-based conventional methods, this data-driven modeling method can make predictions (provide correlation results) by learning from the data obtained from experiments or simulations. Once training is finished, predictions (correlations) can be made instantly.”) See also OH ([Table 2] and [Section 2.2 For Ground Truths: Experiments] “To ensure consistency with the simulation, the focused beam size had the dimensions 3.7mm (lx)×4.4mm (ly), and 3.7mm was the beam size along the beam scanning direction. AISI H13 tool steel, with the chemical composition given in Table 2, was used as the specimen material, and was machined into 30mm×50mm×6mm specimens (the same dimensions used for simulations).”)
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OH Table 2 Reference
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of OH with DE FLIPPAS, RAHMAN, XAVIER, YU, and SHRIKRISHNA as the references deal with predicting microhardness properties of a weld that defines a weld joint. OH would modify DE FLIPPAS, RAHMAN, XAVIER, YU, and SHRIKRISHNA wherein teaches the compositions, and the microhardness values to provide correlation results. The benefits of doing provides a proposed a method for analyzing the laser hardening process by constructing process maps for effective carbon diffusion time and effective cooling time. (OH [Introduction]). Accordingly, claim 9 is rejected based on the combination of these references.
Claim 19
Claim 19 is rejected because it is the method embodiment of claim 9, with similar limitations to claim 9, and is such rejected using the same reasoning found in claim 9.
Claim 10
Claim 10 is rejected because the combination of DE FLIPPAS, RAHMAN, XAVIER, YU, and SHRIKRISHNA and OH teaches the claim 9 limitations.
DE FLIPPAS does not explicitly teach measuring the 2D distribution of microhardness values of the weld.
However, RAHMAN teaches measuring the 2D distribution of microhardness values of the weld ([4.4 Hardness calculation | pdf page 4 of 8] “The martensite transformation was modeled using Köstinen-Marburger relationship [23] expressed in Eq. 5. where, HV is the total hardness, H represents the microhardness, and X is the fraction of phase. The subscriptions F, FB, B, and M stand for ferrite, ferritic bainite, bainite, and martensite, respectively. The micro-hardness of different phases, shown in the Table 4, was determined experimentally, and the phase fraction was simulated using the methods described above. The simulated hardness in the HAZ was first verified with experimentally measured hardness (measuring of the 2D distribution of microhardness values of the weld) of the physically simulated HAZ (dilatometer samples), and the model was then applied to simulate MAG and electron beam welding process using different heat inputs.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of RAHMAN with DE FLIPPAS, as the references deal with predicting microhardness properties of a weld that defines a weld joint. RAHMAN would modify DE FLIPPAS wherein measuring the 2D distribution of microhardness values of the weld. The benefits of doing allows for the determination of hardness in the HAZ of Ti-Nb micro-alloyed steel. (RAHMAN [Introduction]).
The combination of DE FLIPPAS, RAHMAN, and XAVIER does not explicitly teach transmitting the 2D distribution of microhardness values to the processor as the 2D distribution data.
However, YU teaches transmitting the 2D distribution of microhardness values to the processor as the 2D distribution data YU ([ Section 5.2 Prediction subsystem of hardness for laser temper bead welding| pdf page 11 of 16] “Based on plentiful experimental results, the hardness prediction subsystem for these four types of thermal cycles has been constructed, as illustrated in Figs. 15, 16, 17, and 18. Figure 15a, b respectively represents the calculated 3D and 2D-contour figures of the complex relationship between hardness and Tp/CR for 1-cycle. In the 2D-contour figure of Fig. 15b, different hardness ranges are shown in different colors, and the star marks indicate the highest hardness. With this prediction subsystem, the hardness of A533B steel subjected to any single thermal cycle can be calculated if the Tp and CR of the thermal cycle process are known. It means that the hardness in HAZ of single-pass welding for A533B steel can be calculated with the hardness prediction subsystem of 1-cycle. Figure 16 presents an example of the hardness prediction subsystem for 1-cycle+temper. There are three variable parameters: Tp1 and CR1 of the first thermal cycle, and TCTP of the following temper cycles with peak temperature lower than Ac1. Together with the output hardness, it is a four dimensional space. To enable visualization of the results, one of these parameters must be fixed. Figure 16a, b shows the 3D and 2D-contour relationship figure between hardness and CR1/TCTP, when Tp1 of the first thermal cycle is fixed at 1350 °C. Here, all the 2D-contour figures use the same color hardness scale. It can be found that with increasing TCTP, the hardness decreases significantly. See also YU ([Figure 15].)
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YU Figure 15 Reference
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of YU with DE FLIPPAS, RAHMAN, and XAVIER, as the references deal with microhardness properties of a weld that defines a weld joint. YU would modify DE FLIPPAS, RAHMAN, and XAVIER transmitting the 2D distribution of microhardness values to the processor as the 2D distribution data. The benefits of doing so effective for estimating the tempering effect during laser temper bead welding. (YU [Abstract]). Accordingly, claim 10 is rejected based on the combination of these references.
Claim 20
Claim 20 is rejected because it is the method embodiment of claim 10, with similar limitations to claim 10, and is such rejected using the same reasoning found in claim 10.
Claims 4, 5, 14, and 15 are rejected under are rejected under 35 U.S.C. 103 as being unpatentable over DE FLIPPAS, in view of RAHMAN, in view of XAVIER, in view of YU, in view of SHRIKRISHNA, and in further view of YU2 (A low-cost infrared sensing system for monitoring the MIG welding process), herein YU2.
Claim 4
Claim 4 is rejected because the combination of DE FLIPPAS, RAHMAN, XAVIER, and YU teaches the claim 1 limitations.
The combination of DE FLIPPAS and RAHMAN does not explicitly teach wherein the processor determines the cooling rate values in a temperature range of between about 800°C and 500°C YU.
However, YU teaches wherein the processor determines the cooling rate values in a temperature range of between about 800°C and 500°C YU ([Table 2] and [Section 2 Materials and Experimental Procedures] “Figure 1 illustrates four types of thermal cycle patterns to simulate the thermal cycles in HAZ of temper bead welding with consistent layer technique [10], which indicated the 1- cycle, 2-cycle, 1-cycle+temper and 2-cycle+temper, respectively [11, 12]. The thermal cycle parameters are shown in Table 2. Here, Tpi indicates the peak temperature of the ith pass thermal cycle, and CRi means the cooling rate from 800 to 500 °C of the ith pass thermal cycle. Samples (5 × 5 × 5 mm) were heated by the high-frequency induction heating device as show in Fig. 2a.”)
The combination of DE FLIPPAS, RAHMAN, XAVIER, and YU does not explicitly teach wherein the at least two workpieces are formed of a mild steel.
However, SHRIKRISHNA teaches wherein the at least two workpieces are formed of a mild steel SHRIKRISHNA ([Section 2.2 Friction Welding] “To produce high quality joints (at least two workpieces are formed), range of process parameters were chosen based on quantifying the weld samples (mild steel) to free of weld defects and with good mechanical properties (mild steel).”) See also SHRIKRISHNA ([Figure 2], [Table 3], and [Table 8].)
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SHRIKRISHNA Figure 2 and Table 3 References
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of SHRIKRISHNA with DE FLIPPAS, RAHMAN, XAVIER, and YU as the references deal with predicting microhardness properties of a weld that defines a weld joint. SHRIKRISHNA would modify DE FLIPPAS, RAHMAN, XAVIER, and YU wherein the at least two workpieces are formed of a mild steel. The benefits of doing provides increased with an increase in heat input due to grain refinement in the microhardness process. (SHRIKRISHNA [Introduction]).
The combination of DE FLIPPAS, RAHMAN, XAVIER, and YU, and SHRIKRISHNA does not explicitly teach sense the temperature values of the welding process or transmit the sensed temperature values to the processor as the temperature data.
However, YU2 teaches sense the temperature values of the welding process OH ([Section 2 Experimental Setup | pdf page 3 of 8] “Figure 2 shows the welding experimental setup that is implemented. The infrared sensing system that is used to obtain the IR radiation from the surface of the plate being welded is made up of a galvanometer scanner and a point infrared sensor. The point infrared sensor is an Optris CTlaser 2MH which is adjusted to a temperature range between 385 and 1600 °C (685.15 to 1873.15 K) with an accuracy of ±1% over the entire range. The exposure time (90%) of the sensor is 1 ms. The software was designed to control the rotational speed and rotation direction of the galvanometer scanner. By controlling the scanning mirror of the galvanometer scanner rotating in a high speed, the infrared energy at different points of the plate being welded will be continually reflected to the point infrared sensor. This made it possible to obtain the temperature distribution of the welding seam and its heat affected zone by a point infrared sensor.
YU2 also teaches transmit the sensed temperature values to the processor as the temperature data YU2 ([Introduction] “Arc welding process as mentioned above is inherently a thermal-processing method; thus, infrared sensing is being considered as an effective method for monitoring the welding process. Every object with a temperature above the absolute zero (−273.15 °C = 0 K) emits an electromagnetic radiation from its surface, which is proportional to its intrinsic temperature. A part of this so-called intrinsic radiation is infrared radiation, which can be used to measure a body’s temperature. Previous investigations have shown that various disturbances to the welding process will change the heat flow and cause perturbations in the surface temperature distribution which can be picked up by the infrared camera; unique changes in the distribution of temperatures can be taken as reference signals for a particular type of imminent weld defect [9–11].”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of YU2 with DE FLIPPAS, RAHMAN, XAVIER, and YU, and SHRIKRISHNA as the references deal with predicting microhardness properties of a weld that defines a weld joint. YU2 would modify DE FLIPPAS, RAHMAN, XAVIER, and YU, and SHRIKRISHNA wherein transmit the sensed temperature values to the processor as the temperature data. The benefits of doing provides increased with an increase in heat input due to grain refinement in the microhardness process. (YU2 [Introduction]). Accordingly, claim 4 is rejected based on the combination of these references.
Claim 14
Claim 14 is rejected because it is the method embodiment of claim 4, with similar limitations to claim 4, and is such rejected using the same reasoning found in claim 4.
Claim 5
Claim 5 is rejected because the combination of DE FLIPPAS, RAHMAN, XAVIER, and YU teaches the claim 1 limitations.
The combination of DE FLIPPAS, RAHMAN, and XAVIER does not explicitly teach wherein the processor determines the cooling rate values in a temperature range of between about 750°C and 300°C YU.
However, YU teaches wherein the processor determines the cooling rate values in a temperature range of between about 750°C and 300°C YU ([Measurement of cooling rate in HAZ of laser temper bead welding | pdf page 4-5] “The microstructures of the simulated CGHAZ thermal cycle sample (Tp = 1350 °C) with the cooling rate of 100 and 2000 °C/s are shown in Fig. 7. Full martensitic structure can be observed in both samples, which explained the similar hardness even the cooling rate being varied from 100 to 2000 °C/s. It is well known that the hardness in HAZ of steel is generally determined by the percentage of martensite.”) generally determined by the percentage of martensite.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of YU with DE FLIPPAS, RAHMAN, and XAVIER, as the references deal with microhardness properties of a weld that defines a weld joint. YU would modify DE FLIPPAS, RAHMAN, and XAVIER wherein the processor determines the cooling rate values in a temperature range of between about 750°C and 300°C. The benefits of doing so effective for estimating the tempering effect during laser temper bead welding. (YU [Abstract]).
The combination of DE FLIPPAS, RAHMAN, XAVIER, and YU does not explicitly teach wherein the at least two workpieces are formed of an advanced high strength steel.
However, SHRIKRISHNA teaches wherein the at least two workpieces are formed of an advanced high strength steel SHRIKRISHNA ([Section 2.2 Friction Welding] “To produce high quality joints (at least two workpieces are formed), range of process parameters were chosen based on quantifying the weld samples (mild steel) to free of weld defects and with good mechanical properties (mild steel).”) See also SHRIKRISHNA ([Figure 2], [Table 3], and [Table 8].)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of SHRIKRISHNA with DE FLIPPAS, RAHMAN, XAVIER, and YU as the references deal with predicting microhardness properties of a weld that defines a weld joint. SHRIKRISHNA would modify DE FLIPPAS, RAHMAN, XAVIER, and YU wherein the at least two workpieces are formed of an advanced high strength steel. The benefits of doing provides increased with an increase in heat input due to grain refinement in the microhardness process. (SHRIKRISHNA [Introduction]).
The combination of DE FLIPPAS, RAHMAN, XAVIER, YU, and SHRIKRISHNA does not explicitly teach sense the temperature values of the welding process or transmit the sensed temperature values to the processor as the temperature data.
However, YU2 teaches sense the temperature values of the welding process OH ([Section 2 Experimental Setup | pdf page 3 of 8] “Figure 2 shows the welding experimental setup that is implemented. The infrared sensing system that is used to obtain the IR radiation from the surface of the plate being welded is made up of a galvanometer scanner and a point infrared sensor. The point infrared sensor is an Optris CTlaser 2MH which is adjusted to a temperature range between 385 and 1600 °C (685.15 to 1873.15 K) with an accuracy of ±1% over the entire range. The exposure time (90%) of the sensor is 1 ms. The software was designed to control the rotational speed and rotation direction of the galvanometer scanner. By controlling the scanning mirror of the galvanometer scanner rotating in a high speed, the infrared energy at different points of the plate being welded will be continually reflected to the point infrared sensor. This made it possible to obtain the temperature distribution of the welding seam and its heat affected zone by a point infrared sensor.
YU2 also teaches transmit the sensed temperature values to the processor as the temperature data YU2 ([Introduction] “Arc welding process as mentioned above is inherently a thermal-processing method; thus, infrared sensing is being considered as an effective method for monitoring the welding process. Every object with a temperature above the absolute zero (−273.15 °C = 0 K) emits an electromagnetic radiation from its surface, which is proportional to its intrinsic temperature. A part of this so-called intrinsic radiation is infrared radiation, which can be used to measure a body’s temperature. Previous investigations have shown that various disturbances to the welding process will change the heat flow and cause perturbations in the surface temperature distribution which can be picked up by the infrared camera; unique changes in the distribution of temperatures can be taken as reference signals for a particular type of imminent weld defect [9–11].”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of YU2 with DE FLIPPAS, RAHMAN, XAVIER, YU, and SHRIKRISHNA as the references deal with predicting microhardness properties of a weld that defines a weld joint. YU2 would modify DE FLIPPAS, RAHMAN, XAVIER, YU, and SHRIKRISHNA wherein transmit the sensed temperature values to the processor as the temperature data. The benefits of doing provides increased with an increase in heat input due to grain refinement in the microhardness process. (YU2 [Introduction]). Accordingly, claim 5 is rejected based on the combination of these references.
Claim 15
Claim 15 is rejected because it is the method embodiment of claim 5, with similar limitations to claim 5, and is such rejected using the same reasoning found in claim 5.
Conclusion
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/M.K.V./Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186