A Method for Benchmarking of FEM Packages for Multi-Stage Sheet Metal Forming Simulations

Abstract:

Computer simulation plays a crucial role in the designing of sheet metal stamping processes for the prediction of process output, before try-out die sets are manufactured. Different commercial software packages are available on the market for sheet forming simulation, but their accuracy can vary, depending on the selection of the pre-processing parameters and on their formulation. Software benchmarking can be used to select the most appropriate package for a given application. Calibration, i.e. the inverse determination of the correct set of pre-processing parameters, can be used for improving the prediction accuracy. The scientific literature on numerical simulations of sheet metal forming processes presents some examples of software calibration and very few examples of benchmarking. The literature generally neglects a critical and important issue: the inherent variability of real forming processes. In this work, the experimental results of two similar multi-stage deep drawing processes are presented and compared to the simulation output of two popular software packages used in the industry. Statistical methods for benchmarking and calibration are proposed. The paper demonstrates how benchmarking can be misleading if process variability is not considered.

• Periodical: Key Engineering Materials (Volume 926)
• Pages: 2201-2210
• DOI: https://doi.org/10.4028/p-jpf626
• Online since: July 2022
• Authors: Matteo Strano*, Quirico Semeraro, Matteo Panzeri
• Keywords: Benchmarking, Calibration, Sheet Metal, Simulation, Variability

References

[1] Lee MG, Kim C, Pavlina EJ, Barlat F. Advances in Sheet Forming—Materials Modeling, Numerical Simulation, and Press Technologies. J Manuf Sci Eng 2011;133:061001. https://doi.org/10.1115/1.4005117.

DOI: 10.1115/1.4005117

[2] Banabic D. Sheet Metal Forming Processes. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010. https://doi.org/10.1007/978-3-540-88113-1.

[3] Ablat MA, Qattawi A. Numerical simulation of sheet metal forming: a review. Int J Adv Manuf Technol 2017;89:1235–50. https://doi.org/10.1007/s00170-016-9103-5.

DOI: 10.1007/s00170-016-9103-5

[4] Demeri MY, Lou M, Saran MJ. A Benchmark Test for Springback Simulation in Sheet Metal Forming. vol. 1. 2000. https://doi.org/10.4271/2000-01-2657.

DOI: 10.4271/2000-01-2657

[5] Zhang L. Background and Tryout Report for BM2: Underbody Cross Member. AIP Conf. Proc., vol. 778, AIP; 2005, p.888–93. https://doi.org/10.1063/1.2011334.

DOI: 10.1063/1.2011334

[6] Volk W, Illig R, Kupfer H, Wahlen A, Hora P, Kessler L, et al. Virtual Forming Limit Curves. Part A: Physical Tryout report. Numisheet benchmark 1, Zurich: (2008).

[7] de Sena JI V., Guzman CF, Duchene L, Habraken AM, Valente RAF, Alves de Sousa RJ. Numerical simulation of a conical shape made by single point incremental, 2013, p.852–5. https://doi.org/10.1063/1.4850104.

DOI: 10.1063/1.4850104

[8] Roberts SM, Hall FR, Van Bael A, et al (1992) Benchmark tests for 3-D, elasto-plastic, finite-element codes for the modelling of metal forming processes. J Mater Process Technol 34:61–68. https://doi.org/10.1016/0924-0136(92)90090-F.

DOI: 10.1016/0924-0136(92)90090-f

[9] Pimentel AMF, de Carvalho Martins Alves JL, de Seabra Merendeiro NM, Vieira DMF. Comprehensive benchmark study of commercial sheet metal forming simulation softwares used in the automotive industry. Int J Mater Form 2018:1–21. https://doi.org/10.1007/s12289-018-1397-4.

DOI: 10.1007/s12289-018-1397-4

[10] Amaral RL, Neto DM, Wagre D, et al (2020) Issues on the Correlation between Experimental and Numerical Results in Sheet Metal Forming Benchmarks. Metals (Basel) 10:1595. https://doi.org/10.3390/met10121595.

DOI: 10.3390/met10121595