Hostname: page-component-cb9f654ff-d5ftd Total loading time: 0 Render date: 2025-08-26T05:47:21.108Z Has data issue: false hasContentIssue false
  • English
  • Français

Student vs Machine: Comparing Artificial Neural Network Predictions with Student Estimates of Market Price Using Function Structure Models

Published online by Cambridge University Press:  26 May 2022

A. R. Patel  and
J. D. Summers
A. R. Patel*
Affiliation:
The University of Texas at Dallas, United States of America
J. D. Summers
Affiliation:
The University of Texas at Dallas, United States of America
*
apurva.patel@utdallas.edu

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.

This paper investigates the use of ANNs to model human behaviour in design by comparing the predictive capability of ANNs and engineering students. Function structure models of 15 products are used as input for prediction. The type of information provided varied between topology and vocabulary. Analysis of prediction accuracy showed that ANNs perform comparably to students. However, students are more precise with their predictions. Finally, limitations and future work are discussed, with research questions presented for subsequent research.

Keywords

artificial intelligence (AI)functional modellingdesign education

Information

Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1669 - 1678
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), 2022.

References

Bengio, Y. and Grandvalet, Y. (2004), “No unbiased estimator of the variance of K-fold cross-validation”, Journal of Machine Learning Research, Vol. 5, pp. 1089–1105.Google Scholar
Dieter, G.E. and Schmidt, L.C. (2013), Engineering Design, 5th ed., McGraw-Hill, New York.Google Scholar
Dym, C.L. and Little, P. (2000), Engineering Design: A Project-Based Introduction, John Wiley & Sons, Inc., New York.Google Scholar
Fernández, M.G., Seepersad, C.C., Rosen, D.W., Allen, J.K. and Mistree, F. (2001), “Utility-based decision support for selection in engineering design”, Proceedings of the ASME Design Engineering Technical Conference, Vol. 2, pp. 887–899.Google Scholar
Geirhos, R., Janssen, D.H.J., Schütt, H.H., Rauber, J., Bethge, M. and Wichmann, F.A. (2017), “Comparing deep neural networks against humans: object recognition when the signal gets weaker”, available at: http://arxiv.org/abs/1706.06969.Google Scholar
Gill, A.S., Summers, J.D. and Turner, C.J. (2017), “Comparing function structures and pruned function structures for market price prediction: An approach to benchmarking representation inferencing value”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 31 No. 4, pp. 550–566.CrossRefGoogle Scholar
Hirtz, J., Stone, R.B., McAdams, D. a., Szykman, S. and Wood, K.L. (2002), “A functional basis for engineering design: reconciling and evolving previous efforts”, Research in Engineering Design, Vol. 13 No. 2, pp. 65–82.Google Scholar
Hoyle, C., Chen, W., Wang, N. and Gomez-Levi, G. (2011), “Understanding and modelling heterogeneity of human preferences for engineering design”, Journal of Engineering Design, Vol. 22 No. 8, pp. 583–601.CrossRefGoogle Scholar
Lecun, Y., Bengio, Y. and Hinton, G. (2015), “Deep learning”, Nature, Vol. 521 No. 7553, pp. 436–444.Google ScholarPubMed
Lee, B., Fillingim, K.B., Binder, W.R., Fu, K. and Paredis, C.J.J. (2017), “Design Heuristics: A Conceptual Framework and Preliminary Method for Extraction”, Volume 7: 29th International Conference on Design Theory and Methodology, American Society of Mechanical Engineers, pp. 1–10.CrossRefGoogle Scholar
Mathieson, J.L., Shanthakumar, A., Sen, C., Arlitt, R., Summers, J.D. and Stone, R. (2011), “Complexity as a surrogate mapping between function models and market value”, Proceedings of the ASME Design Engineering Technical Conference, Vol. 9, pp. 55–64.Google Scholar
Mathieson, J.L., Wallace, B.A. and Summers, J.D. (2013), “Assembly time modelling through connective complexity metrics”, International Journal of Computer Integrated Manufacturing, Taylor & Francis, Vol. 26 No. 10, pp. 955–967.Google Scholar
McComb, C., Cagan, J. and Kotovsky, K. (2017), “Capturing Human Sequence-Learning Abilities in Configuration Design Tasks Through Markov Chains”, Journal of Mechanical Design, Transactions of the ASME, Vol. 139 No. 9, pp. 1–12.CrossRefGoogle Scholar
Namouz, E.Z. and Summers, J.D. (2013), “Complexity Connectivity Metrics-Predicting Assembly Times with Abstract Assembly Models”, in Abramovici, M. and Stark, R. (Eds.), Smart Product Engineering, Springer Berlin Heidelberg, Bochum, Germany, pp. 77–786.Google Scholar
Pahl, G., Beitz, W., Blessing, L., Feldhusen, J., Grote, K.-H.H. and Wallace, K. (2013), Engineering Design: A Systematic Approach, edited by Second, 3rd ed., Vol. 11, Springer-Verlag London Limited, London.Google Scholar
Patel, A., Andrews, P., Summers, J.D., Harrison, E., Schulte, J. and Laine Mears, M. (2017), “Evaluating the Use of Artificial Neural Networks and Graph Complexity to Predict Automotive Assembly Quality Defects”, Journal of Computing and Information Science in Engineering, Vol. 17 No. 3, available at:10.1115/1.4037179.Google Scholar
Patel, A.R., Gill, A.S., Kramer, W.S., Summers, J.D. and Shuffler-Porter, M.L. (2016), “Graph Complexity Analysis of Function Models Expanded from Partially Completed Models”, in Linsey, J., Yang, M. and Nagai, Y. (Eds.), 4th International Conference on Design Creativity, Atlanta, GA.Google Scholar
Petiot, J.F. and Grognet, S. (2006), “Product design: A vectors field-based approach for preference modelling”, Journal of Engineering Design, Vol. 17 No. 3, pp. 217–233.Google Scholar
Pietila, G. and Lim, T.C. (2015), “Sound quality preference modeling using a nested artificial neural network architecture”, Noise Control Engineering Journal, Vol. 63 No. 2, pp. 138–151.CrossRefGoogle Scholar
Raina, A., McComb, C. and Cagan, J. (2019), “Learning to design from humans: Imitating human designers through deep learning”, Proceedings of the ASME Design Engineering Technical Conference, Vol. 2A-2019, pp. 1–13.Google Scholar
Sen, C., Caldwell, B.W., Summers, J.D. and Mocko, G.M. (2010), “Evaluation of the functional basis using an information theoretic approach”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing), Vol. 24 No. 1, p. 87.CrossRefGoogle Scholar
Sri, Ram, Mohinder, C. V., Gill, A. and Summers, J.D. (2017), “Using Graph Complexity Connectivity Method to Predict Information from Design Representations: A Comparative Study”, in Gero, J.S. (Ed.), Design Computing and Cognition ’16, Springer International Publishing, pp. 667–683.Google Scholar
Sridhar, S., Fazelpour, M., Gill, A. and Summers, J.D. (2016a), “Precision Analysis of the Graph Complexity Connectivity Method: Assembly and Function Model”, CIRP CATS 2016, CIRP, Gothenburg, Sweden, p. 01095.CrossRefGoogle Scholar
Sridhar, S., Fazelpour, M., Gill, A. and Summers, J.D. (2016b), “Accuracy and Precision Analysis of the Graph Complexity Connectivity Method”, CIRP CATS 2016, CIRP, Gothenburg, Sweden, pp. 163–168.Google Scholar
Stone, R.B. and Wood, K.L. (2000), “Development of a Functional Basis For Design”, Journal of Mechanical Design, Vol. 122 No. December.CrossRefGoogle Scholar
Suryadi, D. and Kim, H.M. (2019), “A Data-Driven Methodology to Construct Customer Choice Sets Using Online Data and Customer Reviews”, Journal of Mechanical Design, Vol. 141 No. 11, pp. 1–12.CrossRefGoogle Scholar
Ullman, D.G. (1992), The Mechanical Design Process, edited by Corrigan, J.J. and Morriss, J.M., 3rd ed., McGraw-Hill, New York, USA, available at: http://www.cambridge-design.co.uk/.Google Scholar
Visotsky, D., Patel, A. and Summers, J. (2017), “Using Design Requirements for Environmental Assessment of Products: A Historical Based Method”, Procedia CIRP, Elsevier B.V., Vol. 61, pp. 69–74.Google Scholar
Wan, J. and Krishnamurty, S. (2001), “Learning-based preference modeling in engineering design decision–making”, Journal of Mechanical Design, Transactions of the ASME, Vol. 123 No. 2, pp. 191–198.CrossRefGoogle Scholar
Wang, J. (2001), “Ranking engineering design concepts using a fuzzy outranking preference model”, Fuzzy Sets and Systems, Vol. 119 No. 1, pp. 161–170.CrossRefGoogle Scholar
Yang, J.B. and Sen, P. (1996), “Preference modelling by estimating local utility functions for multiobjective optimization”, European Journal of Operational Research, Vol. 95 No. 1, pp. 115–138.CrossRefGoogle Scholar