Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-13T01:32:52.313Z Has data issue: false hasContentIssue false
  • English
  • Français

Datasets in design research: needs and challenges and the role of AI and GPT in filling the gaps

Published online by Cambridge University Press:  16 May 2024

Mohammad Arjomandi Rad ,
Tina Hajali ,
Julian Martinsson Bonde ,
Massimo Panarotto ,
Kristina Wärmefjord ,
Johan Malmqvist  and
Ola Isaksson
Mohammad Arjomandi Rad*
Affiliation:
Chalmers University of Technology, Sweden
Tina Hajali
Affiliation:
Chalmers University of Technology, Sweden
Julian Martinsson Bonde
Affiliation:
Chalmers University of Technology, Sweden
Massimo Panarotto
Affiliation:
Chalmers University of Technology, Sweden
Kristina Wärmefjord
Affiliation:
Chalmers University of Technology, Sweden
Johan Malmqvist
Affiliation:
Chalmers University of Technology, Sweden
Ola Isaksson
Affiliation:
Chalmers University of Technology, Sweden
*
radmo@chalmers.se

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.

Despite the recognized importance of datasets in data-driven design approaches, their extensive study remains limited. We review the current landscape of design datasets and highlight the ongoing need for larger and more comprehensive datasets. Three categories of challenges in dataset development are identified. Analyses show critical dataset gaps in design process where future studies can be directed. Synthetic and end-to-end datasets are suggested as two less explored avenues. The recent application of Generative Pretrained Transformers (GPT) shows their potential in addressing these needs.

Keywords

data-driven designdataset gapartificial intelligence (AI)design researchgenerative design
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 1919 - 1928
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

Ajayi, K., Wei, X., Gryder, M., Shields, W., Wu, J., Jones, S.M., Kucer, M., Oyen, D., 2023. DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding. Sci Data 10, 772. https://doi.org/10.1038/s41597-023-02653-7CrossRefGoogle ScholarPubMed
Arjomandi Rad, M., Cenanovic, M., Salomonsson, K., 2023. Image regression-based digital qualification for simulation-driven design processes, case study on curtain airbag. Journal of Engineering Design 34, 1–22. https://doi.org/10.1080/09544828.2022.2164440CrossRefGoogle Scholar
Babu, S.S., Mourad, A.-H.I., Harib, K.H., Vijayavenkataraman, S., 2023. Recent developments in the application of machine-learning towards accelerated predictive multiscale design and additive manufacturing. Virtual and Physical Prototyping 18. https://doi.org/10.1080/17452759.2022.2141653CrossRefGoogle Scholar
Bagazinski, N.J., Ahmed, F., 2023. Ship-D: Ship Hull Dataset for Design Optimization using Machine Learning.CrossRefGoogle Scholar
Birkhofer, H., Lindemann, U., Weber, C., 2012. A View on Design: The German Perspective. Journal of Mechanical Design 134, 110301. https://doi.org/10.1115/1.4007847CrossRefGoogle Scholar
Chan, Y.-C., Ahmed, F., Wang, L., Chen, W., 2021. METASET: Exploring Shape and Property Spaces for Data-Driven Metamaterials Design. Journal of Mechanical Design 143, 031707. https://doi.org/10.1115/1.4048629CrossRefGoogle Scholar
Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., Yu, F., 2015. ShapeNet: An Information-Rich 3D Model Repository. https://doi.org/10.48550/arXiv.1512.03012CrossRefGoogle Scholar
Chen, W., Chiu, K., Fuge, M., 2019. Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks, in: AIAA Scitech 2019 Forum. Presented at the AIAA Scitech 2019 Forum, American Institute of Aeronautics and Astronautics, San Diego, California. https://doi.org/10.2514/6.2019-2351CrossRefGoogle Scholar
Chiarello, F., Belingheri, P., Fantoni, G., 2021. Data science for engineering design: State of the art and future directions. Computers in Industry 129, 103447. https://doi.org/10.1016/j.compind.2021.103447CrossRefGoogle Scholar
Curtis, B., Oliveira, V., 2023. Faker. URL https://github.com/faker-ruby/faker (accessed 11.15.23).Google Scholar
Du, X., Bilgen, O., Xu, H., 2021. Generating Pseudo-Data to Enhance the Performance of Classification-Based Engineering Design: A Preliminary Investigation. Presented at the ASME 2020 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers Digital Collection. https://doi.org/10.1115/IMECE2020-24634CrossRefGoogle Scholar
Ejlli, D., 2022. Mockaroo: Generate Data To Practice With SQL And Python. Physics and Machine Learning. URL https://medium.com/physics-and-machine-learning/mockaroo-generate-data-to-practice-with-sql-and-python-7e581bc6d583 (accessed 11.12.23).Google Scholar
Filippi, S., 2023. Measuring the impact of ChatGPT on fostering concept generation in innovative product design. Electronics 12, 3535.CrossRefGoogle Scholar
Girotra, K., Meincke, L., Terwiesch, C., Ulrich, K.T., 2023. Ideas are Dimes a Dozen: Large Language Models for Idea Generation in Innovation. https://doi.org/10.2139/ssrn.4526071CrossRefGoogle Scholar
Goodman, E., 2014 . Design and ethics in the era of big data. interactions 21, 22–24. https://doi.org/10.1145/2598902CrossRefGoogle Scholar
Gorkovenko, K., Burnett, D.J., Thorp, J.K., Richards, D., Murray-Rust, D., 2020. Exploring The Future of Data-Driven Product Design, in: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Presented at the CHI ’20: CHI Conference on Human Factors in Computing Systems, ACM, Honolulu HI USA, pp. 1–14. https://doi.org/10.1145/3313831.3376560CrossRefGoogle Scholar
Gryaditskaya, Y., Sypesteyn, M., Hoftijzer, J.W., Pont, S.C., Durand, F., Bousseau, A., 2019. OpenSketch: a richly-annotated dataset of product design sketches. ACM Trans. Graph. 38, 232–1.CrossRefGoogle Scholar
Guillard, B., Remelli, E., Yvernay, P., Fua, P., 2021. Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketches, in: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Montreal, QC, Canada, pp. 13003–13012. https://doi.org/10.1109/ICCV48922.2021.01278Google Scholar
Han, W., Xiang, S., Liu, C., Wang, R., Feng, C., 2020. SPARE3D: A Dataset for SPAtial REasoning on Three-View Line Drawings, in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Presented at the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Seattle, WA, USA, pp. 14678–14687. https://doi.org/10.1109/CVPR42600.2020.01470CrossRefGoogle Scholar
Nobari, Heyrani, Srivastava, A., Gutfreund, A., Ahmed, D., F., 2022. LINKS: A Dataset of a Hundred Million Planar Linkage Mechanisms for Data-Driven Kinematic Design. Presented at the ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers Digital Collection. https://doi.org/10.1115/DETC2022-89798CrossRefGoogle Scholar
Hoang, V.-N., Nguyen, N.-L., Tran, D.Q., Vu, Q.-V., Nguyen-Xuan, H., 2022. Data-driven geometry-based topology optimization. Struct Multidisc Optim 65, 69. https://doi.org/10.1007/s00158-022-03170-8CrossRefGoogle Scholar
Jayanti, S., Kalyanaraman, Y., Iyer, N., Ramani, K., 2006. Developing an engineering shape benchmark for CAD models. Computer-Aided Design 38, 939–953. https://doi.org/10.1016/j.cad.2006.06.007CrossRefGoogle Scholar
Kasturi, S., 2020. Some Aspects of Test Data Management Strategy, in: 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON). IEEE, pp. 6–12.Google Scholar
Kim, S., Chi, H., Hu, X., Huang, Q., Ramani, K., 2020. A Large-Scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks, in: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (Eds.), Computer Vision – ECCV 2020, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 175–191. https://doi.org/10.1007/978-3-030-58523-5_11CrossRefGoogle Scholar
Koch, P.N., Simpson, T.W., Allen, J.K., Mistree, F., 1999. Statistical Approximations for Multidisciplinary Design Optimization: The Problem of Size. Journal of Aircraft 36, 275–286. https://doi.org/10.2514/2.2435CrossRefGoogle Scholar
Koch, S., Matveev, A., Jiang, Z., Williams, F., Artemov, A., Burnaev, E., Alexa, M., Zorin, D., Panozzo, D., 2019. ABC: A Big CAD Model Dataset for Geometric Deep Learning, in: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Presented at the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9593–9603. https://doi.org/10.1109/CVPR.2019.00983Google Scholar
Lejeune, E., 2020. Mechanical MNIST: A benchmark dataset for mechanical metamodels. Extreme Mechanics Letters 36, 100659. https://doi.org/10.1016/j.eml.2020.100659CrossRefGoogle Scholar
Málaga-Chuquitaype, C., 2022. Machine Learning in Structural Design: An Opinionated Review. Frontiers in Built Environment 8.CrossRefGoogle Scholar
Manda, B., Dhayarkar, S., Mitheran, S., Viekash, V.K., Muthuganapathy, R., 2021. ‘CADSketchNet’ - An Annotated Sketch dataset for 3D CAD Model Retrieval with Deep Neural Networks. Computers & Graphics 99, 100–113. https://doi.org/10.1016/j.cag.2021.07.001CrossRefGoogle Scholar
Mo, K., Zhu, S., Chang, A.X., Yi, L., Tripathi, S., Guibas, L.J., Su, H., 2019. PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding, in: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Presented at the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Long Beach, CA, USA, pp. 909–918. https://doi.org/10.1109/CVPR.2019.00100CrossRefGoogle Scholar
Moghaddam, M., Marion, T., Holtta-Otto, K., Fu, K., Olechowski, A., McComb, C. (Eds.), 2023. Special Issue: Emerging Technologies and Methods for Early-Stage Product Design and Development. Journal of Mechanical Design 145. https://doi.org/10.1115/1.4056744CrossRefGoogle Scholar
Nelson, M.D., Goenner, B.L., Gale, B.K., 2023. Utilizing ChatGPT to assist CAD design for microfluidic devices. Lab on a Chip 23, 3778–3784. https://doi.org/10.1039/d3lc00518fCrossRefGoogle ScholarPubMed
Panarotto, M., Isaksson, O., Habbassi, I., Cornu, N., 2022. Value-Based Development Connecting Engineering and Business: A Case on Electric Space Propulsion. IEEE Transactions on Engineering Management 69, 1650–1663. https://doi.org/10.1109/TEM.2020.3029677CrossRefGoogle Scholar
Panchal, J.H., Fuge, M., Liu, Y., Missoum, S., Tucker, C. (Eds.), 2019. Special Issue: Machine Learning for Engineering Design. Journal of Mechanical Design 141. https://doi.org/10.1115/1.4044690CrossRefGoogle Scholar
Chen, Philip, Zhang, C.L., C.-Y., 2014. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences 275, 314–347. https://doi.org/10.1016/j.ins.2014.01.015CrossRefGoogle Scholar
Picard, C., Schiffmann, J., Ahmed, F., 2023. DATED: Guidelines for Creating Synthetic Datasets for Engineering Design Applications.CrossRefGoogle Scholar
Rad, M.A., Salomonsson, K., Cenanovic, M., Balague, H., Raudberget, D., Stolt, R., 2022. Correlation-based feature extraction from computer-aided design, case study on curtain airbags design. Computers in Industry 138, 103634. https://doi.org/10.1016/j.compind.2022.103634Google Scholar
Ramnath, S., Haghighi, P., Kim, J.H., Detwiler, D., Berry, M., Shah, J.J., Aulig, N., Wollstadt, P., Menzel, S., 2019. Automatically Generating 60,000 CAD Variants for Big Data Applications. Presented at the ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers Digital Collection. https://doi.org/10.1115/DETC2019-97378CrossRefGoogle Scholar
Regenwetter, L., Curry, B., Ahmed, F., 2021. BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks. https://doi.org/10.48550/arXiv.2103.05844CrossRefGoogle Scholar
Regenwetter, L., Nobari, A.H., Ahmed, F., 2022. Deep Generative Models in Engineering Design: A Review. https://doi.org/10.48550/arXiv.2110.10863CrossRefGoogle Scholar
Renear, A.H., Sacchi, S., Wickett, K.M., 2010. Definitions of dataset in the scientific and technical literature. Proceedings of the American Society for Information Science and Technology 47, 1–4. https://doi.org/10.1002/meet.14504701240CrossRefGoogle Scholar
Shugrina, M., Li, C.-Y., Fidler, S., 2022. Neural Brushstroke Engine: Learning a Latent Style Space of Interactive Drawing Tools. ACM Trans. Graph. 41, 1–18. https://doi.org/10.1145/3550454.3555472CrossRefGoogle Scholar
Siddharth, L., Blessing, L., Luo, J., 2022. Natural language processing in-and-for design research. Design Science 8, e21. https://doi.org/10.1017/dsj.2022.16CrossRefGoogle Scholar
Song, B., Zhou, R., Ahmed, F., 2023. Multi-modal Machine Learning in Engineering Design: A Review and Future Directions. https://doi.org/10.48550/arXiv.2302.10909CrossRefGoogle Scholar
Tiro, D., 2023. The Possibility of Applying ChatGPT (AI) for Calculations in Mechanical Engineering, in: Karabegovic, I., Kovačević, A., Mandzuka, S. (Eds.), New Technologies, Development and Application VI, Lecture Notes in Networks and Systems. Springer Nature Switzerland, Cham, pp. 313–320. https://doi.org/10.1007/978-3-031-31066-9_34Google Scholar
Ulrich, K.T., Eppinger, S.D., Yang, M.C., 2008. Product design and development. McGraw-Hill higher education Boston.Google Scholar
Wang, G.G., Shan, S., 2006. Review of Metamodeling Techniques in Support of Engineering Design Optimization. Journal of Mechanical Design 129, 370–380. https://doi.org/10.1115/1.2429697CrossRefGoogle Scholar
Whalen, E., Beyene, A., Mueller, C., 2021. SimJEB: Simulated Jet Engine Bracket Dataset. https://doi.org/10.1111/cgf.14353CrossRefGoogle Scholar
Willis, K.D.D., Pu, Y., Luo, J., Chu, H., Du, T., Lambourne, J.G., Solar-Lezama, A., Matusik, W., 2021. Fusion 360 gallery: a dataset and environment for programmatic CAD construction from human design sequences. ACM Trans. Graph. 40, 1–24. https://doi.org/10.1145/3450626.3459818CrossRefGoogle Scholar
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J., 2015. 3d shapenets: A deep representation for volumetric shapes, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1912–1920.Google Scholar
Xu, P., Huang, Y., Yuan, T., Pang, K., Song, Y.-Z., Xiang, T., Hospedales, T.M., Ma, Z., Guo, J., 2018. SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Presented at the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Salt Lake City, UT, USA, pp. 8090–8098. https://doi.org/10.1109/CVPR.2018.00844CrossRefGoogle Scholar
Yang, M., Jiang, P., Zang, T., Liu, Y., 2023. Data-driven intelligent computational design for products: method, techniques, and applications. Journal of Computational Design and Engineering 10, 1561–1578. https://doi.org/10.1093/jcde/qwad070CrossRefGoogle Scholar
Yüksel, N., Börklü, H.R., Sezer, H.K., Canyurt, O.E., 2023. Review of artificial intelligence applications in engineering design perspective. Engineering Applications of Artificial Intelligence 118, 105697. https://doi.org/10.1016/j.engappai.2022.105697CrossRefGoogle Scholar
Zheng, L., Kumar, S., Kochmann, D.M., 2023. Unifying the design space of truss metamaterials by generative modeling.Google Scholar
Zhu, Q., Luo, J., 2023. Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23. https://doi.org/10.1115/1.4056220Google Scholar
Zhu, Q., Zhang, X., Luo, J., 2023. Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145. https://doi.org/10.1115/1.4056598CrossRefGoogle Scholar