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Effect Analysis for the Uncertain Parameters on Self-Piercing Riveting Simulation Model Using Machine Learning Model
Technical Paper
2020-01-0219
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Self-piercing rivets (SPR) are efficient and economical joining methods used in the manufacturing of lightweight automotive bodies. The finite element method (FEM) is a potentially effective way to assess the joining process of SPRs. However, uncertain parameters could lead to significant mismatches between the FEM predictions and physical tests. Thus, a sensitivity study on critical model parameters is important to guide the high-fidelity modeling of the SPR insertion process. In this paper, an axisymmetric FEM model is constructed to simulate the insertion process of the SPR using LS-DYNA/explicit. Then, several surrogate models are evaluated and trained using machine learning methods to represent the relations between selected inputs (e.g., material properties, interfacial frictions, and clamping force) and outputs (cross-section dimensions). It is found that it is feasible to train surrogate models with high accuracy to replace the time-consuming and computationally expensive CAE simulations with a limited sampling volume. Based on trained surrogate models, an extensive sensitivity study is conducted to thoroughly understand the effect of a set of model parameters. This work provides a solid foundation for data-modelling and CAE model calibration for the SPR insertion process.
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Fang, Y., Huang, L., Zhan, Z., Huang, S. et al., "Effect Analysis for the Uncertain Parameters on Self-Piercing Riveting Simulation Model Using Machine Learning Model," SAE Technical Paper 2020-01-0219, 2020, https://doi.org/10.4271/2020-01-0219.Data Sets - Support Documents
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References
- Li , Y. , Ma , Y. , Lou , M. , Lei , H. , and Lin , Z. Advances in Welding and Joining Processes of Multi-Material Lightweight Car Body Chinese Journal of Mechanical Engineering 52 24 1 23 2016 10.3901/JME.2016.24.001
- Li , D. , Chrysanthou , A. , Patel , I. , and Williams , G. Self-Piercing Riveting - A Review The International Journal of Advanced Manufacturing Technology 92 1777 1824 2017 10.1007/s00170-017-0156-x23
- https://www.assemblymag.com/articles/92728-assembling-fords-aluminum-wonder-truck
- Carandente , M. , Dashwood , R.J. , Masters , I.G. , and Han , L. Improvements in Numerical Simulation of the SPR Process Using a Thermo-Mechanical Finite Element Analysis Journal of Materials Processing Technology 236 148 161 2016 10.1016/j.jmatprotec.2016.05.001
- Porcaro , R. , Langseth , M. , Weyer , S. , and Hooputra , H. An Experimental and Numerical Investigation on Self-Piercing Riveting International Journal of Material Forming 1 1 1307 1310 2008 10.1007/s12289-008-0143-8
- Bouchard , P.O. , Laurent , T. , and Tollier , L. Numerical Modeling of Self-Pierce Riveting - From Riveting Process Modeling Down to Structural Analysis Journal of Materials Processing Technology 202 1-3 290 300 2008 10.1016/j.jmatprotec.2007.08.077
- Hoang , N.H. , Porcaro , R. , Langseth , M. , and Hanssen , A.G. Self-Piercing Riveting Connections Using Aluminium Rivets International Journal of Solids and Structures 47 3-4 427 439 2010 10.1016/j.ijsolstr.2009.10.009
- Mori , K. , Kato , T. , Abe , Y. , and Ravshanbek , Y. Plastic Joining of Ultra High Strength Steel and Aluminium Alloy Sheets by Self-Piercing Rivet CIRP Annals - Manufacturing Technology 55 1 283 286 2006 10.1016/S0007-8506(07)60417-X
- Casalino , G. , Rotondo , A. , and Ludovico , A. On the Numerical Modelling of the Multiphysics Self-Piercing Riveting Process Based on the Finite Element Technique Advances in Engineering Software 39 9 787 795 2008 10.1016/j.advengsoft.2007.12.002
- Huang , L. , Lasecki , J. , Guo , H. , and Su , X. Finite Element Modeling of Dissimilar Metal Self-piercing Riveting Process SAE Int. J. Mater. Manf. 7 3 698 705 2014 10.4271/2014-01-1982
- Huang , L. , Moraes , J.F.C. , Sediako , D.G. , Jordon , J.B. et al. Finite-Element and Residual Stress Analysis of Self-Pierce Riveting in Dissimilar Metal Sheets Journal of Manufacturing Science and Engineering 139 2 2016 10.1115/1.4034437
- Huang , L. , Wu , Y. , Huff , G. , Huang , S. , et al. Simulation of Self-Piercing Rivet Insertion Using Smoothed Particle Galerkin Method 15th International LS-DYNA Users Conference
- Coleman , H.W. and Steele , W.G. Experimentation and Uncertainty Analysis for Engineers Journal of Engineering for Industry 113 2 343 344 1999 10.1115/1.2899692
- Fang , Y. , Zhan , Z. , Yang , J. , and Liu , X. A Mixed-Kernel-Based Support Vector Regression Model for Automotive Body Design under Uncertainty ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering 3 4 041008 2017 10.1115/1.4036990
- Zhang , X.Y. , Trame , M. , Lesko , L. , and Schmidt , S. Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models CPT: Pharmacometrics & Systems Pharmacology 4 2 69 79 2015 10.1002/psp4.6
- Vidal , C. , Filho , M. , Takahashi , W. , and Desouza , P. Application of Sensitivity Analysis for Optimization of a Satellite Structure Journal of Spacecraft and Rockets 37 3 416 418 2000 10.2514/2.3576
- Ge , L. , Kim , N.H. , Bourne , G.R. , and Sawyer , W.G. Material Property Identification and Sensitivity Analysis Using Indentation and FEM ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 1 32 481 489 2006 10.1115/DETC2006-99329
- Jones , A.C. and Wilcox , R.K. Finite Element Analysis of the Spine: Towards a Framework of Verification, Validation and Sensitivity Analysis Medical Engineering and Physics 30 10 1287 1304 2008 10.1016/j.medengphy.2008.09.006
- Andrea , S. , Paola , A. , Ivano , A. , Francesca , C. et al. Variance Based Sensitivity Analysis of Model Output. Design and Estimator for the Total Sensitivity Index Computer Physics Communications 181 2 259 270 10.1016/j.cpc.2009.09.018
- Viana , F.A.C. , Venter , G. , and Balabanov , V. An Algorithm for Fast Optimal Latin Hypercube Design of Experiments International Journal for Numerical Methods in Engineering 82 2 1 10 2010 10.1002/nme.2750
- Rippa , S. An Algorithm for Selecting a Good Value for the Parametercin Radial Basis Function Interpolation Advances in Computational Mathematics 11 2-3 193 210 1999 10.1023/a:1018975909870
- Kleijnen , J.P.C. Kriging Metamodeling in Simulation: A Review European Journal of Operational Research 192 3 707 716 2007 10.1016/j.ejor.2007.10.013
- Chen , C. , Zhan , Z. , Yu , H. , and Zhao , H. An Efficient Decision-Making Framework for Hybrid Metamodeling Engineering Optimization 51 10 1761 1776 2019 10.1080/0305215X.2018.1547717
- Simpson , T.W. , Mauery , T.M. , Korte , J.J. , and Mistree , F. Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization AIAA Journal 39 12 2233 2241 10.2514/3.15017
- Stefano , M. and Bruno , S. UQLab: A Framework for Uncertainty Quantification in MATLAB the 2nd International Conference on Vulnerability and Risk Analysis and Management United Kingdom July 13-16 2014 2554 2563 10.1061/9780784413609.257
- https://www.uqlab.com/sensitivity-sobol-indices