Advertisement

Toward the Rapid Design of Engineered Systems Through Deep Neural Networks

  • Christopher McCombEmail author
Conference paper

Abstract

The design of a system commits a significant portion of the final cost of that system. Many computational approaches have been developed to assist designers in the analysis (e.g., computational fluid dynamics) and synthesis (e.g., topology optimization) of engineered systems.

Notes

Acknowledgements

This material is based upon work supported by the United States Air Force Office of Scientific Research through grants FA9550-16-1-0049 and the Defense Advanced Research Projects Agency through cooperative agreement No. N66001-17-1-4064. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors. We also gratefully acknowledge the support of the NVIDIA Corporation for the donation of the Quadro P5000 GPU used in this work.

References

  1. 1.
    Gero JS (1990) Design prototypes: a knowledge representation schema for design. AI Magaz 11(4):26–36Google Scholar
  2. 2.
    Bhatta SR, Goel AK (1994) Discovery of physical principles from design experiences. Int J AI EDAM (AI for Eng Des Anal Manufact) (July 1992):1–22.  https://doi.org/10.1017/s0890060400000718CrossRefGoogle Scholar
  3. 3.
    Chakrabarti A, Shea K, Stone R, Cagan J, Campbell MI, Hernandez NV, Wood KL (2011) Computer-based design synthesis research: an overview. J Comput Inf Sci Eng 11(2):21003.  https://doi.org/10.1115/1.3593409CrossRefGoogle Scholar
  4. 4.
    Landry LH, Cagan J (2011) Protocol-based multi-agent systems: examining the effect of diversity, dynamism, and cooperation in heuristic optimization approaches. J Mech Des 133(2):21001.  https://doi.org/10.1115/1.4003290CrossRefGoogle Scholar
  5. 5.
    McComb C, Cagan J, Kotovsky K (2016) Drawing inspiration from human design teams for better search and optimization: the heterogeneous simulated annealing teams algorithm. J Mech Des 138(4):44501.  https://doi.org/10.1115/1.4032810CrossRefGoogle Scholar
  6. 6.
    Jain AK, Mao Jianchang, Mohiuddin KM (1996) Artificial neural networks: a tutorial. Computer 29(3):31–44.  https://doi.org/10.1109/2.485891CrossRefGoogle Scholar
  7. 7.
    Ioannidou A, Chatzilari E, Nikolopoulos S, Kompatsiaris I (2017) Deep learning advances in computer vision with 3D data. ACM Comput Surv 50(2):1–38.  https://doi.org/10.1145/3042064CrossRefGoogle Scholar
  8. 8.
    Xiang Y, Kim W, Chen W, Ji J, Choy C, Su H, … Savarese S (2016) Objectnet3D: a large scale database for 3D object recognition. Lecture Notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). 9912 LNCS: 160–176.  https://doi.org/10.1007/978-3-319-46484-8_10CrossRefGoogle Scholar
  9. 9.
    Chang AX, Funkhouser T, Guibas L, Hanrahan P, Huang Q, Li Z, … Yu F (2015) ShapeNet: an information-rich 3D model repository.  https://doi.org/10.1145/3005274.3005291
  10. 10.
    Maturana D, Scherer S (2015) VoxNet: a 3D convolutional neural network for real-time object recognition. In: 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS. IEEE), pp 922–928.  https://doi.org/10.1109/iros.2015.7353481
  11. 11.
    Garcia-Garcia A, Gomez-Donoso F, Garcia-Rodriguez J, Orts-Escolano S, Cazorla M, Azorin-Lopez J (2016) PointNet: a 3D convolutional neural network for real-time object class recognition. In: 2016 International joint conference on neural networks (IJCNN). IEEE, pp 1578–1584.  https://doi.org/10.1109/ijcnn.2016.7727386
  12. 12.
    Achlioptas P, Diamanti O, Mitliagkas I, Guibas L (2017) Representation learning and adversarial generation of 3D point clouds, pp 1–20. Retrieved from http://arxiv.org/abs/1707.02392
  13. 13.
    Wu J, Zhang C, Xue T, Freeman WT, Tenenbaum JB (2016) Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. (Nips). Retrieved from http://arxiv.org/abs/1610.07584
  14. 14.
    Hinton GE (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507.  https://doi.org/10.1126/science.1127647MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Kingma DP, Welling M (2013) Auto-encoding variational Bayes, (Ml), pp 1–14. Retrieved from http://arxiv.org/abs/1312.6114
  16. 16.
    Rezende DJ, Mohamed S, Wierstra D (2014) Stochastic backpropagation and approximate inference in deep generative models. Retrieved from http://arxiv.org/abs/1401.4082
  17. 17.
    Salimans T, Kingma DP, Welling M (2014) Markov chain monte carlo and variational inference: bridging the gap. Retrieved from http://arxiv.org/abs/1410.6460
  18. 18.
    Kingma DP, Rezende DJ, Mohamed S, Welling M (2014) Semi-supervised learning with deep generative models. Retrieved from http://arxiv.org/abs/1406.5298
  19. 19.
    Tseng I, Cagan J, Kotovsky K (2012) Concurrent optimization of computationally learned stylistic form and functional goals. J Mech Des 134(11):111006-1–111006-11.  https://doi.org/10.1115/1.4007304CrossRefGoogle Scholar
  20. 20.
    Dering M, Tucker C (2017) A convolutional neural network model for predicting a product’s function, given its form. J Mech Des 139(11):1–14.  https://doi.org/10.1115/1.4037309CrossRefGoogle Scholar
  21. 21.
    Grace K, Maher M Lou, Wilson D, Najjar N (2017) Personalised specific curiosity for computational design systems. In: Design computing and cognition ’16. Springer International Publishing, Cham, pp 593–610.  https://doi.org/10.1007/978-3-319-44989-0_32CrossRefGoogle Scholar
  22. 22.
    Patel A, Andrews P, Summers JD, Harrison E, Schulte J, Laine Mears M (2017) Evaluating the use of artificial neural networks and graph complexity to predict automotive assembly quality defects. J Comput Inf Sci Eng 17(3):31017.  https://doi.org/10.1115/1.4037179CrossRefGoogle Scholar
  23. 23.
    Di Angelo L, Di Stefano P (2011) A neural network-based build time estimator for layer manufactured objects. Int J Adv Manuf Technol 57(1–4):215–224.  https://doi.org/10.1007/s00170-011-3284-8CrossRefGoogle Scholar
  24. 24.
    Xiong J, Zhang G, Hu J, Wu L (2014) Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. J Intell Manuf 25(1):157–163.  https://doi.org/10.1007/s10845-012-0682-1CrossRefGoogle Scholar
  25. 25.
    Chowdhury S, Anand S (2017) Artificial neural network based geometric compensation for thermal deformation in additive manufacturing processes, pp 1–10Google Scholar
  26. 26.
    Mørk G, Barstow S, Kabuth A, Pontes MT (2010) Assessing the global wave energy potential. In: 29th international conference on ocean, offshore and arctic engineering, vol 3. ASME, pp 447–454.  https://doi.org/10.1115/omae2010-20473
  27. 27.
    Czech B, Bauer P (2012) Wave energy converter concepts: design challenges and classification. IEEE Ind Electron Mag 6(2):4–16.  https://doi.org/10.1109/MIE.2012.2193290CrossRefGoogle Scholar
  28. 28.
    Li Y, Yu Y-H (2012) A synthesis of numerical methods for modeling wave energy converter-point absorbers. Renew Sustain Energy Rev 16(6):4352–4364.  https://doi.org/10.1016/j.rser.2011.11.008CrossRefGoogle Scholar
  29. 29.
    Babarit A, Hals J, Muliawan MJ, Kurniawan A, Moan T, Krokstad J (2012) Numerical benchmarking study of a selection of wave energy converters. Renew Energy 41:44–63.  https://doi.org/10.1016/j.renene.2011.10.002CrossRefGoogle Scholar
  30. 30.
    McComb C, Lawson M, Yu Y-H (2013) Combining multi-body dynamics and potential flow simulation methods to model a wave energy converter. In: 1st marine energy technology symposium.  https://doi.org/10.13140/rg.2.1.3817.3285
  31. 31.
    Ruehl K, Michelen C, Kanner S, Lawson M, Yu Y (2014) Preliminary verification and validation of WEC-Sim, an open-source wave energy converter design tool. In: Volume 9B: ocean renewable energy. V09BT09A040ASME.  https://doi.org/10.1115/omae2014-24312
  32. 32.
    McComb C, Cagan J, Kotovsky K (2015) Lifting the Veil: drawing insights about design teams from a cognitively-inspired computational model. Des Stud 40:119–142.  https://doi.org/10.1016/j.destud.2015.06.005CrossRefGoogle Scholar
  33. 33.
    Chollet F (2015) Keras. GitHubGoogle Scholar
  34. 34.
    Al-Rfou R, Alain G, Almahairi A, Angermueller C, Bahdanau D, Ballas N, … Zhang Y (2016) Theano: a python framework for fast computation of mathematical expressions. arXiv e-prints. abs/1605.0. Retrieved from http://arxiv.org/abs/1605.02688
  35. 35.
    Babarit A, Delhommeau G (2015) Theoretical and numerical aspects of the open source BEM solver NEMOH. In: 11th European wave and tidal energy conference (EWTEC2015), Nantes, FranceGoogle Scholar
  36. 36.
    Tieleman T, Hinton G (2012) Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitudeGoogle Scholar
  37. 37.
    Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79–86.  https://doi.org/10.1214/aoms/1177729694MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, … Bengio Y (2014) Generative adversarial networks. Retrieved from http://arxiv.org/abs/1406.2661
  39. 39.
    Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhutdinov R, … Bengio Y (2015) Show, attend and tell: neural image caption generation with visual attention.  https://doi.org/10.1109/72.279181CrossRefGoogle Scholar
  40. 40.
    Chen K, Wang J, Chen L-C, Gao H, Xu W, Nevatia R (2015) ABC-CNN: an attention based convolutional neural network for visual question answering. Retrieved from http://arxiv.org/abs/1511.05960
  41. 41.
    Ba J, Mnih V, Kavukcuoglu K (2014) Multiple object recognition with visual attention. Retrieved from http://arxiv.org/abs/1412.7755
  42. 42.
    Mnih V, Heess N, Graves A, Kavukcuoglu K (2014) Recurrent models of visual attention. doi: 1406.6247Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.The Pennsylvania State UniversityUniversity ParkUSA

Personalised recommendations