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

Tomasz Trzepieciński; Sherwan Mohammed Najm; Omar Maghawry Ibrahim; Marek Kowalik

doi:10.3390/ma16155207

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

Materials (Basel). 2023 Jul 25;16(15):5207. doi: 10.3390/ma16155207.

Authors

Tomasz Trzepieciński¹, Sherwan Mohammed Najm^{2

3}, Omar Maghawry Ibrahim⁴, Marek Kowalik⁵

Affiliations

¹ Department of Manufacturing Processes and Production Engineering, Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, al. Powst. Warszawy 8, 35-959 Rzeszów, Poland.
² Kirkuk Technical Institute, Northern Technical University, 36001 Kirkuk, Iraq.
³ Department of Manufacturing Science and Engineering, Budapest University of Technology and Economics, Műegyetemrkp 3, H-1111 Budapest, Hungary.
⁴ Plant Production Department, Arid Land Cultivation Research Institute, City of Scientific Research and Technological Applications SRTA-City, Borg Al-Arab 21934, Egypt.
⁵ Faculty of Mechanical Engineering, Kazimierz Pulaski University of Technology and Humanities in Radom, 54 Stasieckiego Street, 26-600 Radom, Poland.

Abstract

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 R_p0.2 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 R_p0.2 values between 105 and 190 resulted in a decreased COF. The COF values dropped to 9-35 for viscosity and 105-190 for R_p0.2, 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 R_p0.2 between 105 and 190, the Ra between 0.95 and 1.25, and the oil viscosity between 9 and 35.

Keywords: aluminium alloys; coefficient of friction; friction; sheet metal forming; tribology.

Grants and funding

This research received no external funding.