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Large language models in complex system design

Published online by Cambridge University Press:  16 May 2024

Alejandro Pradas Gomez ,
Petter Krus ,
Massimo Panarotto  and
Ola Isaksson
Alejandro Pradas Gomez*
Affiliation:
Chalmers University of Technology, Sweden
Petter Krus
Affiliation:
Linköping University, Sweden
Massimo Panarotto
Affiliation:
Chalmers University of Technology, Sweden
Ola Isaksson
Affiliation:
Chalmers University of Technology, Sweden
*
alejandro.pradas@chalmers.se

Abstract

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This paper investigates the use of Large Language Models (LLMs) in engineering complex systems, demonstrating how they can support designers on detail design phases. Two aerospace cases, a system architecture definition and a CAD model generation activities are studied. The research reveals LLMs' challenges and opportunities to support designers, and future research areas to further improve their application in engineering tasks. It emphasizes the new paradigm of LLMs support compared to traditional Machine Learning techniques, as they can successfully perform tasks with just a few examples.

Keywords

large language model (LLM)complex systemsartificial intelligence (AI)computer-aided design (CAD)knowledge-based engineering (KBE)
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2197 - 2206
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), 2024.

References

Amadori, K., Tarkian, M., Ölvander, J. and Krus, P., 2012. Flexible and robust CAD models for design automation. Advanced Engineering Informatics, 26(2), pp.180-195. https://doi.org/10.1016/j.aei.2012.01.004CrossRefGoogle Scholar
Aranburu, A., Cotillas, J., Justel, D., Contero, M. and Camba, J.D., 2022. How does the modeling strategy influence design optimization and the automatic generation of parametric geometry variations?. Computer-Aided Design, 151, https://doi.org/10.1016/j.cad.2022.103364CrossRefGoogle Scholar
Behzadi, M.M. and Ilieş, H.T., 2022. Gantl: Toward practical and real-time topology optimization with conditional generative adversarial networks and transfer learning. Journal of Mechanical Design, 144(2), p.021711.Google Scholar
Bian, S., Grandi, D., Liu, T., Jayaraman, P.K., Willis, K., Sadler, E., Borijin, B., Lu, T., Otis, R., Ho, N. and Li, B., 2024. HG-CAD: Hierarchical Graph Learning for Material Prediction and Recommendation in Computer-Aided Design. Journal of Computing and Information Science in Engineering, 24(1). 10.1115/1.4063226CrossRefGoogle Scholar
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y.T., Li, Y., Lundberg, S. and Nori, H., 2023. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712.Google Scholar
Chiarello, F., Cimino, A., Fantoni, G. and Dell'Orletta, F., 2018. Automatic users extraction from patents. World Patent Information, 54, pp.28-38. https://doi.org/10.1016/j.wpi.2018.07.006CrossRefGoogle Scholar
European Union Aviation Safety Agency (EASA), 2023. Artificial Intelligence Roadmap 2.0. easa.europa.eu/ai. Last accessed November 2023.Google Scholar
Fantoni, G., Coli, E., Chiarello, F., Apreda, R., Dell'Orletta, F. and Pratelli, G., 2021. Text mining tool for translating terms of contract into technical specifications: Development and application in the railway sector. Computers in Industry, 124, http://dx.doi.org/10.1016/j.compind.2020.103357CrossRefGoogle Scholar
Giordano, V., Chiarello, F., Melluso, N., Fantoni, G. and Bonaccorsi, A., 2021. Text and dynamic network analysis for measuring technological convergence: A case study on defense patent data. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2021.3078231CrossRefGoogle Scholar
Giordano, V., Puccetti, G., Chiarello, F., Pavanello, T. and Fantoni, G., 2023. Unveiling the inventive process from patents by extracting problems, solutions and advantages with natural language processing. Expert Systems with Applications, 229, p.120499. https://doi.org/10.1016/j.eswa.2023.120499CrossRefGoogle Scholar
Han, J., Sarica, S., Shi, F. and Luo, J., 2022. Semantic networks for engineering design: state of the art and future directions. Journal of Mechanical Design, 144(2), p.020802. https://doi.org/10.1115/1.4052148Google Scholar
Han, Y. and Moghaddam, M., 2021. Eliciting attribute-level user needs from online reviews with deep language models and information extraction. Journal of Mechanical Design, 143(6), p.061403. https://dx.doi.org/10.1115/1.4048819CrossRefGoogle Scholar
Hu, Z., Li, X., Pan, X., Wen, S. and Bao, J., 2023. A question answering system for assembly process of wind turbines based on multi-modal knowledge graph and large language model. Journal of Engineering Design, pp.1-25. https://doi.org/10.1080/09544828.2023.2272555CrossRefGoogle Scholar
Jun, H. and Nichol, A., 2023. Shap-e: Generating conditional 3d implicit functions. arXiv preprint arXiv:2305.02463.Google Scholar
Just, J., 2024. Natural language processing for innovation search–Reviewing an emerging non-human innovation intermediary. Technovation, 129, p.102883. https://doi.org/10.1016/j.technovation.2023.102883CrossRefGoogle Scholar
Kodnongbua, M., Jones, B.T., Ahmad, M.B.S., Kim, V.G. and Schulz, A., 2023. Zero-shot CAD Program Re-Parameterization for Interactive Manipulation. arXiv preprint arXiv:2306.03217.CrossRefGoogle Scholar
La Rocca, G. and Van Tooren, M.J., 2009. Knowledge-based engineering approach to support aircraft multidisciplinary design and optimization. Journal of aircraft, 46(6), pp.1875-1885. https://dx.doi.org/10.2514/1.39028CrossRefGoogle Scholar
La Rocca, G., 2012. Knowledge based engineering: Between AI and CAD. Review of a language based technology to support engineering design. Advanced engineering informatics, 26(2), pp.159-179. https://doi.org/10.1016/j.aei.2012.02.002CrossRefGoogle Scholar
Li, M., Lou, S., Zheng, H., Feng, Y., Gao, Y., Zeng, S. and Tan, J., 2024. A cognitive analysis-based key concepts derivation approach for product design. Expert Systems with Applications, https://dx.doi.org/10.1016/j.eswa.2023.121289CrossRefGoogle Scholar
Marrone, A., 2023. Optimizing Product Development and Innovation Processes with Artificial Intelligence (Master Thesis, Politecnico di Torino). uri: http://webthesis.biblio.polito.it/id/eprint/27710Google Scholar
Meltzer, P., Lambourne, J.G. and Grandi, D., 2024. What's in a Name? Evaluating Assembly-Part Semantic Knowledge in Language Models Through User-Provided Names in Computer Aided Design Files. Journal of Computing and Information Science in Engineering, 24(1), p.011002. https://doi.org/10.1115/1.4062454CrossRefGoogle Scholar
Moon, J. and Suh, E.S., 2023. Multiple technology infusion assessment: a framework and case study. Research in Engineering Design, 34(3), pp.347-366. https://doi.org/10.1007/s00163-023-00414-6CrossRefGoogle Scholar
Müller, J.R., Panarotto, M. and Isaksson, O., 2020. Design space exploration of a jet engine component using a combined object model for function and geometry. Aerospace, 7(12), p.173. https://dx.doi.org/10.3390/aerospace7120173CrossRefGoogle Scholar
Nelson, M.D., Goenner, B.L. and Gale, B.K., 2023. Utilizing ChatGPT to assist CAD design for microfluidic devices. Lab on a Chip, 23(17), pp.3778-3784. https://doi.org/10.1039/D3LC00518FCrossRefGoogle ScholarPubMed
Panarotto, M., Kipouros, T., Brahma, A., Isaksson, O., Strandh Tholin, O. and Clarkson, J., 2022. Using DSMs in functionally driven explorative design experiments–an automation approach. In DS 121: Proceedings of the 24th International DSM Conference (DSM 2022), Eindhoven, The Netherlands, October, 11-13, 2022 (pp. 68-77). https://doi.org/10.35199/dsm2022.08Google Scholar
SAE International, 2021. axonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. J3016_202104. https://www.sae.org/standards/content/j3016_202104/Google Scholar
Sarsa, S., Denny, P., Hellas, A. and Leinonen, J., 2022, August. Automatic generation of programming exercises and code explanations using large language models. In Proceedings of the 2022 ACM Conference on International Computing Education Research-Volume 1 (pp. 27-43). https://dx.doi.org/10.1145/3501385.3543957CrossRefGoogle Scholar
Sheard, S.A. and Mostashari, A., 2009. Principles of complex systems for systems engineering. Systems Engineering, 12(4), pp.295-311. https://doi.org/10.1002/sys.20124CrossRefGoogle Scholar
Singh, J., Samborowski, L. and Mentzer, K., 2023. A Human Collaboration with ChatGPT: Developing Case Studies with Generative AI. In Proceedings of the ISCAP Conference ISSN (Vol. 2473, p. 4901).Google Scholar
Suh, E.S., Furst, M.R., Mihalyov, K.J. and Weck, O.D., 2010. Technology infusion for complex systems: A framework and case study. Systems Engineering, 13(2), pp.186-203. https://doi.org/10.1002/sys.20142CrossRefGoogle Scholar
Ray, Tikayat, Cole, A., Pinon Fischer, B.F., White, O.J., and Mavris, R.T., D.N., 2023. aeroBERT-Classifier: Classification of Aerospace Requirements Using BERT. Aerospace, 10(3), p.279. https://doi.org/10.3390/aerospace10030279Google Scholar
Ulrich, K., 1995. The role of product architecture in the manufacturing firm. Research policy, 24(3), pp.419-440. https://doi.org/10.1016/0048-7333(94)00775-3CrossRefGoogle Scholar
Ulrich, K.T. and Eppinger, S.D. (2008) Product Design and Development. 4th Edition, McGraw-Hill, New York.Google Scholar
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., et al. .., 2017. Attention is all you need. Advances in neural information processing systems, 30. ISBN: 9781510860964Google Scholar
Wang, X., Anwer, N., Dai, Y. and Liu, A., 2023. ChatGPT for design, manufacturing, and education. Procedia CIRP, 119, pp.7-14. https://doi.org/10.1016/j.procir.2023.04.001CrossRefGoogle Scholar
Xiao, Y., Zheng, S., Shi, J., Du, X. and Hong, J., 2023. Knowledge graph-based manufacturing process planning: A state-of-the-art review. Journal of Manufacturing Systems, 70, pp.417-435. 10.1016/j.jmsy.2023.08.006CrossRefGoogle Scholar
Zhu, Q., Zhang, X. and Luo, J., 2023. Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design, 145(4), p.041409. https://doi.org/10.1115/1.4056598CrossRefGoogle Scholar