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NEW OPPORTUNITIES AND BENEFITS IN THE PRODUCT DEVELOPMENT PROCESS USING THE MACHINE LEARNING BASED DIRECT INVERSE METHOD FOR MATERIAL PARAMETER IDENTIFICATION

Published online by Cambridge University Press:  19 June 2023

Paul Meißner  and
Thomas Vietor
Paul Meißner*
Affiliation:
Institute for Engineering Design, Technische Universität Braunschweig
Thomas Vietor
Affiliation:
Institute for Engineering Design, Technische Universität Braunschweig
*
Meißner, Paul, TU Braunschweig, Germany, p.meissner@tu-braunschweig.de

Abstract

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Finite element (FE) simulations can be used both in the early product development phase to evaluate the performance of developed components as well as in later stages to verify the reliability of functions and components that would otherwise require a large number of physical prototype tests. This requires calibrated material cards that are capable of realistically representing the specific material behavior. The necessary material parameter identification process is usually time-consuming and resource-intensive, which is why the direct inverse method based on machine learning has recently become increasingly popular. Within the neural network (NN) the generated domain knowledge can be stored and retrieved within milliseconds, which is why this method is time and resource-efficient. This research paper describes advantages and potentials of the direct inverse method in the context of the product development process (PDP). Additionally, arising transformation opportunities of the PDP are discussed and an application scenario of the method is presented followed by possible linkage potentials with existing development methods such as shape optimization.

Keywords

Machine learningMaterial parameter identificationSimulationNeural NetworksComputational design methods
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2785 - 2794
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), 2023. Published by Cambridge University Press

References

Afonso, P., Nunes, M., Paisana, A. and Braga, A. (2008), “The influence of time-to-market and target costing in the new product development success”, International Journal of Production Economics, Vol. 115 No. 2, pp. 559–568. http://doi.org/10.1016/j.ijpe.2008.07.003CrossRefGoogle Scholar
Aguir, H., BelHadjSalah, H. and Hambli, R. (2011), “Parameter identification of an elasto-plastic behaviour using artificial neural networks–genetic algorithm method”, Materials & Design, Vol. 32 No. 1, pp. 48–53. http://doi.org/10.1016/j.matdes.2010.06.039CrossRefGoogle Scholar
Albers, A. and Nowicki, L. (2003), Integration der Simulation in die Produktentwicklung - Neue Möglichkeiten zur Steigerung der Qualität und Effizienz in der Produktentwicklung, Symposium Simulation in der Produkt- und Prozessentwicklung, Bremen, 2003.Google Scholar
Chamekh, A., Bel Hadj Salah, H. and Hambli, , R. (2009), “Inverse technique identification of material parameters using finite element and neural network computation”, The International Journal of Advanced Manufacturing Technology, Vol. 44 No. 1-2, pp. 173–179. http://doi.org/10.1007/s00170-008-1809-6CrossRefGoogle Scholar
Demir, S., Mincev, K., Kok, K. and Paterakis, N.G. (2021), “Data augmentation for time series regression: Applying transformations, autoencoders and adversarial networks to electricity price forecasting”, Applied Energy, Vol. 304 No. 1, p. 117695. http://doi.org/10.1016/j.apenergy.2021.117695CrossRefGoogle Scholar
Elgowainy, A., Han, J., Ward, J., Joseck, F., Gohlke, D., Lindauer, A., Ramsden, T., Biddy, M., Alexander, M., Barnhart, S., Sutherland, I., Verduzco, L. and Wallington, T.J. (2018), “Current and Future United States Light-Duty Vehicle Pathways: Cradle-to-Grave Lifecycle Greenhouse Gas Emissions and Economic Assessment”, Environmental science & technology, Vol. 52 No. 4, pp. 2392–2399.CrossRefGoogle ScholarPubMed
Euro-NCAP (2020), Rating Review 2018: Report from the Ratings Group, European New Car Assessment Programme, Leuven, Belgium.Google Scholar
Jones, E.M.C., Carroll, J.D., Karlson, K.N., Kramer, S.L.B., Lehoucq, R.B., Reu, P.L. and Turner, D.Z. (2018), “Parameter covariance and non-uniqueness in material model calibration using the Virtual Fields Method”, Computational Materials Science, Vol. 152 No. 4, pp. 268–290. http://doi.org/10.1016/j.commatsci.2018.05.037CrossRefGoogle Scholar
Jospin, L.V., Buntine, W., Boussaid, F., Laga, H. and Bennamoun, M. (2022), “Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users”, IEEE Computational Intelligence Magazine, Vol. 17 No. 2, pp. 29–48. http://doi.org/10.1109/MCI.2022.3155327CrossRefGoogle Scholar
Jubinville, D., Esmizadeh, E., Tzoganakis, C. and Mekonnen, T. (2021), “Thermo-mechanical recycling of polypropylene for the facile and scalable fabrication of highly loaded wood plastic composites”, Composites Part B: Engineering, Vol. 219, p. 108873. http://doi.org/10.1016/j.compositesb.2021.108873CrossRefGoogle Scholar
Kim, H.-J., Keoleian, G.A. and Skerlos, S.J. (2011), “Economic Assessment of Greenhouse Gas Emissions Reduction by Vehicle Lightweighting Using Aluminum and High-Strength Steel”, Journal of Industrial Ecology, Vol. 15 No. 1, pp. 64–80. http://doi.org/10.1111/j.1530-9290.2010.00288.xCrossRefGoogle Scholar
Kohar, C.P., Greve, L., Eller, T.K., Connolly, D.S. and Inal, K. (2021), “A machine learning framework for accelerating the design process using CAE simulations: An application to finite element analysis in structural crashworthiness”, Computer Methods in Applied Mechanics and Engineering, Vol. 385 No. 4, p. 114008.CrossRefGoogle Scholar
Kučerová, A. (2007), Identification of nonlinear mechanical model parameters based on softcomputing methods. Czech Technical University in Prague.Google Scholar
Mahnken, R. (2018), “Identification of Material Parameters for Constitutive Equations”, in Stein, E., Borst, R. de and Hughes, T.J.R. (Eds.), Encyclopedia of Computational Mechanics Second Edition, Vol. 71, John Wiley & Sons, Ltd, Chichester, UK, pp. 1–21. http://doi.org/10.1002/9781119176817.ecm2043Google Scholar
Mareš, T., Janouchová, E. and Kučerová, A. (2016), “Artificial neural networks in the calibration of nonlinear mechanical models”, Advances in Engineering Software, Vol. 95 No. 2, pp. 68–81. http://doi.org/10.1016/j.advengsoft.2016.01.017CrossRefGoogle Scholar
Meißner, P., Winter, J. and Vietor, T. (2022a), “Methodology for Neural Network-Based Material Card Calibration Using LS-DYNA MAT_187_SAMP-1 Considering Failure with GISSMO”, Materials (Basel, Switzerland), Vol. 15 No. 2. http://doi.org/10.3390/ma15020643Google ScholarPubMed
Meißner, P., Hoppe, T. and Vietor, T. (2022b), “Comparative Study of Various Neural Network Types for Direct Inverse Material Parameter Identification in Numerical Simulations”, Applied Sciences, Vol. 12 No. 24, p. 12793. http://doi.org/10.3390/app122412793CrossRefGoogle Scholar
Morand, L. and Helm, D. (2019), “A mixture of experts approach to handle ambiguities in parameter identification problems in material modeling”, Computational Materials Science, Vol. 167 No. 3–4, pp. 85–91. http://doi.org/10.1016/j.commatsci.2019.04.003CrossRefGoogle Scholar
N.H.T.S.A. (2008), National Highway Traffic Safety Administration Laboratory Test Procedure for FMVSS 208, Occupant Crash Protection, U.S. Department of Transportation, Washington.Google Scholar
Rappel, H., Beex, L.A.A., Hale, J.S., Noels, L. and Bordas, S.P.A. (2020), “A Tutorial on Bayesian Inference to Identify Material Parameters in Solid Mechanics”, Archives of Computational Methods in Engineering, Vol. 27 No. 2, pp. 361–385. http://doi.org/10.1007/s11831-018-09311-xCrossRefGoogle Scholar
Schuh, G., Reinhart, G., Prote, J.-P., Sauermann, F., Horsthofer, J., Oppolzer, F. and Knoll, D. (2019), “Data Mining Definitions and Applications for the Management of Production Complexity”, Procedia CIRP, Vol. 81 No. 487–495, pp. 874–879. http://doi.org/10.1016/j.procir.2019.03.217CrossRefGoogle Scholar
Stavroulakis, G.E., Bolzon, G., Waszczyszyn, Z. and Ziemianski, L. (2003), “Inverse Analysis”, in Comprehensive Structural Integrity, Vol. 24, Elsevier, pp. 685–718. http://doi.org/10.1016/B0-08-043749-4/03117-7CrossRefGoogle Scholar
Technical Commitee 4.10, V.D.I. (2022), Development of mechatronic and cyber-physical systems. http://doi.org/10.31224/2452CrossRefGoogle Scholar
Unger, J.F. and Könke, C. (2011), “An inverse parameter identification procedure assessing the quality of the estimates using Bayesian neural networks”, Applied Soft Computing, Vol. 11 No. 4, pp. 3357–3367. http://doi.org/10.1016/j.asoc.2011.01.007CrossRefGoogle Scholar
Yagawa, G. and Okuda, H. (1996), “Neural networks in computational mechanics”, Archives of Computational Methods in Engineering, Vol. 3 No. 4, pp. 435–512. http://doi.org/10.1007/BF02818935CrossRefGoogle Scholar