DETAILED ACTION
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 .
This is a response to U.S. Patent Application No. 18/627,109 filed on 04/04/2024 in which Claims 1 – 20 were filed for examination.
Status of the Claims
Claims 1 – 4, 6, 7, 9, 11 – 14, 16, 17 and 19 are rejected under 35 U.S.C. 102(a)(2) and Claims 5, 8, 10, 15, 18 and 20 are rejected under 35 U.S.C. 103.
Examiner Note
The Examiner cites particular columns, line numbers and/or paragraph numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/04/2024 and 11/12/2025 have been entered and considered by the examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 – 4, 6, 7, 9, 11 – 14, 16, 17 and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by CHABEAUTI et al. (US 2024/0111277) (hereinafter, Chabeauti).
Regarding Claim 1, Chabeauti teaches a method of adaptive machining of a forged part (See Chabeauti’s Abstract) comprising the steps of:
1) forming a rough part and subjecting the rough part to heat treatment (Chabeauti in par 0019, teaches that the simulation model is adapted to define a variability of the residual stress field in the workpiece based on manufacturing parameters of the raw workpiece, said raw workpiece having undergone at least one of the following manufacturing operations: forging, heat treatment, cold rolling, machining. Chabeauti in par 0052, teaches that a workpiece 1 may be a workpiece of varied size and/or of varied shape. It is preferably a large workpiece in the form of a beam, namely a workpiece one dimension of which is large compared to its other dimensions. The workpiece 1 is produced from a raw workpiece 2 obtained by usual manufacturing methods. The raw workpiece 2 is preferably a forged workpiece);
2) cooling the rough part (Chabeauti in par 0073, further teaches that the simulation model 13 is adapted to define a variability of the residual stress field in the workpiece 1 for a raw workpiece 2 obtained using at least one of the following manufacturing processes: forging, heat treatment, cold rolling, machining);
3) performing rough machining on the rough part (Chabeauti in par 0076, further teaches starting from a raw workpiece 2, enables a finished workpiece 5 to be produced via one or more states referred to hereinafter as “intermediate workpieces”. The workpiece 1 machined by the method M is the intermediate workpiece to which said method M is applied. Chabeauti in par 0087, further teaches In each iteration the intermediate workpiece 3 is the intermediate workpiece 4 of the preceding iteration. The series SE of steps is therefore repeated until the intermediate workpiece 4 is the finished workpiece 5 to be produced);
4) measuring a geometry of the rough part after the rough machining (Chabeauti in par 0056 – 0057, teaches that the measurement step E1 consist in measuring geometric parameters 6 (see fig. 3) of the workpiece 1 at a given moment during machining thereof, at which given moment the residual stress field RS needs to be known. The geometric parameters 6 measured in this way make it possible to determine a geometric profile 7 of the workpiece 1. That geometric profile 7 may have dimensions measured on the workpiece 1 at the given moment as well as other data. For example, it may include other geometric parameters of the workpiece 1 measured during earlier machining steps or deformations 9 that said workpiece 1 has undergone during previous machining steps. These deformations 9 may be caused by residual stresses in the workpiece 1 and may be manifested in various forms, so for example the workpiece 1 may be twisted or bent, in particular in the case of workpieces of elongate shape), and associating the measured geometry with heating and cooling parameters from steps 1) and 2), and providing the measured geometry to a machine learning module (Chabeauti in par 0061 – 0062, further teaches that the estimation model 10 is trained in such a manner as to be able to use the geometric profile 7 of the workpiece 1 and the prerecorded data 12 in the database DATA to estimate the subsequent deformations 11 of the workpiece 1. Moreover, the step E3 of estimating a residual stress field RS in the workpiece 1 uses a simulation model 13 taking into account the subsequent deformations 11 estimated in the estimation step E2);
5) providing the machine learning module with a training set that associates the measured geometry with a predicted reaction to finish machining (Chabeauti in par 0059 – 0060, further teaches that the estimation model 10 is first trained by machine learning on so-called training parts. For example, the estimation model 10 may be trained with the aid of artificial intelligence taking into account prerecorded data 12 obtained from training workpieces. This prerecorded data 12 includes at least geometric parameters measured on said training workpieces. The training workpieces are preferably workpieces similar to the workpiece 1 that are machined by a machining process similar to that for machining the workpiece 1. By “similar workpieces” is meant workpieces with the same dimensions, made of the same material, having the same mechanical characteristics and obtained from similar raw workpieces); and
6) adapting a finish machining strategy based upon the prediction (Chabeauti in par 0067, further teaches that the method M makes it possible to estimate the residual stress field RS in the workpiece 1 during machining without recourse to direct calculation of said residual stresses. In particular it makes it possible to estimate the residual stress field RS at a very early stage of machining the workpiece 1. It is then possible to anticipate the behavior of said workpiece 1 during further machining and to adjust said machining as specified hereinafter. Chabeauti in par 0089, further teaches that he evolution of the residual stress field RS in the intermediate workpiece 3 can then be taken into account at any moment during machining. Moreover, the machining parameter or parameters 14 may be adjusted accordingly and in real time).
Regarding Claim 2, Chabeauti teaches the limitations contained in parent Claim 1. Chabeauti further teaches:
wherein the machine learning module considers temperatures on the rough part during the heat treatment of step 1) (Chabeauti in par 0064, further teaches that it is possible to calculate residual stresses potentially induced by each of said manufacturing steps knowing the parameters of the various manufacturing steps and the geometry of the raw workpiece 2. By way of non-exhaustive example, those parameters may include values of particular dimensions of the raw workpiece 2, of the dimension differences between certain parts of the raw workpiece 2, of forces or of pressures linked to operations in the manufacture of the raw workpiece 2 (compression, machining, . . . ), error values relating to said manufacturing operations or temperature values in the case of heat treatment).
Regarding Claim 3, Chabeauti teaches the limitations contained in parent Claim 2. Chabeauti further teaches:
wherein the machine learning module considers a cooling rate of the rough part during step 2) (Chabeauti in par 0073, further teaches that the simulation model 13 is adapted to define a variability of the residual stress field in the workpiece 1 for a raw workpiece 2 obtained using at least one of the following manufacturing processes: forging, heat treatment, cold rolling, machining).
Regarding Claim 4, Chabeauti teaches the limitations contained in parent Claim 3. Chabeauti further teaches:
wherein the finished part is an aerospace part (Chabeauti in par 0103, further teaches that the process P in which machining the raw workpiece 2 enables the finished workpiece 5 to be obtained. This refers to machining an aeronautical workpiece that is referred to as a “cross” in the remainder of the description. The cross has a straight elongate shape with a cruciform cross section, as depicted in FIG. 4 and FIG. 5).
Regarding Claim 6, Chabeauti teaches the limitations contained in parent Claim 1. Chabeauti further teaches:
wherein the machine learning module considers a cooling rate of the rough part during step 2) (Chabeauti in par 0073, further teaches that the simulation model 13 is adapted to define a variability of the residual stress field in the workpiece 1 for a raw workpiece 2 obtained using at least one of the following manufacturing processes: forging, heat treatment, cold rolling, machining).
Regarding Claim 7, Chabeauti teaches the limitations contained in parent Claim 6. Chabeauti further teaches:
wherein the finished part is an aerospace part (Chabeauti in par 0103, further teaches that the process P in which machining the raw workpiece 2 enables the finished workpiece 5 to be obtained. This refers to machining an aeronautical workpiece that is referred to as a “cross” in the remainder of the description. The cross has a straight elongate shape with a cruciform cross section, as depicted in FIG. 4 and FIG. 5).
Regarding Claim 9, Chabeauti teaches the limitations contained in parent Claim 1. Chabeauti further teaches:
wherein the finished part is an aerospace part (Chabeauti in par 0103, further teaches that the process P in which machining the raw workpiece 2 enables the finished workpiece 5 to be obtained. This refers to machining an aeronautical workpiece that is referred to as a “cross” in the remainder of the description. The cross has a straight elongate shape with a cruciform cross section, as depicted in FIG. 4 and FIG. 5).
Regarding Claim 11, Chabeauti teaches a system for machining a part after a forging operation (See Chabeauti’s Abstract) comprising:
at least one machine for providing rough machining and subsequent machining (Chabeauti in par 0019, teaches that the simulation model is adapted to define a variability of the residual stress field in the workpiece based on manufacturing parameters of the raw workpiece, said raw workpiece having undergone at least one of the following manufacturing operations: forging, heat treatment, cold rolling, machining. Chabeauti in par 0052, teaches that a workpiece 1 may be a workpiece of varied size and/or of varied shape. It is preferably a large workpiece in the form of a beam, namely a workpiece one dimension of which is large compared to its other dimensions. The workpiece 1 is produced from a raw workpiece 2 obtained by usual manufacturing methods. The raw workpiece 2 is preferably a forged workpiece); and
a control for the at least one machine, the control having a machine learning module and processing circuitry operable to associate heat treatment information from a heat treating system and cooling information from a cooling system (Chabeauti in par 0061 – 0062, further teaches that the estimation model 10 is trained in such a manner as to be able to use the geometric profile 7 of the workpiece 1 and the prerecorded data 12 in the database DATA to estimate the subsequent deformations 11 of the workpiece 1. Moreover, the step E3 of estimating a residual stress field RS in the workpiece 1 uses a simulation model 13 taking into account the subsequent deformations 11 estimated in the estimation step E2. Chabeauti in par 0073, further teaches that the simulation model 13 is adapted to define a variability of the residual stress field in the workpiece 1 for a raw workpiece 2 obtained using at least one of the following manufacturing processes: forging, heat treatment, cold rolling, machining. Chabeauti in par 0129, further teaches that the system may include a controller or a computing device comprising a processing and a memory), with measured information from rough machining to predict a residual stress and operable to develop and implement a finished machining strategy for the at least one machine based upon the prediction (Chabeauti in par 0056 – 0057, teaches that the measurement step E1 consist in measuring geometric parameters 6 (see fig. 3) of the workpiece 1 at a given moment during machining thereof, at which given moment the residual stress field RS needs to be known. The geometric parameters 6 measured in this way make it possible to determine a geometric profile 7 of the workpiece 1. That geometric profile 7 may have dimensions measured on the workpiece 1 at the given moment as well as other data. For example, it may include other geometric parameters of the workpiece 1 measured during earlier machining steps or deformations 9 that said workpiece 1 has undergone during previous machining steps. These deformations 9 may be caused by residual stresses in the workpiece 1 and may be manifested in various forms, so for example the workpiece 1 may be twisted or bent, in particular in the case of workpieces of elongate shape. Chabeauti in par 0067, further teaches that the method M makes it possible to estimate the residual stress field RS in the workpiece 1 during machining without recourse to direct calculation of said residual stresses. In particular it makes it possible to estimate the residual stress field RS at a very early stage of machining the workpiece 1. It is then possible to anticipate the behavior of said workpiece 1 during further machining and to adjust said machining as specified hereinafter).
Regarding Claim 12, Chabeauti teaches the limitations contained in parent Claim 11. Chabeauti further teaches:
wherein the machine learning module is operable to predict the residual stress based on temperatures on the rough part during the heat treatment (Chabeauti in par 0064, further teaches that it is possible to calculate residual stresses potentially induced by each of said manufacturing steps knowing the parameters of the various manufacturing steps and the geometry of the raw workpiece 2. By way of non-exhaustive example, those parameters may include values of particular dimensions of the raw workpiece 2, of the dimension differences between certain parts of the raw workpiece 2, of forces or of pressures linked to operations in the manufacture of the raw workpiece 2 (compression, machining, . . . ), error values relating to said manufacturing operations or temperature values in the case of heat treatment).
Regarding Claim 13, Chabeauti teaches the limitations contained in parent Claim 12. Chabeauti further teaches:
wherein the machine learning module is operable to predict the residual stress based on a cooling rate of the rough part (Chabeauti in par 0064, further teaches that it is possible to calculate residual stresses potentially induced by each of said manufacturing steps knowing the parameters of the various manufacturing steps and the geometry of the raw workpiece 2. Chabeauti in par 0073, further teaches that the simulation model 13 is adapted to define a variability of the residual stress field in the workpiece 1 for a raw workpiece 2 obtained using at least one of the following manufacturing processes: forging, heat treatment, cold rolling, machining).
Regarding Claim 14, Chabeauti teaches the limitations contained in parent Claim 13. Chabeauti further teaches:
wherein the finished part is an aerospace part (Chabeauti in par 0103, further teaches that the process P in which machining the raw workpiece 2 enables the finished workpiece 5 to be obtained. This refers to machining an aeronautical workpiece that is referred to as a “cross” in the remainder of the description. The cross has a straight elongate shape with a cruciform cross section, as depicted in FIG. 4 and FIG. 5).
Regarding Claim 16, Chabeauti teaches the limitations contained in parent Claim 11. Chabeauti further teaches:
wherein the machine learning module is operable to predict the residual stress based on a cooling rate of the rough part (Chabeauti in par 0064, further teaches that it is possible to calculate residual stresses potentially induced by each of said manufacturing steps knowing the parameters of the various manufacturing steps and the geometry of the raw workpiece 2. Chabeauti in par 0073, further teaches that the simulation model 13 is adapted to define a variability of the residual stress field in the workpiece 1 for a raw workpiece 2 obtained using at least one of the following manufacturing processes: forging, heat treatment, cold rolling, machining).
Regarding Claim 17, Chabeauti teaches the limitations contained in parent Claim 16. Chabeauti further teaches:
wherein the finished part is an aerospace part (Chabeauti in par 0103, further teaches that the process P in which machining the raw workpiece 2 enables the finished workpiece 5 to be obtained. This refers to machining an aeronautical workpiece that is referred to as a “cross” in the remainder of the description. The cross has a straight elongate shape with a cruciform cross section, as depicted in FIG. 4 and FIG. 5).
Regarding Claim 19, Chabeauti teaches the limitations contained in parent Claim 11. Chabeauti further teaches:
wherein the finished part is an aerospace part (Chabeauti in par 0103, further teaches that the process P in which machining the raw workpiece 2 enables the finished workpiece 5 to be obtained. This refers to machining an aeronautical workpiece that is referred to as a “cross” in the remainder of the description. The cross has a straight elongate shape with a cruciform cross section, as depicted in FIG. 4 and FIG. 5).
Claim Rejections - 35 USC § 103
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.
Claims 5, 8, 10, 15, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chabeauti in view of LAPPAS et al. (US 2018/0095450) (hereinafter, Lappas).
Regarding Claim 5, Chabeauti teaches the limitations contained in parent Claim 4. Chabeauti further teaches:
Chabeauti in par 0103, teaches machining an aeronautical workpiece that is referred to as a “cross”. The cross has a straight elongate shape with a cruciform cross section.
However, Chabeauti does not specifically disclose wherein the aerospace part is one of an integrally bladed rotor, a casing, a blade, and a turbine disk.
Lappas teaches the formation of at least one 3D object (See Lappas’ Abstract).
Lappas in par 0171, further teaches that the 3D object can be retrieved when the 3D part, composed of hardened (e.g., solidified) material, is at a handling temperature that is suitable to permit the removal of the 3D object from the material bed without substantial deformation. Lappas in par 0184, further teaches that the material (e.g., alloy or elemental) may comprise a material used for applications in industries comprising aerospace (e.g., aerospace super alloys), jet engine, missile, automotive, marine, locomotive, satellite, defense, oil & gas, energy generation, semiconductor, fashion, construction, agriculture, printing, or medical.
Lappas in par 0235, further teaches that the physics model (and associated simulations) includes calculations of an estimated deformation that consider change in state of the material (e.g., in relation to density and/or surface tension). The 3D object can be characterized as having an overall shape (e.g., cone shape, toroidal shape, disk shape, disc cone shape, spherical shape, wing shape, spiral shape, or bridge shape.) that can cause it to deform in a characteristic way. Lappas in par 0254, further teaches FIG. 33A shows a geometric model (e.g., CAD drawing) of a requested 3D object 3300 having a spiral blade shape. FIGS. 26A-26D show perspective views of graphical representations of four example modes for the 3D object having a spiral blade shape (e.g., considering the geometric model of the requested object shown in FIG. 33A)
Therefore, it would have being obvious to one of ordinary skill in the art before the effective filing date to utilize the teachings as in Lappas with the teachings as in Chabeauti to finished parts in Chabeauti from a plurality of industries as in Lappas. The motivation for doing so would have been to provide a process of making parts of any shape from a design (See Lappas’ par 0003).
Regarding Claim 8, Chabeauti teaches the limitations contained in parent Claim 7.
wherein the aerospace part is one of an integrally bladed rotor, a casing, a blade, and a turbine disk (See the above rejection of Claim 5).
Regarding Claim 10, Chabeauti teaches the limitations contained in parent Claim 9.
wherein the aerospace part is one of an integrally bladed rotor, a casing, a blade, and a turbine disk (See the above rejection of Claim 5).
Regarding Claim 15, Chabeauti teaches the limitations contained in parent Claim 14.
wherein the aerospace part is one of an integrally bladed rotor, a casing, a blade, and a turbine disk (See the above rejection of Claim 5).
Regarding Claim 18, Chabeauti teaches the limitations contained in parent Claim 17.
wherein the aerospace part is one of an integrally bladed rotor, a casing, a blade, and a turbine disk (See the above rejection of Claim 5).
Regarding Claim 20, Chabeauti teaches the limitations contained in parent Claim 19.
wherein the aerospace part is one of an integrally bladed rotor, a casing, a blade, and a turbine disk (See the above rejection of Claim 5).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARIEL MERCADO VARGAS whose telephone number is (571)270-1701. The examiner can normally be reached M-F 8:00am - 4:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached at 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ARIEL MERCADO-VARGAS/Primary Examiner, Art Unit 2118
Prosecution Insights