DETAILED ACTION
This Office Action is responsive to the claims filed on December 8, 2022. Claims 1-19 are under examination. Claims 1, 16, and 18 are independent claims.
Claims 1, 5, 9, and 15-19 are rejected under 35 USC 103. Because the primary Bandara reference has the potential to be overcome by declaration, alternative 35 USC 103 rejections are provided in accordance with MPEP 2120(I)(C).
Primary Set of 35 USC 103 Rejections
Claims 1, 5, 9, and 15-19 are rejected under 35 USC 103 as obvious over Bandara in view of Mahalik.
Backup Set of 35 USC 103 Rejections
Claims 1, 5, 9, 16, and 18 are rejected under 35 USC 103 as obvious over Nourbakhsh, Mahalik, and Li.
Claims 15, 17, and 19 are rejected under 35 USC 103 as obvious over Nourbakhsh, Mahalik, Li, and Razzell.
Claims 2-4, 6-8, and 10-14 are allowable over art.
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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on February 9, 2023, November 10, 2023, December 1, 2023, January 5, 2024, August 21, 2025, and January 12, 2026 were filed prior to this action. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
EXAMINER’S NOTE: The best reference is interpreted to be the Bandara reference of record filed by the Applicant of the instant application. Under MPEP 2120(I)(C), while compact prosecution typically involves a single rejection being made for each claim under examination, exceptions are provided when “the most pertinent disclosure could be shown not to be prior art by invoking an exception in a 37 CFR 1.130 affidavit or declaration of attribution or prior public disclosure.” The Bandara reference is prior art in part because the inventive entity of the instant application partially differs from the inventive entity of the Bandara reference, restricting the application of the 102(b)(1) exceptions to the use of Bandara as prior art to the instant application. Under this circumstance, MPEP 2106(I)(C) states,
In the interest of compact prosecution, such rejections should be backed up by the best other art rejections available. Keep in mind the best backup rejection(s) could be based on alternate embodiments from the same "best available" reference(s). For example, if an anticipation rejection could be overcome by invoking an exception in a 37 CFR 1.130(b) declaration, it would be appropriate to make an additional obviousness rejection over another disclosure in the same reference. Merely cumulative rejections, i.e., those which would clearly fall if the primary rejection were not sustained, should be avoided.
Accordingly, the Office Action includes a primary set of 35 USC 103 rejections based on the Bandara and Mahalik references of record and a backup set of 35 USC 103 rejections based on the Nourbakhsh (NB), Mahalik, Li, and Razell references.
Primary Set of 35 USC 103 Rejections
Claims 1, 5, 9, and 15-19: Bandara and Mahalik
Claim(s) 1, 5, 9, 16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0150623 A1 to Bandara et al. (Bandara) in view of
Claims 1, 9, and 16
Regarding claim 16, Bandara teaches:
A system comprising: a non-transitory storage medium having instructions of a computer aided design program stored thereon; and one or more data processing apparatus configured to run the instructions of the computer aided design program to: (Bandara [0052] “FIG. 1A shows an example of a system 100 usable to design and manufacture physical structures. A computer 110 includes a processor 112 and a memory 114 […] The computer 110 can include various types of computer storage media and devices, which can include the memory 114, to store instructions of programs that run on the processor 112, including Computer Aided Design (CAD) program(s) 116, which implement three-dimensional (3D) modeling functions and includes one or more generative design processes for topology optimization (e.g., using at least one level-set method as described) with physical simulation.”)
obtain a design space for a modeled object, for which a corresponding physical structure will be manufactured, one or more design criteria for the modeled object, one or more in-use load cases for the physical structure, and a [property of] a material from which the physical structure will be manufactured; (Bandara Claim 1 “obtaining, by a computer aided design program, a design space for an object to be manufactured, a setup for physical simulation of the object, at least one design objective for the object, and at least one design constraint for the object;” – A design space for the modeled object is obtained. [0157] “FIG. 4A shows an example of a process (e.g., as performed by the CAD program(s) 116 of FIG. 1A) that optimizes a topology of lattice infill for one or more portions of a 3D model of an object being generatively designed for manufacture. Design criteria, boundary conditions and/or other design variables are obtained 400, e.g., by the CAD program(s) 116. This can involve receiving user input, e.g., via UI 122 on display device 120, importing information from another program or a third party source, and/or one or more of these inputs can be predefined in a given implementation.” – Inputs include one or more design criteria for the modeled object. [0055] “In this case, the defined problem is the Michell type arch problem, where the user 160 has specified a domain 134 and loading cases 136. However, this is but one of many possible examples.” – This accounts for one or more loading cases from user inputs. [0056] “ In some implementations, the inputs for use in physical simulation and generative design processes can include one or more regions of a current 3D model in which to generate new 3D geometry, loading case(s) defining one or more loads in one or more different directions to be borne by a physical structure being designed, one or more materials (e.g., one or more isotropic solid materials identified as a baseline material model for the design space), one or more seed model types to use as input to a generative design process, one or more generative design processes to use, and/or one or more lattice topologies to use in one or more regions of the design space. Inputs to the generative design and physical simulation processes can include non-design spaces, different types of components (e.g., rods, bearings, shells), one or more target manufacturing processes and associated parameters, obstacle geometries that should be avoided, preserve geometries that should be included in the final design, and parameters related to various aspects, such as resolution of the design, type of synthesis, etc.” [0122] “ Therefore, the lattice microstructures can be represented as a continuous material with equivalent properties.“ [0160] “Further, the simulation of physical properties can include, among other possibilities, simulating buckling, natural frequency, thermal, electric or electro-magnetic flux, and material solidification properties. Note that the setup for physical simulation(s) will be different for different physical simulation(s).” – Inputs include one or more design criteria for the modeled object, one or more in-use load cases for the physical structure, and material properties.)
iteratively modify a generatively designed three dimensional shape of the modeled object in the design space in accordance with the one or more design criteria, the one or more in-use load cases for the physical structure, and the [property of] the material, wherein the one or more data processing apparatus are configured to run the instructions of the computer aided design program to iteratively modify the generatively designed three dimensional shape of the modeled object by being configured to run the instructions of the computer aided design program to enforce a design criterion that limits a minimum thickness of the generatively designed three dimensional shape of the modeled object, the minimum thickness being based on the [property of] the material; and (Bandara [0196] “Both a three dimensional topology of a generative model for the object and one or more outer shapes of the three dimensional topology are iteratively modified 505, e.g., by the CAD program(s) 116, using a generative design process that represents the three dimensional topology of the generative model as one or more boundaries between one or more solid regions and one or more void regions within the design space. For example, this can include using gradient based algorithms to optimize the 3D topology and shape of the object model, or other algorithms, such as evolutionary algorithms and stochastic algorithms. Further, the generative model can be a level-set representation of the one or more outer shapes of the three dimensional topology, and a level-set method of topology optimization can be employed. In some implementations, the iterative modification 505 involves using the systems and techniques described above in connection with FIGS. 3A (Hollow) and 5B-5F.” [0189] “The design variables can include the thickness values of each polygon vertex in the skin region which are initiated to the minimum thickness tmin at the start of the algorithm.” – Bandara iteratively modifies the 3D model in accordance with design criteria and other inputs. [0157] “FIG. 4A shows an example of a process (e.g., as performed by the CAD program(s) 116 of FIG. 1A) that optimizes a topology of lattice infill for one or more portions of a 3D model of an object being generatively designed for manufacture. Design criteria, boundary conditions and/or other design variables are obtained 400, e.g., by the CAD program(s) 116. This can involve receiving user input, e.g., via UI 122 on display device 120, importing information from another program or a third party source, and/or one or more of these inputs can be predefined in a given implementation.” – Inputs include one or more design criteria for the modeled object. [0055] “In this case, the defined problem is the Michell type arch problem, where the user 160 has specified a domain 134 and loading cases 136. However, this is but one of many possible examples.” – This accounts for one or more loading cases from user inputs. [0056] “ In some implementations, the inputs for use in physical simulation and generative design processes can include one or more regions of a current 3D model in which to generate new 3D geometry, loading case(s) defining one or more loads in one or more different directions to be borne by a physical structure being designed, one or more materials (e.g., one or more isotropic solid materials identified as a baseline material model for the design space), one or more seed model types to use as input to a generative design process, one or more generative design processes to use, and/or one or more lattice topologies to use in one or more regions of the design space. Inputs to the generative design and physical simulation processes can include non-design spaces, different types of components (e.g., rods, bearings, shells), one or more target manufacturing processes and associated parameters, obstacle geometries that should be avoided, preserve geometries that should be included in the final design, and parameters related to various aspects, such as resolution of the design, type of synthesis, etc.” [0160] “Further, the simulation of physical properties can include, among other possibilities, simulating buckling, natural frequency, thermal, electric or electro-magnetic flux, and material solidification properties. Note that the setup for physical simulation(s) will be different for different physical simulation(s).” – Inputs include one or more design criteria for the modeled object, one or more in-use load cases for the physical structure, and material properties. [0153] “In Algorithm 5, the thickness design variable can be represented by volume fraction at each solid element in the lattice region. The thickness values are initiated to the minimum thickness tmin at the start of the algorithm. The FEA model used for simulation can represent a lattice as a solid element with equivalent lattice properties (RVEs).” – A minimum thickness is used as a constraint to optimize the thickness of the model.)
provide the generatively designed three dimensional shape of the modeled object for use in manufacturing the physical structure corresponding to the modeled object using one or more computer-controlled manufacturing systems. (Bandara Claim 1 “providing, by the computer aided design program, a three dimensional model of the object in accordance with the three dimensional topology, the one or more outer shapes of the three dimensional topology, the adjusted thickness of the hollow structure and the adjusted lattice, for use in manufacturing a physical structure corresponding to the object using one or more computer-controlled manufacturing systems.” – The 3D generatively designed model is provided for manufacture.)
Bandara teaches that the model accounts for physical properties of materials that would affect the thickness determination of the generatively designed model (Bandara [0009] “The physical simulation performed by the systems and techniques described in this document can simulate one or more physical properties and can use one or more types of simulation. For example, FEA, including linear static FEA, finite difference method(s), and material point method(s) can be used. Further, the simulation of physical properties can include, among other possibilities, simulating buckling, natural frequency, thermal, electric or electro-magnetic flux, and material solidification properties. Moreover, different types of generative models and generative design processes can be used.” [0013] “The homogenized lattice material representation can express structural behavior of a given lattice as an anisotropic solid material being a continuous material with properties approximately equivalent to the given lattice.”), but Bandara does not appear to explicitly teach, but Bandara in view of Mahalik teaches:
obtain a design space for a modeled object, for which a corresponding physical structure will be manufactured, one or more design criteria for the modeled object, one or more in-use load cases for the physical structure, and a critical fatigue crack length for a material from which the physical structure will be manufactured;
iteratively modify a generatively designed three dimensional shape of the modeled object in the design space in accordance with the one or more design criteria, the one or more in-use load cases for the physical structure, and the [property of] fatigue crack length for the material, wherein the one or more data processing apparatus are configured to run the instructions of the computer aided design program to iteratively modify the generatively designed three dimensional shape of the modeled object by being configured to run the instructions of the computer aided design program to enforce a design criterion that limits a minimum thickness of the generatively designed three dimensional shape of the modeled object, the minimum thickness being based on the critical fatigue crack length for the material; and (Mahalik See Table 4 on Page 7 and Table 5 on Page 8 ALSO Abstract “Generative Design technique has revolutionised the method of designing a structural component to attain the optimum component’s geometry while maintaining its structural integrity in withstanding the given loads, both static and fatigue. Since the MLG fitting is one of the Principal Structural Elements (PSEs) of the aircraft, the failure of this component can lead into a catastrophic accident. Hence, a damage tolerance analysis is essential to be conducted in order to determine the service life and the inspection interval of the fitting. Here, a combination of the short- and long-range flights for the aircraft was used to construct the load spectrum utilised in fitting’s crack growth examination. It is expected that the implementation of both the topology optimisation and the damage tolerance analysis on the fitting could significantly reduce the weight of the fitting while maintaining the compliance to the required static strength and damage tolerance aspects as stated on the Federal Acquisition Regulation (FAR) 23.” – The model is optimized for weight/thickness based on stress factors. Page 8 “Common crack detection methods in the industry is able to distinguish a crack with a size of four millimetres at the smallest. By considering that, hence following the expression in Eq. 4, the inspection interval for the topology optimised fitting is tabulated as in Tab. 5.”
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Where I is the inspection interval in cycles, acrit is the critical crack length at fracture in
millimetres, and adet is the detectable crack length measured by the device in millimetres.” – One stress factor, upon which the optimization is conducted, is critical crack length.)
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It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the size of a model in generative modeling based on material properties in Bandara by the material properties used in generative design to determine size of the model in Mahalik because the person of ordinary skill in the art would be motivated, based on the aim of performing generative modeling using physical properties of a material in Bandara to look to Mahalik, which accounts for material stress factors to optimize the weight of a generatively designed model. (Bandara [0015]-[0016] “Particular embodiments of the subject matter described in this specification can be implemented to realize one or more of the following advantages. A CAD program can provide a variety of different generative design synthesis methods to choose from, including: a level-set-based topology optimization that provides a basic level-set method for topology optimization, a lattice and skin optimization that provides a thickness optimization of lattice and skin, a hybrid topology optimization that provides a topology optimization with lattice infill, an inside-out hybrid topology optimization in which the lattice infill is present in a negative space between the topology-optimized design and the original design space, a hollow topology optimization that provides a method for topology optimization with internal hollow regions, and a hybrid-hollow topology optimization that provides a method for topology optimization with lattice infill and internal hollow regions. The user can be enabled to mix and match different generative design synthesis methods, as well as a variety of input design variables, to produce generative design processes that facilitate the creation of new generative designs that meet the user's goals. Introducing a lattice inside a part during the topology optimization process can increase the specific stiffness of the part (stiffness/mass) and improve manufacturability of the design, e.g., when using additive manufacturing. By considering the effects of lattice behavior during topology optimization, the performance to weight ratio of the part can be improved. Further, components can be designed that contain an optimized lattice structure within a topology optimized body based on expected structural loading to produce lightweight designs with high stiffness.” Claim 10 “The method of claim 1, wherein the homogenized lattice material representation expresses structural behavior of a given lattice as an anisotropic solid material being a continuous material with properties approximately equivalent to the given lattice.”; Mahalik Abstract “Here, a combination of the short- and long-range flights for the aircraft was used to construct the load spectrum utilised in fitting’s crack growth examination. It is expected that the implementation of both the topology optimisation and the damage tolerance analysis on the fitting could significantly reduce the weight of the fitting while maintaining the compliance to the required static strength and damage tolerance aspects as stated on the Federal Acquisition Regulation (FAR) 23.”)
Claims 1 and 18 recite features substantially similar to the features of claim 16 and are rejected for at least the same reasons.
Claim 5
Regarding claim 5, Bandara in view of Mahalik and Li teach the features of claim 1 and further teach:
wherein the enforcing uses a measure of thickness for the generatively designed three dimensional shape of the modeled object that is a combination of at least two distinct thickness measures. (Bandara [0011] “Adjusting the thickness of the hollow structure can include, starting from one or more inner shapes of the hollow structure, iteratively modifying the one or more inner shapes of the three dimensional topology using the generative design process, including changing the constitutive model for the physical simulation in accordance with (i) a current iteration of the three dimensional topology and the one or more inner shapes and (ii) the homogenized lattice material representation, such that the thickness of the hollow structure varies across the three dimensional topology after modifying the one or more inner shapes is completed. The generative model can include a level-set representation of the one or more inner shapes and the one or more outer shapes of the three dimensional topology, and the generative design process can employ a level-set method of topology optimization to iteratively modify the one or more inner shapes and the one or more outer shapes of the three dimensional topology.” – Bandara enforces constraints by designing a shape with a combination of at least two distinct thickness measures. The shapes in Bandara explicitly can have more than one thickness.)
Claim 9
Regarding claim 9, Bandara in view of Mahalik and Li teach the features of claim 1 and further teach:
wherein obtaining the critical fatigue crack length for the material comprises: obtaining one or more specifications of the material from which the physical structure will be manufactured; and (Mahalik Page 2, Second Paragraph “The fitting uses AISI 4340, in which considered as a high-strength low alloy steel material (Wanhill et al., 2010). The physical and the material properties of the original MLG fitting is given in Tab. 1.” See Also Table 1 on Page 2 – One or more properties are provided for AISI 4340 for calculation of the critical crack length.)
Claims 15, 17, and 19
Regarding claim 17, Bandara in view of Mahalik teaches the features of claim 16 and further teaches:
wherein the one or more computer-controlled manufacturing systems comprise an additive manufacturing machine, and the operations comprise: generating toolpath specifications for the additive manufacturing machine from the three dimensional model; and manufacturing the physical structure corresponding to the object with the additive manufacturing machine using the toolpath specifications. (Bandara [0013] “ Moreover, the one or more computer-controlled manufacturing systems can include an additive manufacturing machine, and the providing can include: generating toolpath specifications for the additive manufacturing machine from the three dimensional model; and manufacturing the physical structure corresponding to the object with the additive manufacturing machine using the toolpath specifications.” - Verbatim)
Backup Set of 35 USC 103 Rejections
Claims 1, 5, 9, 16, and 18: NB, Mahalik, and Li
Claim(s) 1, 5, 9, 16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0155966 A1 to Nourbakhsh et al. (NB) in view of NPL: “Topology and Damage Tolerance Optimisation of an Island-Hopping Aircraft MLG Fitting using Generative Design” by Mahalik (Mahalik) and NPL: “Topology and thickness optimization of an indenter under stress and stiffness constraints” by Li et al. (Li).
Claims 1, 16, and 18
Regarding claim 16, NB teaches:
A system comprising: a non-transitory storage medium having instructions of a computer aided design program stored thereon; and one or more data processing apparatus configured to run the instructions of the computer aided design program to: (NB [0003] “Computer-aided design (CAD) is the use of one or more computer systems to design, among other things, two-dimensional curves or figures and three-dimensional surfaces and solids.” [0059] “As shown, computing device 600 includes, without limitation, an interconnect (bus) 640 that connects a processing unit 650, an input/output (I/O) device interface 660 coupled to input/output (I/O) devices 680, memory 610, a storage 630, and a network interface 670.” – Computer with memory and processor configured to run CAD instructions.)
obtain a design space for a modeled object, for which a corresponding physical structure will be manufactured, one or more design criteria for the modeled object, one or more in-use load cases for the physical structure, and [stress/fatigue parameters]; (NB [0019] “Problem definition 201 includes physical constraints of a design solution (geometric, equilibrium, stress, and other applicable constraints). In addition, problem definition 201 includes an input frame 211 as a starting point for process 200. Input frame 211 includes a collection of trusses connected at their endpoints by pin joints, either to each other or to fixed points (fixed supports) in space. In some embodiments, problem definition 201 further includes an optimization objective, which is typically user-selected and indicates the design goal for frames generated by process 200. For example, for a frame generated by process 200, user-selectable optimization objective can include a total weight of the frame, a cost of the frame, a heat transfer capability of the frame, and the like. Problem definition 201 may further include user-selectable parameters for divergent search algorithm 202, and/or convergent search algorithm 203.” – Design space, modeled object, design criteria [0039] “In representing the physics of the load case or cases to be considered, forces are applied at certain nodes of a frame. Since, in the exemplary embodiment described herein, the trusses in the network are connected to each other by pin joints, each truss responds to the applied forces by extending or compressing in the axial directions only.” – In-use load cases. [0043] “In some embodiments, stress constraints can be implemented as follows. Since the trusses are composed of a physical material that can experience failure or breakage, the trusses cannot sustain arbitrarily large tensile and compressive stresses. Since stress is defined as force per unit area, the stress constraints can include user-prescribed thresholds derived from material maximum yields and safety factors. Any suitable constraint formulation implementing the above stress constraints can then be employed in convergent search algorithm 203.” – Stress/fatigue parameters)
iteratively modify a generatively designed three dimensional shape of the modeled object in the design space in accordance with the one or more design criteria, the one or more in-use load cases for the physical structure, [stress/fatigue parameters], wherein the one or more data processing apparatus are configured to run the instructions of the computer aided design program to iteratively modify the generatively designed three dimensional shape of the modeled object (NB [0003] “For example, given certain inputs, such as user-specified load conditions and geometric constraints, some CAD software programs can perform generative design operations, which are iterative design operations that ultimately generate one or more designs satisfying the user-specified constraints. A user can then sort through the generated designs and decide which to refine further.” – CAD programs iteratively modify designs to satisfy constraints and design objectives. [0043] “In some embodiments, stress constraints can be implemented as follows. Since the trusses are composed of a physical material that can experience failure or breakage, the trusses cannot sustain arbitrarily large tensile and compressive stresses. Since stress is defined as force per unit area, the stress constraints can include user-prescribed thresholds derived from material maximum yields and safety factors. Any suitable constraint formulation implementing the above stress constraints can then be employed in convergent search algorithm 203.” – Stress/fatigue parameters)
by being configured to run the instructions of the computer aided design program to enforce a design criterion that limits a minimum [physical characteristic] of the generatively designed three dimensional shape of the modeled object, the minimum [physical characteristic] being based on the [stress/fatigue parameters] for the material; and (NB [0016] “Multi-modal objective function 100 quantifiably expresses a value of a physical or other characteristic 110 of a design solution to a particular problem, such as weight, cost, peak temperature achieved during operation, etc., with respect to one or more optimization parameters 111 (only a single parameter is shown in FIG. 1). “ - [0043] “In some embodiments, stress constraints can be implemented as follows. Since the trusses are composed of a physical material that can experience failure or breakage, the trusses cannot sustain arbitrarily large tensile and compressive stresses. Since stress is defined as force per unit area, the stress constraints can include user-prescribed thresholds derived from material maximum yields and safety factors. Any suitable constraint formulation implementing the above stress constraints can then be employed in convergent search algorithm 203.” – Stress/fatigue parameters)
provide the generatively designed three dimensional shape of the modeled object for use in manufacturing the physical structure corresponding to the modeled object using one or more computer-controlled manufacturing systems. (NB [0018] “The output of process 200 can be a single frame design (or “frame”) that satisfies certain geometric, manufacturing, and/or other physical constraints.” – Designs are output. [0007] “Thus, in certain cases, conventional generative design approaches generate designs for strong and light-weight structural frames that are difficult and expensive to manufacture.” – Designs from CAD are provided to manufacture structures. [0003] “Computer-aided design (CAD) is the use of one or more computer systems to design, among other things, two-dimensional curves or figures and three-dimensional surfaces and solids.” “For example, in the context of frame design, iterative solutions 120 are generated sequentially by the convergent search algorithm when starting with a first input frame 121 as the initial solution to a structural frame problem, and iterative solutions 130 are generated sequentially by the convergent search algorithm when starting with a second input frame 131 as the initial solution to the problem.” – The designs include those for 3D objects, such as frames.)
NB teaches that 3D models are optimized for physical characteristics, including weight and cost, based on design constraints, such as stress parameters (NB [0016] “Multi-modal objective function 100 quantifiably expresses a value of a physical or other characteristic 110 of a design solution to a particular problem, such as weight, cost, peak temperature achieved during operation, etc., with respect to one or more optimization parameters 111 (only a single parameter is shown in FIG. 1). “ [0043] “In some embodiments, stress constraints can be implemented as follows. Since the trusses are composed of a physical material that can experience failure or breakage, the trusses cannot sustain arbitrarily large tensile and compressive stresses. Since stress is defined as force per unit area, the stress constraints can include user-prescribed thresholds derived from material maximum yields and safety factors. Any suitable constraint formulation implementing the above stress constraints can then be employed in convergent search algorithm 203.”), but does not appear to explicitly teach, but NB in view of Mahalik teaches:
iteratively modify a generatively designed three dimensional shape of the modeled object in the design space in accordance with the one or more design criteria, the one or more in-use load cases for the physical structure, and the critical fatigue crack length for the material, wherein the one or more data processing apparatus are configured to run the instructions of the computer aided design program to iteratively modify the generatively designed three dimensional shape of the modeled object; […] by being configured to run the instructions of the computer aided design program to enforce a design criterion that limits a minimum critical fatigue crack length for the material; and (Mahalik Page 3, 2.1 Topology Optimisation of the MLG Fitting “The process of optimisation is the work that was done in obtaining the optimum solution with the ultimate end goal that the design objective and constraints are met. Structural optimisation targeted the minimisation of constraints. The reduction on the MLG fitting’s weight is desired due to stress and deflection constraints. The weight decrease of the fitting is beneficial in terms of the manufacturing and the operating cost. Lighter weight may lead into less material needed to create the fitting and thus may deduct the manufacturing cost. The general expression of structural optimisation is shown in Eq. 1 (Larsson, 2016), where x is design variable(s) (i.e. MLG fitting), f (x) is the optimisation’s objective (i.e. weight reduction), ) g(x) is the inequality constraints, and h(x) is the equality constraints.” Page 8, First Paragraph “Common crack detection methods in the industry is able to distinguish a crack with a size of four millimetres at the smallest. By considering that, hence following the expression in Eq. 4, the inspection interval for the topology optimised fitting is tabulated as in Tab. 5.
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Where I is the inspection interval in cycles, acrit is the critical crack length at fracture in millimetres, and adet is the detectable crack length measured by the device in millimetres.“ – This teaches that critical crack size is used as an input to determine optimal topologies.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the generative design of NB by the stress considerations for optimizing weight and cost of generative designs in Mahalik because the person of ordinary skill in the art would be motivated by the expressed potential aim of NB to minimize/optimize weight and cost of the design generated by the generative design process to look to Mahalik that teaches the accounting for stress related quantities including critical crack size in a manner that effectively reduces weight and cost. (NB [0016] “Multi-modal objective function 100 quantifiably expresses a value of a physical or other characteristic 110 of a design solution to a particular problem, such as weight, cost, peak temperature achieved during operation, etc., with respect to one or more optimization parameters 111 (only a single parameter is shown in FIG. 1). “ [0043] “In some embodiments, stress constraints can be implemented as follows. Since the trusses are composed of a physical material that can experience failure or breakage, the trusses cannot sustain arbitrarily large tensile and compressive stresses. Since stress is defined as force per unit area, the stress constraints can include user-prescribed thresholds derived from material maximum yields and safety factors. Any suitable constraint formulation implementing the above stress constraints can then be employed in convergent search algorithm 203.”; Mahalik Abstract “Here, a combination of the short- and long-range flights for the aircraft was used to construct the load spectrum utilised in fitting’s crack growth examination. It is expected that the implementation of both the topology optimisation and the damage tolerance analysis on the fitting could significantly reduce the weight of the fitting while maintaining the compliance to the required static strength and damage tolerance aspects as stated on the Federal Acquisition Regulation (FAR) 23.”)
NB teaches that 3D models are optimized for physical characteristics, including weight and cost, (NB [0016] “Multi-modal objective function 100 quantifiably expresses a value of a physical or other characteristic 110 of a design solution to a particular problem, such as weight, cost, peak temperature achieved during operation, etc., with respect to one or more optimization parameters 111 (only a single parameter is shown in FIG. 1). “ [0043] “In some embodiments, stress constraints can be implemented as follows. Since the trusses are composed of a physical material that can experience failure or breakage, the trusses cannot sustain arbitrarily large tensile and compressive stresses. Since stress is defined as force per unit area, the stress constraints can include user-prescribed thresholds derived from material maximum yields and safety factors. Any suitable constraint formulation implementing the above stress constraints can then be employed in convergent search algorithm 203.”), but does not appear to explicitly teach, but NB in view of Li teaches:
iteratively modify a generatively designed three dimensional shape of the modeled object in the design space in accordance with the one or more design criteria, the one or more in-use load cases for the physical structure, and the critical fatigue crack length for the material, wherein the one or more data processing apparatus are configured to run the instructions of the computer aided design program to iteratively modify the generatively designed three dimensional shape of the modeled object; […] by being configured to run the instructions of the computer aided design program to enforce a design criterion that limits a minimum thickness the generatively designed three dimensional shape of the modeled object, the minimum thickness being based on the critical fatigue crack length for the material; and (Li Abstract “In this paper, a new layout free from the existing structure for indenter with lighter weight and good performance is presented. The design procedure includes two steps: Topology optimization and thickness optimization. In topology optimization process, a creative layout of indenter is obtained using Solid isotropic microstructures with penalization (SIMP) method. In thickness optimization process, based on topology optimization results, a design review was conducted, and a revised model was created which addresses all structural and manufacturability concerns. Shape and size optimization was then performed in the detailed design stage to further minimize the mass while meeting the stiffness and stress targets. Finally, an optimized model for the indenter is obtained. From the finite element analysis, it can be seen that there is a 31.8 % reduction in total mass while the performance increased compared with the original model.” – The Li reference teaches using thickness optimization to minimize weight/size of the 3D object while accounting for stress factors, as an element of generative design.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the optimization of the physical parameters of an object for weight or cost of NB by the specific aim of minimizing thickness of the generated object of Li because the person of ordinary skill in the art would be motivated by the explicit aim of NB to optimize the generation of the generated object for weight or cost while accounting for stress to look to Li, which optimizes thickness to reduce mass and cost while accounting for stress in a way that significantly reduces total mass and increases performance. (NB [0016] “Multi-modal objective function 100 quantifiably expresses a value of a physical or other characteristic 110 of a design solution to a particular problem, such as weight, cost, peak temperature achieved during operation, etc., with respect to one or more optimization parameters 111 (only a single parameter is shown in FIG. 1). “ [0043] “In some embodiments, stress constraints can be implemented as follows. Since the trusses are composed of a physical material that can experience failure or breakage, the trusses cannot sustain arbitrarily large tensile and compressive stresses. Since stress is defined as force per unit area, the stress constraints can include user-prescribed thresholds derived from material maximum yields and safety factors. Any suitable constraint formulation implementing the above stress constraints can then be employed in convergent search algorithm 203.”; Li Abstract “In this paper, a new layout free from the existing structure for indenter with lighter weight and good performance is presented. The design procedure includes two steps: Topology optimization and thickness optimization. In topology optimization process, a creative layout of indenter is obtained using Solid isotropic microstructures with penalization (SIMP) method. In thickness optimization process, based on topology optimization results, a design review was conducted, and a revised model was created which addresses all structural and manufacturability concerns. Shape and size optimization was then performed in the detailed design stage to further minimize the mass while meeting the stiffness and stress targets. Finally, an optimized model for the indenter is obtained. From the finite element analysis, it can be seen that there is a 31.8 % reduction in total mass while the performance increased compared with the original model.”)
Regarding claim 1, claim 1 recites the method that the system of claim 16 is configured to effectuate, so claim 1 is rejected for at least the same reasons as claim 16.
Regarding claim 18, claim 18 recites a CRM that is an embodiment of the CRM recited in claim 16, so claim 18 is rejected for at least the same reasons as claim 16.
Claim 5
Regarding claim 5, NB in view of Mahalik and Li teaches the features of claim 1 and further teaches:
wherein the enforcing uses a measure of thickness for the generatively designed three dimensional shape of the modeled object that is a combination of at least two distinct thickness measures. (Li Page 220, 4.2 Thickness optimization of indenter “Design variables for thickness optimization are shown in Fig. 15. The post-processed indenter in this figure is reconstructed by using 8 plate members, each of which has its corresponding thickness design variable. Considering the manufacturability, the optimized thicknesses of the plates should be available and mass produced [25].” See Also Fig. 15 – The plates that comprise the object have different thicknesses.)
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Claim 9
Regarding claim 9, NB in view of Mahalik and Li teach the features of claim 1 and further teach:
wherein obtaining the critical fatigue crack length for the material comprises: obtaining one or more specifications of the material from which the physical structure will be manufactured; and (Mahalik Page 2, Second Paragraph “The fitting uses AISI 4340, in which considered as a high-strength low alloy steel material (Wanhill et al., 2010). The physical and the material properties of the original MLG fitting is given in Tab. 1.” See Also Table 1 on Page 2 – One or more properties are provided for AISI 4340 for calculation of the critical crack length.)
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calculating the critical fatigue crack length for the material from information in the one or more specifications, the information comprising a modulus of the material's fatigue crack growth curve. (Mahalik Page 7, Second Paragraph “Cycle-by-cycle, the fitting was analysed using the Constant Amplitude (CA) loading condition as one of the methods to complete the DTA. The approach uses the same load magnitude in each cycle up until the fracture occurred. Thus, the utilisation of Eq. 3 along with the application of CA loading produced the information in Tab. 4 that gives the details about the crack growth and residual strength of both the original and the optimised fitting when it failed.” See Also Table 1 on Page 2 and Table 4 on Page 7 – The material parameters values include Young’s Modulus of the material, and it generates the Critical Crack Length based thereon.)
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Claims 15, 17, and 19: NB, Mahalik, Li, and Razzell
Claim(s) 1, 5, 9, 16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0155966 A1 to Nourbakhsh et al. (NB) in view of NPL: “Topology and Damage Tolerance Optimisation of an Island-Hopping Aircraft MLG Fitting using Generative Design” by Mahalik (Mahalik), NPL: “Topology and thickness optimization of an indenter under stress and stiffness constraints” by Li et al. (Li), and US 2020/0265122 A1 to Razzell et al. (Razzell).
Claims 15, 17, and 19
Regarding claim 15, NB in view of Mahalik and Li teaches the features of claim 1. NB teaches that parts are manufactured from the generated designs (NB [0004] “Because of the large number of nodes included in the optimally light-weight structure, however, the structure can be highly complex, even after applying an edge-pruning strategy during optimization. As a result, fabricating such structures can be complex or even impossible, unless additional supplemental manufacturing operations are performed. Thus, in certain cases, conventional generative design approaches generate designs for strong and light-weight structural frames that are difficult and expensive to manufacture.”), but NB in view of Mahalik and Li fails to teach, but NB in view of Mahalik, Li, and Razzell teaches:
wherein the one or more computer-controlled manufacturing systems comprise an additive manufacturing machine, and the operations comprise: generating toolpath specifications for the additive manufacturing machine from the three dimensional model; and manufacturing the physical structure corresponding to the object with the additive manufacturing machine using the toolpath specifications. (Razzell [0012] “Particular embodiments of the subject matter described in this specification can be implemented to realize one or more of the following advantages. Downstream manufacturing processes can be considered earlier in the part design process. A user can be enabled to produce toolpaths (either manually or automatically) on initial geometry of a model before running a generative design engine, and from these toolpaths (or from separate user input) the manufacturing forces to be imparted during subtractive manufacturing can be defined in a force model. The force model can be used in a generative design process to account for the subtractive manufacturing forces at the part design stage. Moreover, the machine tool moves (corresponding to the toolpaths used in the subtractive manufacturing) can be used to constrain where the generative geometry is allowed to be created, thus ensuring that toolpaths will not collide with generated geometry during machining.” [0039] “In any case, the CAD program(s) 116 can provide a document 160 (having toolpath specifications of an appropriate format) to the AM machine 170 to produce the physical structure 180. The AM machine 170 can employ one or more additive manufacturing techniques, such as granular techniques (e.g., Powder Bed Fusion (PBF), Selective Laser Sintering (SLS) and Direct Metal Laser Sintering (DMLS)), extrusion techniques (e.g., Fused Deposition Modelling (FDM), which can include metals deposition AM). In some cases, the AM machine 170 builds the physical structure 180 directly, and in some cases, the AM machine 170 builds a mold for use in casting or forging the physical structure 180.” – This teaches using AM to manufacture the 3D model based on generated toolpath specifications determined from the generated 3D model.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the generative design for the purpose of manufacture of BN by the generative model-based manufacturing methods of Razzell because the person of ordinary skill in the art would be motivated by the express aim of BN to optimize manufacturability and cost to look to Razzell, which generatively designs parts that are more readily manufacturable and can save costs during manufacturing. (BN [0007] “At least one advantage of the disclosed techniques is that a structural frame produced via a generative design process can have significantly fewer nodes than the frames used by a convergent algorithm included in the process. Consequently, candidate frames input to the convergent algorithm can have a large number of nodes, while the structural frame output by the convergent algorithm can have fewer nodes, and therefore be less complex in appearance and easier to fabricate. A further advantage is that the structural frame so produced is more likely than a structural frame generated with conventional generative design techniques to approximate a global minimum of the objective function used to optimize the frame design. Thus, for a specific input frame that is optimized for minimal weight, a structural frame produced by the disclosed techniques will be lighter-weight and/or can have fewer nodes and beams than a structural frame generated with conventional generative design techniques. Yet another advantage is that the divergent search can eliminate prospective solutions that are not manufacturable and/or violate certain feasibility conditions”; Razzell [0013] “In the case of additively manufactured parts, such parts will often require some amount of subtractive manufacturing (e.g., finishing operations in a milling machine) and generatively design parts will likely be an important component of additive manufacturing processes. By taking the machining forces into account during the generative design stage of part modelling, the downstream manufacturing processes can be facilitated. This can result in generatively designed parts that are more readily manufacturable and can save costs”)
Regarding claims 17 and 19, claims 17 and 19 recite substantially the same features as claim 15 and are rejected for at least the same reasons.
Claims Allowable Over Art
Claims 2-4, 6-8, and 10-14 are allowable over prior art for the following reasons.
Claims 2-4
Claim 2 recites:
determining an expected number of loading cycles for each of the at least one of the one or more in-use load cases for the physical structure using the maximized stress or strain element and the data relating fatigue strength to loading cycles,
redefining a fatigue safety factor inequality constraint for the modeled object based on a damage fraction calculated from the required number of loading cycles for the modeled object and the expected number of loading cycles for each of the at least one of the one or more in-use load cases for the physical structure, and
computing shape change velocities for an implicit surface in a level-set representation of the three dimensional shape in accordance with at least the fatigue safety factor inequality constraint.
The Bandara reference teaches determining shape change velocities, also known as advective velocities, for a level-set representation in generative design, teaches that a safety factor can be a target of the design (e.g., by user input), and also teaches that different load cases are considered, but it fails to teach the determination of an expected number of load cycles using a maximized stress or strain unit, redefining a safety factor, redefining a safety factor constraint, redefining a safety factor inequality constraint, redefining an inequality safety factor constraint based on a damage fraction calculated from a required number of loading cycles and an expected number of loading cycles, redefining an inequality safety factor constraint based on a damage fraction calculated from a required number of loading cycles and an expected number of loading cycles for each load case, or computing shape change velocities/advective velocities in accordance with a fatigue safety factor inequality constraint.
Similarly, the Siemens reference of record teaches using the safety factor as a consideration or constraint input by the user, but it does not provide dynamicity in the safety factor constraint required by the claim or the modification of the advective velocities to accord with a factor of safety constraint.
Similarly, the Yu reference of record teaches using factors of safety as constraints or design inputs but falls to teach the dynamic adjustment of the constraints associated with the factor of safety or that advective velocities are determined based thereon.
Similarly, the Cramer reference of record teaches using factors of safety as constraints or design inputs but falls to teach the dynamic adjustment of the constraints associated with the factor of safety or that advective velocities are determined based thereon.
The NB reference teaches that load factors and cases are considerations in the generative design and simulating the load cases but fails to teach the rest of the cited features.
Mahalik teaches using critical crack length, which relates fatigue strength to loading cycles, but fails to teach the rest of the features.
The Grolier, Cyprien, Ivanov, and Zhang references of record teaches the generation of safety factors that relate fatigue strength to loading cycles and their calculation.
None of the references of record expressly teach that a safety factor inequality is redefined based on required and expected numbers of loading cycles for each load case and that shape change velocities for an implicit service in a level-set representation are computed in accordance with the redefined fatigue safety factor inequality without the use of a combination that would require impermissible hindsight. Accordingly, claim 2, and claims 3 and 4 that depend from claim 2, are allowable over the prior art.
Claims 6-8
Claim 6 recites:
wherein the at least two distinct thickness measures comprise (i) a first distance measure being a length within the modeled object of a ray cast in a negative normal direction from a surface point of the modeled object, and (ii) a second distance measure being a diameter of a largest sphere that touches the surface point of the modeled object and fits inside the modeled object as determined by checking discrete sampling locations defined on the sphere's surface.
The Bandara reference teaches using a minimum thickness as a parameter and performing the generative design to optimize the design or size, but does not specify the distinct thickness measures as recited in claim 6.
The NB reference teaches optimizing overall cost and weight, which would be affected by the different thicknesses of the design, but it does not teach optimizing based on the specific thicknesses recited in claim 6.
The Li, Klarbring, and Shobeiri references of record teaches optimization for thickness of design objects but also does no specify optimizing the design for the specific thicknesses described in claim 6.
None of the cited references teach that the model is optimized for both a thickness representing a length within the modeled object of a ray cast in a negative normal direction from a surface point of the modeled object and a thickness representing a diameter of a largest sphere that touches the surface point of the modeled object and fits inside the modeled object by checking discrete sampling locations defined on the sphere’s surface, without the use of a combination that would require impermissible hindsight. Accordingly, claim 6, and claims 7 and 8 that depend from claim 6, are allowable over the prior art.
Claims 10-14
Claim 10 recites:
wherein the one or more in-use load cases for the physical structure comprise two or more in-use load cases for the physical structure, the one or more design criteria comprise a required number of loading cycles for the modeled object for each of the two or more in-use load cases for the physical structure, determining the expected number of loading cycles comprises determining a separate number of expected loading cycles for each of multiple points on the implicit surface for each of the two or more in-use load cases, and redefining the fatigue safety factor inequality constraint comprises:
summing, for each of the multiple points, load-specific damage fractions corresponding to the two or more in-use load cases, wherein each load-specific damage fraction comprises an expected number of loading cycles, for one of the multiple points and one of the in-use load cases, divided by the required number of loading cycles for the one of the in-use load cases, to produce a sum of the load-specific damage fractions for each of the multiple points;
inverting each of the sums of the load-specific damage fractions; and
using a minimum value of the inverted sums of the load-specific damage fractions to redefine the fatigue safety factor inequality constraint for the modeled object.
The Bandara reference (a prior application by the Applicant) teaches many features similar to the features of the claims including evaluating models for different load cases and accounting for damage constraints (e.g., brittle fraction resistance) [0221], but the Bandara reference is silent as to the determination of a require or expected number of load cycles, summing and inverting load specific damage fractions determined based on the determined load cycle quantities, or using a minimum of the inverted sums to redefine a factor of safety inequality constraint. Again, in this reference, factor of safety is a static input potentially used as a constraint.
Similarly, the Siemens reference fails to teach all of those features and treats factor of safety like a static input that is used as a constraint. It lacks the dynamicity of claim 6.
The NB reference of record teaches generative design and manufacturing of models based on in-use load cases and loading factors across the object but fails to teach the features lacking in the Bandara and Siemens references.
The Grolier reference of record teaches determining safety factors based on numbers of load cycles, based on parameter values determined from Wohler curves and Haigh diagrams, but it fails to teach and would require impermissible hindsight to arrive at the features of claim 10.
None of the references, in isolation or combination of record explicitly teach determining expected loading cycles for multiple points on an implicit surface, summing load-specific damage fractions corresponding to in-use load cases as recited, inverting the sums, and using a minimum value of the inverted sums to redefine the fatigue safety factor inequality constraint, without the use of impermissible hindsight. Accordingly, claim 10, and claims 11-14 that depend from claim 10, are allowable over the prior art.
Conclusion
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/J.M.W./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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