Skip to main content
SpringerLink
Log in

Toward the Rapid Design of Engineered Systems Through Deep Neural Networks

  • Conference paper
  • First Online:
Design Computing and Cognition '18 (DCC 2018)

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/HSDL/WAnet/releases/tag/v1.0-beta.

References

  1. Gero JS (1990) Design prototypes: a knowledge representation schema for design. AI Magaz 11(4):26–36

    Google Scholar 

  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/s0890060400000718

    Article  Google Scholar 

  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/10.1115/1.3593409

    Article  Google Scholar 

  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/10.1115/1.4003290

    Article  Google Scholar 

  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/10.1115/1.4032810

    Article  Google Scholar 

  6. Jain AK, Mao Jianchang, Mohiuddin KM (1996) Artificial neural networks: a tutorial. Computer 29(3):31–44. https://doi.org/10.1109/2.485891

    Article  Google Scholar 

  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/10.1145/3042064

    Article  Google Scholar 

  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_10

    Chapter  Google Scholar 

  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. 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. 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. 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. 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. Hinton GE (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647

    Article  MathSciNet  MATH  Google Scholar 

  15. Kingma DP, Welling M (2013) Auto-encoding variational Bayes, (Ml), pp 1–14. Retrieved from http://arxiv.org/abs/1312.6114

  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. 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. 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. 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/10.1115/1.4007304

    Article  Google Scholar 

  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/10.1115/1.4037309

    Article  Google Scholar 

  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_32

    Chapter  Google Scholar 

  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/10.1115/1.4037179

    Article  Google Scholar 

  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/s00170-011-3284-8

    Article  Google Scholar 

  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/s10845-012-0682-1

    Article  Google Scholar 

  25. Chowdhury S, Anand S (2017) Artificial neural network based geometric compensation for thermal deformation in additive manufacturing processes, pp 1–10

    Google Scholar 

  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. 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.2012.2193290

    Article  Google Scholar 

  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.2011.11.008

    Article  Google Scholar 

  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.2011.10.002

    Article  Google Scholar 

  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. 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. 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.2015.06.005

    Article  Google Scholar 

  33. Chollet F (2015) Keras. GitHub

    Google Scholar 

  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. 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, France

    Google Scholar 

  36. Tieleman T, Hinton G (2012) Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude

    Google Scholar 

  37. Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79–86. https://doi.org/10.1214/aoms/1177729694

    Article  MathSciNet  MATH  Google Scholar 

  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. 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/10.1109/72.279181

    Article  Google Scholar 

  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. Ba J, Mnih V, Kavukcuoglu K (2014) Multiple object recognition with visual attention. Retrieved from http://arxiv.org/abs/1412.7755

  42. Mnih V, Heess N, Graves A, Kavukcuoglu K (2014) Recurrent models of visual attention. doi: 1406.6247

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. The Pennsylvania State University, University Park, PA, USA

    Christopher McComb

Authors
  1. Christopher McComb
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christopher McComb .

Editor information

Editors and Affiliations

  1. Department of Computer Science and School of Architecture, University of North Carolina at Charlotte, Charlotte, NC, USA

    John S. Gero

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

McComb, C. (2019). Toward the Rapid Design of Engineered Systems Through Deep Neural Networks. In: Gero, J. (eds) Design Computing and Cognition '18. DCC 2018. Springer, Cham. https://doi.org/10.1007/978-3-030-05363-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05363-5_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05362-8

  • Online ISBN: 978-3-030-05363-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation