Abstract
In this study, we address the limitations of digital twins, including significant computational time and the complexity of real-world processes, by utilizing surrogate models (SMs) to partially or entirely represent digital twins. We investigate the performance of surrogate models in a manufacturing system scenario through sensitivity analysis and adaptive sampling strategies with incremental learning. Our experimental setup involves a synthetic digital twin and a neural network-based surrogate model. The study explores the impact of various parameters such as dataset size, number of epochs, and batch size on the neural network’s performance. We also analyze the effectiveness of different adaptive sampling strategies in the incremental learning process.
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References
Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Nee, A.Y.C.: Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 94(9), 3563–3576 (2018)
Negri, E., Fumagalli, L., Macchi, M.: A review of the roles of digital twin in CPS-based production systems. Procedia Manuf. 11, 939–948 (2017)
Leng, J., et al.: Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model. Robot. Comput.-Integr. Manuf. 63, 10 (2020)
Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W.: Digital twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine 51(11), 1016–1022 (2018)
Grieves, M.: Digital twin: manufacturing excellence through virtual factory replication. White Paper 1(2014), 1–7 (2014)
Tao, F., Zhang, H., Liu, A., Nee, A.Y.: Digital twin in industry: state-of-the-art. IEEE Trans. Ind. Inf. 15(4), 2405–2415 (2018)
Chakraborti, A., Heininen, A., Koskinen, K.T., Lämsä, V.: Digital twin: multi-dimensional model reduction method for performance optimization of the virtual entity. Procedia CIRP 93, 240–245 (2020)
Benner, P., Gugercin, S., Willcox, K.: A survey of projection-based model reduction methods for parametric dynamical systems. SIAM Rev. 57(4), 483–531 (2015)
Uhlemann, T.H.-J., Lehmann, C., Steinhilper, R., Verl, A.: The digital twin: demonstrating the potential of real time data acquisition in production systems. Procedia Manuf. 9, 113–120 (2017)
Enders, M.R., Hoßbach, N.: Dimensions of digital twin applications-a literature review (2019)
Dias, L., Bhosekar, A., Ierapetritou, M.: Adaptive sampling approaches for surrogate-based optimization. In: Computer Aided Chemical Engineering, pp. 377–384. Elsevier (2019)
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Casal, A.G., García, A.P. (2023). Sensitivity Analysis of Surrogate Modeling for Manufacturing in Digital Twins. In: Mehmood, R., et al. Distributed Computing and Artificial Intelligence, Special Sessions I, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 741. Springer, Cham. https://doi.org/10.1007/978-3-031-38318-2_29
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DOI: https://doi.org/10.1007/978-3-031-38318-2_29
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