Framework for rapid additive design with generative techniques
According to some embodiments, a system may include a design experience data store containing electronic records associated with prior industrial asset item designs. A deep learning model platform, coupled to the design experience data store, may include a communication port to receive constraint and load information from a designer device. The deep learning platform may further include a computer processor adapted to automatically and generatively create boundaries and geometries, using a deep learning model associated with an additive manufacturing process, for an industrial asset item based on the prior industrial asset item designs and the received constraint and load information. According to some embodiments, the deep learning model computer processor is further to receive design adjustments from the designer device. The received design adjustments might be for example, used to execute an optimization process and/or be fed back to continually re-train the deep learning model.
1. A system to facilitate creation of an industrial asset item, comprising:
a design experience data store containing electronic records associated with prior industrial asset item designs; and
a deep learning model platform, coupled to the design experience data store, including:
a communication port to receive constraint and load information from a designer device, and
a deep learning model computer processor coupled to the communication port and adapted to automatically and generatively create boundaries and geometries, using a deep learning model associated with an additive manufacturing process, for the industrial asset item based on the prior industrial asset item designs and the received constraint and load information, wherein the automatically and generatively created boundaries and geometries are associated with a generative model for the industrial asset item.
2. The system of claim 1 , further comprising:
a deep learning model creation platform to receive the electronic records associated with the prior industrial asset item designs and to automatically create the deep learning model using three-dimensional images of multiple industrial asset item designs.
3. The system of claim 1 , wherein the deep learning model computer processor is further to receive design adjustments from the designer device and, based on the received design adjustments, execute an optimization process.
4. The system of claim 3 , wherein the received design adjustments are fed back to continually re-train the deep learning model.
5. The system of claim 1 , further comprising:
a physics model platform to receive the boundaries and geometries and execute a validation process on at least one intermediate industrial asset design.
6. The system of claim 5 , wherein results of the validation process are fed back to continually re-train the deep learning model.
7. The system of claim 1 , further comprising:
a repository storing a plurality of searchable generative models.
8. The system of claim 7 , wherein a designer can perform an automated search of the repository.
9. The system of claim 8 , wherein results of said search comprise generative model recommendations ranked by probabilities.
10. The system of claim 1 , wherein the generative model is created by a Generative Adversarial Network (“GAN”) based on at least one contextual model or at least one image model.
11. The system of claim 1 , wherein the generative model is associated with at least one of: (i) a Gaussian mixture model, (ii) a hidden Markov model, (iii) probabilistic context-free grammar, (iv) a naive Bayes model, (v) averaged one-dependence estimators, (vi) latent Dirichlet allocation, (vii) a restricted Boltzmann machine, and (xiii) a generative adversarial network.
12. The system of claim 1 , wherein the generative model is further created based on natural language processing of at least one of: (i) text, (ii) reports, (iii) design handbooks, and (iv) design practices.
13. The system of claim 12 , wherein the natural language processing creates a contextual model.
14. The system of claim 1 , wherein the generative model is further created based on classification and feature identification performed by a deep neural network in connection with at least one of: (i) an image, (ii) a manufactured design, (iii) a cross-section, (iv) a binary Computer Aided Design (“CAD”) file, (v) a geometry file, and (vi) analysis results.
15. The system of claim 14 , wherein the classification and feature identification creates an image model.
16. The system of claim 1 , further comprising:
an additive manufacturing printer, to receive a final industrial asset design based on the automatically and generatively created boundaries and geometries, wherein the printer associated with at least one of: (i) three-dimensional printing, (ii) vat photopolymerization, (iii) material jetting, (iv) binder jetting, (vi) material extrusion, (vii) powder bed fusion, (viii) sheet lamination, and (ix) directed energy deposition.
17. A computer-implemented method to facilitate creation of an industrial asset item, comprising:
receiving, at a search platform from a designer device, a search of a generative model repository;
executing a search of the generative model repository to identify a starting seed shape for the industrial asset item;
receiving, at a deep learning model platform, the starting seed shape along with constraint and load information from the designer device;
automatically and generatively creating boundaries and geometries, by the deep learning model platform using a deep learning model associated with an additive manufacturing process, for the industrial asset item based on the prior industrial asset item designs and the received constraint and load information;
receiving design adjustments at the deep learning model platform from the designer device;
executing an optimization process based on the received design adjustments, execute an optimization process;
executing, at a physics model platform, a validation process on at least one intermediate industrial asset design based on received the boundaries and geometries;
receiving, at an additive manufacturing printer, a final industrial asset design based on the automatically and generatively created boundaries and geometries; and
creating, by the additive manufacturing printer, the industrial asset item.
18. The method of claim 17 , wherein the automatically and generatively created boundaries and geometries are associated with a generative model for the industrial asset item.
19. The method of claim 18 , wherein the generative model is created by a Generative Adversarial Network (“GAN”) based on at least one contextual model or at least one image model.
20. The method of claim 19 , wherein the generative model is associated with at least one of: (i) a Gaussian mixture model, (ii) a hidden Markov model, (iii) probabilistic context-free grammar, (iv) a naive Bayes model, (v) averaged one-dependence estimators, (vi) latent Dirichlet allocation, (vii) a restricted Boltzmann machine, and (xiii) a generative adversarial network.
21. The method of claim 20 , wherein the generative model is further created based on a contextual model produced via natural language processing of at least one of: (i) text, (ii) reports, (iii) design handbooks, and (iv) design practices.
22. The method of claim 20 , wherein the generative model is further created based on an image model produced via classification and feature identification performed by a deep neural network in connection with at least one of: (i) an image, (ii) a manufactured design, (iii) a cross-section, (iv) a binary Computer Aided Design (“CAD”) file, (v) a geometry file, and (vi) analysis results.