Modeling the strain localization of shell elements subjected to combined stretch–bend loads: Application on automotive sheet metal stamping simulations

Pham, Quoc Tuan; Islam, Md. Shafiqul; Barlo, Alexander; Sigvant, Mats; Caro, Lluís Pérez; Trana, Kristoffer

doi:10.1016/j.tws.2023.110804

Cited by 3 publications

(1 citation statement)

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“…The metal forming processes of deep drawing high-quality steel sheets are among the leading methods of producing finished components, especially in car body production [1,2]. The mass production of car body parts takes place almost exclusively using sheet metal forming processes [3]. This method of plastic working is fast and does not cause production waste, which is inherent in machining processes [4].…”

Section: Introductionmentioning

confidence: 99%

The Comparison of the Multi-Layer Artificial Neural Network Training Methods in Terms of the Predictive Quality of the Coefficient of Friction of 1.0338 (DC04) Steel Sheet

Trzepieciński

2024

Materials

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Friction is one of the main phenomena accompanying sheet metal forming methods, affecting the surface quality of products and the formability of the sheet metal. The most basic and cheapest way to reduce friction is to use lubricants, which should ensure the highest lubrication efficiency and at the same time be environmentally friendly. Due to the trend towards sustainable production, vegetable oils have been used in research as an alternative to petroleum-based lubricants. The analysis of friction in sheet metal forming requires an appropriate tribotester simulating the friction conditions in a specific area of the sheet metal being formed. Research has used a special strip drawing tribometer, enabling the determination the value of the coefficient of friction in the blankholder zone in the deep drawing process. Quantitative analysis of the friction phenomenon is necessary at the stage of designing the technological process and selecting technological parameters, including blankholder pressure. This article presents the results of friction testing of 1.0338 (DC04) steel sheets using a strip drawing test. The experimental tests involved pulling a strip of sheet metal between two countersamples with a rounded surface. The tests were carried out on countersamples with different levels of roughness for the range of contact pressures occurring in the blankholder zone in the deep drawing process (1.7–5 MPa). The values of the coefficient of friction determined under dry friction conditions were compared with the results for edible (corn, sunflower and rapeseed) and non-edible (Moringa, Karanja) vegetable lubricants. The tested oils are the most commonly used vegetable-based biolubricants in metal forming operations. Multi-layer artificial neural networks were used to determine the relationship between the value of the contact pressure, the roughness of the countersamples, the oil viscosity and density, and the value of the coefficient of friction. Rapeseed oil provided the best lubrication efficiency during friction testing for all of the tested samples, with an average surface roughness of Sa 0.44–1.34 μm. At the same time, as the roughness of the countersamples increased, a decrease in lubrication efficiency was observed. The lowest root mean squared error value was observed for the MLP-4-8-1 network trained with the quasi-Newton algorithm. Most of the analysed networks with different architectures trained using the various algorithms showed that the kinematic viscosity of the oil was the most important aspect in assessing the friction of the sheets tested. The influence of kinematic viscosity on the value of the coefficient of friction is strongly dependent on the surface roughness of the countersamples.

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Section: Introductionmentioning

confidence: 99%

The Comparison of the Multi-Layer Artificial Neural Network Training Methods in Terms of the Predictive Quality of the Coefficient of Friction of 1.0338 (DC04) Steel Sheet

Trzepieciński

2024

Materials

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Uncertainty quantification for conical hole expansion test of DP800 sheet metal

Pham,

Barlo,

Islam

et al. 2024

Int J Mater Form

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The hole expansion ratio (HER) observed in a standardized hole expansion test (HET) is commonly used to determine the edge fracture of steel sheets. A large variation of the measured HER restricts the practical application of the method. This study presents a systematic investigation on uncertainties in the HER of DP800 sheet material, including the hole-edge quality, pre-strain due to the hole-punching process, the friction coefficient, and the determination of fracture. An artificial neural network was trained to develop a surrogate model using a database gained from a thousand finite element simulations of the HET. Monte-Carlo simulations were performed using the trained surrogate model to characterize the distribution of the HER. Sensitivity analysis via Sobol’s indices is calculated to determine the influence of the input variables on the output. It is found that the pre-strain and pre-damage generated during the hole punching process in the shear-affected zone dominate the variation of the HER. Discussions on reducing the output’s variation are detailed. In general, these findings provide valuable insights for the determination of HER as well as the edge crack behavior of the investigated DP800 steel sheet.

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