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A CASE STUDY OF THE DECISION-MAKING BEHIND THE AUTOMATION OF A COMPOSITES-BASED DESIGN PROCESS
Published online by Cambridge University Press: 19 June 2023
Abstract
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Automation and artificial intelligence (AI) are increasingly seen as appealing tools to perform design tasks traditionally accomplished by human designers. In today's digital economy, industries aim to adopt these tools to improve the efficiency of their complex design processes. But how does one decide what parts of their existing design process should be automated and which automation/AI tool to implement? With these questions in mind, we present a case study highlighting a company's decision-making process in converting its existing designer-dependent design process to one supported by automation. In this case study, we observed the company's decisions in selecting and rejecting certain automation and AI methods before finalizing a heuristics-based automation method that proved highly efficient compared to the company's traditional human-driven design program. In addition, we present three key discussion points observed in this case study: (1) the importance of implementing the designer's heuristics in the automation framework, (2) the importance of a uniform and modular design automation framework, and (3) the challenges of implementing AI methods.
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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), 2023. Published by Cambridge University Press
References
Brainard, S.M. and Szajnfarber, Z. (2019), “How government oversight adds time to contractor engineering work”, Systems Engineering, Wiley Online Library, Vol. 22 No. 1, pp. 54–65.Google Scholar
Buede, D.M. and Miller, W.D. (2016), “The engineering design of systems: models and methods”, John Wiley & Sons.Google Scholar
Chua, D.K.H. and Hossain, M.A. (2011), “Predicting change propagation and impact on design schedule due to external changes”, IEEE Transactions on Engineering Management, IEEE, Vol. 59 No. 3, pp. 483–493.Google Scholar
Collopy, P.D. and Hollingsworth, P.M. (2011), “Value-driven design”, Journal of Aircraft, Vol. 48 No. 3, pp. 749–759.CrossRefGoogle Scholar
Cyert, R.M. and March, J.G. (1963), A Behavioral Theory of the Firm, Vol. 2, Englewood Cliffs, NJ.Google Scholar
Fujita, K., Minowa, K., Nomaguchi, Y., Yamasaki, S. and Yaji, K. (2021), “Design concept generation with variational deep embedding over comprehensive optimization”, Proceedings of the ASME Design Engineering Technical Conference, Vol. 3B-2021, available at:https://doi.org/10.1115/DETC2021-69544.CrossRefGoogle Scholar
Hou, T., Yannou, B., Leroy, Y. and Poirson, E. (2019), “Mining customer product reviews for product development: A summarization process”, Expert Systems with Applications, available at:https://doi.org/10.1016/j.eswa.2019.04.069.CrossRefGoogle Scholar
Hsiao, S.W. and Chou, J.R. (2004), “A creativity-based design process for innovative product design”, International Journal of Industrial Ergonomics, available at:https://doi.org/10.1016/j.ergon.2004.05.005.CrossRefGoogle Scholar
Lasi, H., Fettke, P., Kemper, H.G., Feld, T. and Hoffmann, M. (2014), “Industry 4.0”, Business and Information Systems Engineering, Vol. 6, pp. 239–242.CrossRefGoogle Scholar
Lee, J., Davari, H., Singh, J. and Pandhare, V. (2018), “Industrial Artificial Intelligence for industry 4.0-based manufacturing systems”, Manufacturing Letters, Vol. 18, pp. 20–23.CrossRefGoogle Scholar
Love, P.E.D. (2002), “Influence of project type and procurement method on rework costs in building construction projects”, Journal of Construction Engineering and Management, American Society of Civil Engineers, Vol. 128 No. 1, pp. 18–29.Google Scholar
Marion, T.J. and Fixson, S.K. (2020), “The Transformation of the Innovation Process: How Digital Tools are Changing Work, Collaboration, and Organizations in New Product Development”, Journal of Product Innovation Management, Vol. 38 No. 1, available at:https://doi.org/10.1111/jpim.12547.Google Scholar
Rechtin, E. and Maier, M.W. (2010), The Art of Systems Architecting, CRC press.CrossRefGoogle Scholar
Saadi, J.I. and Yang, M.C. (2023), “Generative Design: Reframing the Role of the Designer in Early-Stage Design Process”, Journal of Mechanical Design, American Society of Mechanical Engineers, Vol. 145 No. 4, p. 41411.Google Scholar
Verganti, R., Vendraminelli, L. and Iansiti, M. (2020), “Innovation and Design in the Age of Artificial Intelligence”, Journal of Product Innovation Management, available at:https://doi.org/10.1111/jpim.12523.CrossRefGoogle Scholar
De Weck, O.L., Roos, D. and Magee, C.L. (2011), Engineering Systems: Meeting Human Needs in a Complex Technological World, Mit Press.CrossRefGoogle Scholar
Zhou, C.C., Yin, G.F. and Hu, X.B. (2009), “Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach”, Materials and Design, available at:https://doi.org/10.1016/j.matdes.2008.06.006.CrossRefGoogle Scholar
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