Application of enhanced coulomb models and virtual tribology in a practical study

Carvalho, Lucas Alexandre de; Lukács, Zsolt

doi:10.1556/606.2022.00582

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…It was found that the restraining force normalised by the drawbead width increased with increasing values in the bead height-to-radius ratio. De Carvalho and Lukács [24] used friction shear stress values to detect those places on the workpiece that were exposed to tangential stress caused by contact with the tools and to verify the drawbeads. For the analysed region, the coulomb model was found to be more relevant than the pressure-dependent model for describing friction phenomena.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Frictional Performance of AW-5251 Aluminium Alloy Sheets Using the Random Forest Machine Learning Algorithm and Multilayer Perceptron

et al. 2023

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This paper is devoted to the determination of the coefficient of friction (COF) in the drawbead region in metal forming processes. As the test material, AW-5251 aluminium alloys sheets fabricated under various hardening conditions (AW-5251-O, AW-5251-H14, AW-5251-H16 and AW-5251H22) were used. The sheets were tested using a drawbead simulator with different countersample roughness and different orientations of the specimens in relation to the sheet rolling direction. A drawbead simulator was designed to model the friction conditions when the sheet metal passed through the drawbead in sheet metal forming. The experimental tests were carried out under conditions of dry friction and lubrication of the sheet metal surfaces with three lubricants: machine oil, hydraulic oil, and engine oil. Based on the results of the experimental tests, the value of the COF was determined. The Random Forest (RF) machine learning algorithm and artificial neural networks (ANNs) were used to identify the parameters affecting the COF. The R statistical package software version 4.1.0 was used for running the RF model and neural network. The relative importance of the inputs was analysed using 12 different activation functions in ANNs and nine different loss functions in the RF. Based on the experimental tests, it was concluded that the COF for samples cut along the sheet rolling direction was greater than for samples cut in the transverse direction. However, the COF’s most relevant input was oil viscosity (0.59), followed by the average counter sample roughness Ra (0.30) and the yield stress Rp0.2 and strength coefficient K (0.05 and 0.06, respectively). The hard sigmoid activation function had the poorest R2 (0.25) and nRMSE (0.30). The ideal run was found after training and testing the RF model (R2 = 0.90 ± 0.028). Ra values greater than 1.1 and Rp0.2 values between 105 and 190 resulted in a decreased COF. The COF values dropped to 9–35 for viscosity and 105–190 for Rp0.2, with a gap between 110 and 130 when the oil viscosity was added. The COF was low when the oil viscosity was 9–35, and the Ra was 0.95–1.25. The interaction between K and the other inputs, which produces a relatively limited range of reduced COF values, was the least relevant. The COF was reduced by setting the Rp0.2 between 105 and 190, the Ra between 0.95 and 1.25, and the oil viscosity between 9 and 35.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Frictional Performance of AW-5251 Aluminium Alloy Sheets Using the Random Forest Machine Learning Algorithm and Multilayer Perceptron

et al. 2023

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“…The stamped portion is typically overbent to counteract this effect. One of the most crucial elements affecting contacting surface characteristics of the tool and sheet is the Blank Holding Force (BHF) [3]. Shape variation after sheet forming is mostly influenced by contact surface pressure and friction, and tool geometries [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Determination of influential springback parameters in U-bending test

Dessie

Lukács

2023

Pollack

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U-bending tests are the most common method to predict springback and are influenced by the process and geometrical variables in addition to material behaviour. It needs a numerical study at a high level with many variables to reduce try-out time and loop. In this study, the U-bending test of DC01 steel has been researched numerically and experimentally to govern the influential parameters. The numerical analysis was conducted using AutoForm-Sigma code. The die radius has an excessive influence on the change of flange angle than the punch radius, but the punch radius has the greatest influence on the variation of the sidewall angle. The coefficient of friction played a great impact on both flange and sidewall angle deviation and its influence grows stronger as the blank holding force increases.

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Application of enhanced coulomb models and virtual tribology in a practical study

Cited by 2 publications

References 10 publications

Analysis of the Frictional Performance of AW-5251 Aluminium Alloy Sheets Using the Random Forest Machine Learning Algorithm and Multilayer Perceptron

Analysis of the Frictional Performance of AW-5251 Aluminium Alloy Sheets Using the Random Forest Machine Learning Algorithm and Multilayer Perceptron

Determination of influential springback parameters in U-bending test

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