Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-24T03:41:21.606Z Has data issue: false hasContentIssue false
  • English
  • Français

MODELING A STRATEGIC HUMAN ENGINEERING DESIGN PROCESS: HUMAN-INSPIRED HEURISTIC GUIDANCE THROUGH LEARNED VISUAL DESIGN AGENTS

Part of: Design Support Tools

Published online by Cambridge University Press:  11 June 2020

L. Puentes ,
A. Raina ,
J. Cagan  and
C. McComb
L. Puentes
Affiliation:
The Pennsylvania State University, United States of America
A. Raina
Affiliation:
Carnegie Mellon University, United States of America
J. Cagan*
Affiliation:
Carnegie Mellon University, United States of America
C. McComb
Affiliation:
The Pennsylvania State University, United States of America
*
*[email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Human designers often work in a visual design space, projecting step-by-step design progression through evolving mental images. The strategic evolution of that design leverages heuristics based on experience and domain knowledge. The methodology presented in this paper brings together the visual nature of design problem solving and design heuristics in a deep learning computational agent framework that emulates and enables human-mirrored design. When applied to a truss design task, results demonstrate superior results to those of human designers who provided the initial data.

Keywords

artificial intelligence (AI)engineering designdesign automationdeep learningheuristics
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 355 - 364
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2020. Published by Cambridge University Press

References

Ahmed, S., Wallace, K.M. and Blessing, L.T.M. (2003), “Understanding the differences between how novice and experienced designers approach design tasks”, Research in Engineering Design, Vol. 14 No. 1, pp. 1–11. https://doi.org/10.1007/s00163-002-0023-zCrossRefGoogle Scholar
Athavankar, U.A. (1997), “Mental Imagery as a Design Tool”, Cybernetics and Systems, Taylor & Francis, Vol. 28 No. 1, pp. 25–42. https://doi.org/10.1080/019697297126236CrossRefGoogle Scholar
Brooks, R.A. (1991), “New Approaches to Robotics”, Science, Vol. 253 No. 5025, pp. 1227–1232.CrossRefGoogle ScholarPubMed
Campbell, M.I., Rai, R. and Kurtoglu, T. (2012), “A Stochastic Tree-Search Algorithm for Generative Grammars”, Journal of Computing and Information Science in Engineering, Vol. 12, pp. 1–11. https://doi.org/10.1115/1.4007153CrossRefGoogle Scholar
Chakrabarti, A. et al. (2011), “Computer-Based Design Synthesis Research: An Overview”, Journal of Computing and Information Science in Engineering, Vol. 11 No. 2, p. 021003. https://doi.org/10.1115/1.3593409CrossRefGoogle Scholar
Daly, S. and Christian, J.L. (2012), “Assessing design heuristics for idea generation in an introductory engineering course”, International Journal of Engineering Education, Vol. 28 No. 2, pp. 1–11.Google Scholar
Goldschmidt, G. (1992), “Serial Sketching: Visual Problem Solving in Designing”, Cybernetics and Systems, Taylor & Francis, Vol. 23 No. 2, pp. 191–219. https://doi.org/10.1080/01969729208927457CrossRefGoogle Scholar
Hinton, G.E. and Salakhutdinov, R.R. (2006), “Reducing the Dimensionality of Data with Neural Networks”, Science, Vol. 313 No. 5786, pp. 504–507. https://doi.org/10.1126/science.1127647CrossRefGoogle ScholarPubMed
Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P. (1983), “Optimization by Simulated Annealing”, Science, Vol. 220 No. 4598, pp. 671–680. https://doi.org/10.1126/science.220.4598.671CrossRefGoogle ScholarPubMed
Königseder, C. and Shea, K. (2014), “Systematic rule analysis of generative design grammars”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 28 No. 03, pp. 227–238.CrossRefGoogle Scholar
Königseder, C. and Shea, K. (2016), “Visualizing Relations between Grammar Rules, Objectives, and Search Space Exploration in Grammar-Based Computational Design Synthesis”, Journal of Mechanical Design, Vol. 138 No. 10, pp. 1–11. https://doi.org/10.1115/1.4034270CrossRefGoogle Scholar
Kramer, J. et al. (2014), “A case-study analysis of design heuristics in an upper-level cross-disciplinary design course”, ASEE Annual Conference and Exposition, Conference Proceedings.Google Scholar
Masci, J. et al. (2011), “Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction”, In: Honkela, T., Duch, W., Girolami, M. and Kaski, S. (Eds.), Artificial Neural Networks and Machine Learning -- ICANN 2011, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 52–59. https://doi.org/10.1007/978-3-642-21735-7_7CrossRefGoogle Scholar
McComb, C., Cagan, J. and Kotovsky, K. (2015), “Rolling with the punches: An examination of team performance in a design task subject to drastic changes”, Design Studies, Vol. 36 No. C, pp. 99–121. https://doi.org/10.1016/j.destud.2014.10.001CrossRefGoogle Scholar
McComb, C., Cagan, J. and Kotovsky, K. (2017a), “Capturing Human Sequence-Learning Abilities in Configuration Design Tasks Through Markov Chains”, Journal of Mechanical Design, ASME, Vol. 139 No. 9, pp. 91101–91112. https://doi.org/10.1115/1.4037185CrossRefGoogle Scholar
McComb, C., Cagan, J. and Kotovsky, K. (2017b), “Mining Process Heuristics from Designer Action Data via Hidden Markov Models”, Journal of Mechanical Design, Vol. 139 No. 11, p. 111412. https://doi.org/10.1115/1.4037308CrossRefGoogle Scholar
McComb, C., Cagan, J. and Kotovsky, K. (2017c), “Utilizing Markov Chains to Understand Operation Sequencing in Design Tasks”, Design Computing and Cognition, Vol. 16, pp. 401–418. https://doi.org/10.1007/978-3-319-44989-0_22CrossRefGoogle Scholar
McComb, C., Cagan, J. and Kotovsky, K. (2018), “Data on the design of truss structures by teams of engineering students”, Data in Brief, Vol. 18, pp. 160–163. https://doi.org/10.1016/j.dib.2018.02.078CrossRefGoogle ScholarPubMed
Miller, S.W., Simpson, T.W. and Yukish, M.A. (2015), “Design as a Sequential Decision Process: A Method for Reducing Design Set Space Using Models to Bound Objectives”. https://doi.org/10.1007/s00158-017-1756-7.CrossRefGoogle Scholar
Puentes, L., Cagan, J. and Mccomb, C. (2019), “Heuristic-Guided Solution Search through a Two-Tiered Design Grammar”, Journal of Computing and Information Science in Engineering, pp. 1–38. https://doi.org/10.1115/1.4044694Google Scholar
Raina, A., Cagan, J. and McComb, C. (2019), “Transferring Design Strategies From Human to Computer and Across Design Problems”, Journal of Mechanical Design, Vol. 141 No. 11. https://doi.org/10.1115/1.4044258CrossRefGoogle Scholar
Raina, A., McComb, C. and Cagan, J. (2019), “Learning to Design From Humans: Imitating Human Designers Through Deep Learning”, Journal of Mechanical Design, Vol. 141 No. 11, pp. 1–11. https://doi.org/10.1115/1.4044256CrossRefGoogle Scholar
Reddy, G. and Cagan, J. (1995), “An Improved Shape Annealing Algorithm For Truss Topology Generation”, Journal of Mechanical Design, Vol. 117 No. 2A, pp. 315–321. https://doi.org/10.1115/1.2826141CrossRefGoogle Scholar
Schmidt, L.C. and Cagan, J. (1997), “GGREADA: A graph grammar-based machine design algorithm”, Research in Engineering Design, Vol. 9 No. 4, pp. 195–213. https://doi.org/10.1007/BF01589682CrossRefGoogle Scholar
Shea, K. and Cagan, J. (1997), “Innovative dome design: Applying geodesic patterns with shape annealing”, Artificial Intelligence for Engineering, Design, Analysis and Manufacturing, Vol. 11 No. 05, pp. 379–394. https://doi.org/10.1017/S0890060400003310CrossRefGoogle Scholar
Stöckli, F. and Shea, K. (2017), “Automated Synthesis of Passive Dynamic Brachiating Robots Using a Simulation-Driven Graph Grammar Method”, Journal of Mechanical Design, Vol. 139 No. 9, p. 092301.CrossRefGoogle Scholar
Tai, K. and Akhtar, S. (2005), “Structural topology optimization using a genetic algorithm with a morphological geometric representation scheme”, Structural and Multidisciplinary Optimization, Vol. 30 No. 2, pp. 113–127. https://doi.org/10.1007/s00158-004-0504-yCrossRefGoogle Scholar
Yilmaz, S. and Seifert, C.M. (2011), “Creativity through design heuristics: A case study of expert product design”, Design Studies, Elsevier Ltd, Vol. 32 No. 4, pp. 384–415. https://doi.org/10.1016/j.destud.2011.01.003CrossRefGoogle Scholar
Yukish, M.A., Miller, S.W. and Simpson, T.W. (2015), “A Preliminary Model of Design as a Sequential Decision Process”, Procedia Computer Science, Elsevier, Vol. 44, pp. 174–183. https://doi.org/10.1016/j.procs.2015.03.039CrossRefGoogle Scholar