154
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A surrogate model based calibration method for structural adhesive joint progressive failure simulations

, , , , , , , , ORCID Icon & show all
Pages 1579-1606 | Received 18 Jul 2022, Accepted 08 Oct 2022, Published online: 23 Nov 2022

References

  • Chen, Q.; Guo, H.; Avery, K.; Su, X.; Kang, H. Fatigue Performance and Life Estimation of Automotive Adhesive Joints Using a Fracture Mechanics Approach. Eng. Fract. Mech. 2017, 172, 73. DOI: 10.1016/j.engfracmech.2017.01.005.
  • Adams, R.; Peppiatt, N. Stress Analysis of Adhesive Bonded Tubular Lap Joints. J. Adhes. 1977, 9(1), 1. DOI: 10.1080/00218467708075095.
  • Renton, W. J.; Vinson, J. R. The Efficient Design of Adhesive Bonded Joints. J. Adhes. 1975, 7(3), 175. DOI: 10.1080/00218467508075049.
  • Allman, D. A Theory for Elastic Stresses in Adhesive Bonded Lap Joints. Q. J. Mech. Appl. Math. 1977, 30(4), 415. DOI: 10.1093/qjmam/30.4.415.
  • Crocombe, A.; Adams, R. D. Influence of the Spew Fillet and Other Parameters on the Stress Distribution in the Single Lap Joint. J. Adhes. 1981, 13(2), 141. DOI: 10.1080/00218468108073182.
  • Avendaño, R.; Carbas, R.; Chaves, F.; Costa, M.; Da Silva, L.; Fernandes, A. Impact Loading of Single Lap Joints of Dissimilar Lightweight Adherends Bonded with a crash-resistant Epoxy Adhesive. Journal of Engineering Materials and Technology. 2016, 138(4). DOI: 10.1115/1.4034204.
  • Banea, M. D.; da Silva, L. F.; Carbas, R.; Campilho, R. D. Effect of Material on the Mechanical Behaviour of Adhesive Joints for the Automotive Industry. J. Adhes. Sci. Technol. 2017, 31(6), 663. DOI: 10.1080/01694243.2016.1229842.
  • Chen, G.; Guo, M. Failure Modeling of Adhesive Bonded Joints with Cohesive Elements; SAE Technical Paper, 2017.DOI: 10.4271/2017-01-0351.
  • de Oliveira, L. A.; Donadon, M. V. Delamination Analysis Using Cohesive Zone Model: A Discussion on traction-separation Law and mixed-mode Criteria. Eng. Fract. Mech. 2020, 228, 106922. DOI: 10.1016/j.engfracmech.2020.106922.
  • Capuano, G.; Rimoli, J. J . Smart Finite Elements: A Novel Machine Learning Application. Computer Methods in Applied Mechanics and Engineering. 2019, 345, 363–381. DOI: 10.1016/j.cma.2018.10.046.
  • Yang, X.; Xia, Y.; Zhou, Q.; Wang, P.-C.; Wang, K. Modeling of High Strength Steel Joints Bonded with Toughened Adhesive for Vehicle Crash Simulations. Int. J. Adhes. Adhes. 2012, 39, 21–32. DOI: 10.1016/j.ijadhadh.2012.06.007.
  • Dong, S.; Smith, A.; Sheldon, A. Modeling of Curing Adhesives between Jointed Steel and Aluminum Plates Using MAT_277 in LS-DYNA. in 11th European LS-DYNA Conference, Salzburg, Austria. 2017.
  • Veisytabar, M.; Reza, A.; Shekari, Y. Stress Analysis of adhesively-bonded Single stepped-lap Joints Based on three-parameter Fractional Viscoelastic Foundation Model. Proc. Inst. Mech. Eng. Part L. 2022, 236(5), 933–949. DOI: 10.1177/14644207211062497.
  • Fernandes, F. J.; Pavanello, R. Topology Optimization of Adhesive Material in a Single Lap Joint Using an Evolutionary Structural Optimization Method and a Cohesive Zone Model as Failure Criterion. Proc. Inst. Mech. Eng. Part L. 2022, 236(4), 757–778. DOI: 10.1177/14644207211056945.
  • Akhavan-Safar, A.; Beygi, R.; Delzendehrooy, F.; da Silva, L. Fracture Energy Assessment of Adhesives – Part I: Is GIC an Adhesive Property? A Neural Network Analysis. Proc. Inst. Mech. Eng. Part L. 2021, 235(6), 1461–1476. DOI: 10.1177/14644207211002763.
  • Delzendehrooy, F.; Beygi, R.; Akhavan-Safar, A.; da Silva, L. F. M. Fracture Energy Assessment of Adhesives Part II: Is GIIC an Adhesive Material Property? (A Neural Network Analysis). Journal of Advanced Joining Processes. 2021, 3, 100049. DOI: 10.1016/j.jajp.2021.100049.
  • Frazer, J. Parametric Computation: History and Future. Architectural Design. 2016, 862, 18–23. DOI:10.1002/ad.2019.
  • Zhang, X. Y.; Trame, M. N.; Lesko, L. J.; Schmidt, S. Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models. CPT: Pharmacometrics & Systems Pharmacology. 2015, 4(2), 69–79. DOI: 10.1002/psp4.6.
  • Valoroso, N.; Fedele, R. Characterization of a cohesive-zone Model Describing Damage and de-cohesion at Bonded Interfaces. Sensitivity Analysis and mode-I Parameter Identification. Int. J. Solids Struct. 2010, 47(13), 1666. DOI: 10.1016/j.ijsolstr.2010.03.001.
  • Schwarzkopf, G.; Bobbert, M.; Teutenberg, D.; Meschut, G.; Matzenmiller, A. Tolerance Analysis of Adhesive Bonds in Crash Simulation. Procedia CIRP. 2016, 43, 321–326. DOI: 10.1016/j.procir.2016.02.151.
  • Fang, Y.; Huang, L.; Zhan, Z.; Huang, S.; Liu, X.; Chen, Q.; Zhao, H.; Han, W. A Framework for Calibration of self-piercing Riveting Process Simulation Model. J. Manuf. Processes. 2022, 76, 223–235.DOI: 10.1016/j.jmapro.2022.01.015.
  • Rao, H. S.; Tiwari, S.; Koralla, S.; Ghosh, D.; Dey, S. Adhesive Failure Prediction in Crash Simulations; SAE Technical Paper, 2019. DOI: 10.4271/2019-26-0297.
  • Fang, Y.; Huang, L.; Zhan, Z.; Huang, S.; Han, W. Effect Analysis for the Uncertain Parameters on self-piercing Riveting Simulation Model Using Machine Learning Model; SAE Technical Paper, 2020. DOI: 10.4271/2020-01-0219.
  • Bhadeshia, H. Neural Networks and Information in Materials Science. Statistical Analysis and Data Mining: The ASA Data Science Journal. 2009, 1(5), 296–305. DOI: 10.1002/sam.10018.
  • Silva, G. C.; Beber, V. C.; Pitz, D. B. Machine Learning and Finite Element Analysis: An Integrated Approach for Fatigue Lifetime Prediction of Adhesively Bonded Joints. Fatigue Fract. Eng. Mater. Struct. 2021, 44(12), 3334. DOI: 10.1111/ffe.13559.
  • Pruksawan, S.; Lambard, G.; Samitsu, S.; Sodeyama, K.; Naito, M. Prediction and Optimization of Epoxy Adhesive Strength from a Small Dataset through Active Learning. Science and technology of advanced materials. 2019, 20(1), 1010–1021. DOI: 10.1080/14686996.2019.1673670.
  • Kang, H.; Lee, J. H.; Choe, Y.; Lee, S. G. Prediction of Lap Shear Strength and Impact Peel Strength of Epoxy Adhesive by Machine Learning Approach. Nanomaterials. 2021, 11(4), 872.DOI: 10.3390/nano11040872.
  • Shimamoto, K.; Akiyama, H. Estimating the Mechanical Residual Strength from IR Spectra Using Machine Learning for Degraded Adhesives. J. Adhes. 2021. DOI: 10.1080/00218464.2021.1978293.
  • Liang, Y.; Liu, Y.; Li, W. Prediction of Strength of Adhesive Bonded Joints Based on Machine Learning Algorithm and Finite Element Analysis. In Data Driven Smart Manufacturing Technologies and Applications; Springer: Cham. DOi: 10.1007/978-3-030-66849-5.
  • Marelli, S.; Lamas, C.; Konakli, K.; Mylonas, C.; Wiederkehr, P.; Sudret, B., UQLab user manual – Sensitivity analysis, Report UQLab-V2.0-106, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2022.
  • Lataniotis, C.; Wicaksono, D.; Marelli, S.; Sudret, B. UQLab User Manual – Kriging (Gaussian Process Modeling), Report UQLab-V2.0-105, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2022.
  • LS-DYNA, LS-DYNA Keyword User’s Manual, Version R13, LSTC., 2021.
  • Jiang, P.; Zhou, Q.; Shao, X. Surrogate model-based Engineering Design and Optimization; Springer: Singapore, 2020. DOi: 10.1007/978-981-15-0731-1.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.