No CrossRef data available.
Article contents
KNOWLEDGE-BASED DATA IDENTIFICATION FOR MACHINE LEARNING USE CASES
Published online by Cambridge University Press: 19 June 2023
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.
The number of digital solutions based on machine learning has increased in recent years. In many industrial sectors, they try to enhance automation in manual or repetitive tasks or provide decision support for complex problems. Data plays an essential role in the selection and implementation of ML algorithms, as it determines the quality of the training and the results. As data drive ML models, selecting the correct data with the suitable ML algorithm for a given use case is crucial but challenging. This paper reviews the application of machine learning in the embodiment design phase addressing the challenge. The work focuses on ML applications in conventional product development and non-conventional additive manufacturing processes. Based on the literature review, the required knowledge to implement the ML algorithms has been derived and presented in a systematic approach. This work highlights the importance of an initial analysis of the existing knowledge in the engineering and additive manufacturing processes in order to implement the proper ML algorithms.
Information
- Type
- Article
- Information
- Creative Commons
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
Apt, W., Bovenschulte, M., Priesack, K., Weiß, C. and Hartmann, E.A. (2018), Einsatz von digitalen Assistenzsystemen im Betrieb: Forschungsbericht vom Institut für Innovation und Technik im Auftrag des BMAS (accessed 23 November 2022).Google Scholar
Awad, E.M. and Ghaziri, H. (2004), Knowledge management, 1st ed., Prentice Hall, Upper Saddle River, N.J.Google Scholar
Braw, E. (2021), Artificial intelligence: The risks posed by the current lack of standards, AEI: American Enterprise Institute for Public Policy Research.Google Scholar
Cepowski, T. and Chorab, P. (2021a), “Determination of design formulas for container ships at the preliminary design stage using artificial neural network and multiple nonlinear regression”, OCEAN ENGINEERING, Vol. 238.CrossRefGoogle Scholar
Cepowski, T. and Chorab, P. (2021b), “The Use of Artificial Neural Networks to Determine the Engine Power and Fuel Consumption of Modern Bulk Carriers, Tankers and Container Ships”, ENERGIES, Vol. 14 No. 16.CrossRefGoogle Scholar
Chiarello, F., Belingheri, P. and Fantoni, G. (2021), “Data science for engineering design: State of the art and future directions”, Computers in Industry, Vol. 129, p. 103447.CrossRefGoogle Scholar
Chua, C.K. and Leong, K.F. (2014), 3d Printing And Additive Manufacturing: Principles And Applications (With Companion Media Pack) - Fourth Edition Of Rapid Prototyping, World Scientific Publishing Company.CrossRefGoogle Scholar
Després, N., Cyr, E., Setoodeh, P. and Mohammadi, M. (2020), “Deep Learning and Design for Additive Manufacturing: A Framework for Microlattice Architecture”, JOM, Vol. 72 No. 6, pp. 2408–2418.CrossRefGoogle Scholar
Dieter, G.E. and Schmidt, L.C. (2013), Engineering design, 5. ed., McGraw-Hill, New York, NY.Google Scholar
Gibson, I., Rosen, D., Stucker, B. and Khorasani, M. (2021), “Design for Additive Manufacturing”, in Additive Manufacturing Technologies, Springer, Cham, pp. 555–607.CrossRefGoogle Scholar
Habib, A. and Yildirim, U. (2022), “Developing a physics-informed and physics-penalized neural network modelfor preliminary design of multi-stage friction pendulum bearings”, ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, Vol. 113.CrossRefGoogle Scholar
Huang, H., Xue, C., Zhang, W. and Guo, M. (2022), “Torsion design of CFRP-CFST columns using a data-driven optimization approach”, ENGINEERING STRUCTURES, Vol. 251.CrossRefGoogle Scholar
Jiang, J., Xiong, Y., Zhang, Z. and Rosen, D.W. (2022), “Machine learning integrated design for additive manufacturing”, JOURNAL OF INTELLIGENT MANUFACTURING, Vol. 33 No. 4, pp. 1073–1086.CrossRefGoogle Scholar
Joshi, A.V. (2020), Machine Learning and Artificial Intelligence, Springer International Publishing, Cham.CrossRefGoogle Scholar
Kim, D., Seth, A. and Liem, R.P. (2022), “Data-enhanced dynamic flight simulations for flight performance analysis”, AEROSPACE SCIENCE AND TECHNOLOGY, Vol. 121.CrossRefGoogle Scholar
Kim, S.-G., Yoon, S.M., Yang, M., Choi, J., Akay, H. and Burnell, E. (2019), “AI for design: Virtual design assistant”, CIRP Annals, Vol. 68 No. 1, pp. 141–144.CrossRefGoogle Scholar
Ko, H., Witherell, P., Lu, Y., Kim, S. and Rosen, D.W. (2021), “Machine learning and knowledge graph based design rule construction for additive manufacturing”, Additive Manufacturing, Vol. 37, p. 101620.CrossRefGoogle Scholar
Liao, H., Mei, H., Hu, G., Wu, B. and Wang, Q. (2021), “Machine learning strategy for predicting flutter performance of streamlined box girders”, JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, Vol. 209.CrossRefGoogle Scholar
Mouritz, A.P. (Ed.) (2012), Introduction to Aerospace Materials, Woodhead Publishing Limited, Cambridge, United Kingdom.Google Scholar
Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R. and Muharemagic, E. (2015), “Deep learning applications and challenges in big data analytics”, Journal of Big Data, Vol. 2 No. 1, pp. 1–21.CrossRefGoogle Scholar
Oh, Y., Sharp, M., Sprock, T. and S, KWON. (2021), “Neural network-based build time estimation for additive manufacturing: a performance comparison”, Journal of Computational Design and Engineering, Vol. 8 No. 5, pp. 1243–1256.CrossRefGoogle Scholar
Pahl, G., Beitz, W., Feldhusen, J. and Grote, K.-H. (2007), Engineering design: A systematic approach, 3rd ed., Springer, London.CrossRefGoogle Scholar
Park, H., Ko, H., Lee, Y.T., Feng, S., Witherell, P. and Cho, H. (2021), “Collaborative knowledge management to identify data analytics opportunities in additive manufacturing”, Journal of Intelligent Manufacturing, pp. 1–24.Google Scholar
Polyzotis, N., Roy, S., Whang, S.E. and Zinkevich, M. (2018), “Data Lifecycle Challenges in Production Machine Learning”, ACM SIGMOD Record, Vol. 47 No. 2, pp. 17–28.CrossRefGoogle Scholar
Posch, S., Winter, H., Zelenka, J., Pirker, G. and Wimmer, A. (2021), “Development of a tool for the preliminary design of large engine prechambers using machine learning approaches”, APPLIED THERMAL ENGINEERING, Vol. 191.CrossRefGoogle Scholar
Pradel, P., Zhu, Z., Bibb, R. and Moultrie, J. (2018), “A framework for mapping design for additive manufacturing knowledge for industrial and product design”, Journal of Engineering Design, Vol. 29 No. 6, pp. 291–326.CrossRefGoogle Scholar
Preidel, M., Wang, W.M., Exner, K. and Stark, R. (2018a), “Knowledge in Engineering Design: A Systematic Literature Review on Artifacts and IT Systems”, in Proceedings of the DESIGN 2018 15th International Design Conference, May, 21-24, 2018, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Croatia; The Design Society, Glasgow, UK, pp. 881–892.CrossRefGoogle Scholar
Preidel, M., Wang, W.M., Exner, K. and Stark, R. (2018b), “KNOWLEDGE IN ENGINEERING DESIGN: A SYSTEMATIC LITERATURE REVIEW ON ARTIFACTS AND IT SYSTEMS”, paper presented at DESIGN 2018 - 15th International Design Conference, available at: https://www.designsociety.org/publication/40501/knowledge+in+engineering+design%3a+a+systematic+literature+review+on+artifacts+and+it+systems.CrossRefGoogle Scholar
Stark, R. (2022), Virtual Product Creation in Industry, Springer Berlin Heidelberg, Berlin, Heidelberg.CrossRefGoogle Scholar
Stark, R., Brandenburg, E. and Lindow, K. (2021), “Characterization and application of assistance systems in digital engineering”, CIRP Annals, Vol. 70 No. 1, pp. 131–134.CrossRefGoogle Scholar
Stark, R. and Weber, C. (1991), Wissensbasierte Systeme für die Konstruktion—Grundlagen aus konstruktionsmethodischer Sicht.Google Scholar
Thoben, K.-D. and Lewandowski, M. (2016), “Information and Data Provision of Operational Data for the Improvement of Product Development”, in Bouras, A., Eynard, B., Foufou, S. and Thoben, K.-D. (Eds.), Product Lifecycle Management in the Era of Internet of Things, IFIP Advances in Information and Communication Technology, Vol. 467, Springer International Publishing, Cham, pp. 3–12.Google Scholar
Ullman, D. (2009), EBOOK: The Mechanical Design Process, 4th edition, McGraw-Hill Education, New York.Google Scholar
Ulrich, K.T. and Eppinger, S.D. (2016), Product design and development, Sixth edition, McGraw-Hill Education, New York NY.Google Scholar
Wang, H.-H. and Chen, C.-P. (2021), Design, Integration, and Verification of a Low-Cost Nailfold Capillaroscopy, 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW).CrossRefGoogle Scholar
Wang, W.M., Preidel, M., Fachbach, B. and Stark, R. (2020), “Towards a Reference Model for Knowledge Driven Data Provision Processes”, in Springer, Cham, pp. 123–132.Google Scholar
Zhao, Y. and Kim, D.-J. (2022), Using GA-BP Neural Network to Assist Ship Design, 2022 International Symposium on Electrical, Electronics and Information Engineering (ISEEIE).CrossRefGoogle Scholar
You have
Access
Open access
- 2
- Cited by