Optimization of wear parameters for ECAP-processed ZK30 alloy using response surface and machine learning approaches: a comparative study

Mahmoud Shaban; Fahad Nasser Alsunaydih; Hanan Kouta; Samar El-Sanabary; Abdulrahman Alrumayh; Abdulrahman I Alateyah; Majed O Alawad; Waleed H El-Garaihy; Yasmine El-Taybany

doi:10.1038/s41598-024-59880-0

Optimization of wear parameters for ECAP-processed ZK30 alloy using response surface and machine learning approaches: a comparative study

Sci Rep. 2024 Apr 22;14(1):9233. doi: 10.1038/s41598-024-59880-0.

Authors

Affiliations

¹ Department of Electrical Engineering, College of Engineering, Qassim University, 56452, Unaizah, Saudi Arabia.
² Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt.
³ Department of Production Engineering and Mechanical Design, Port Said University, Port Fouad, 42526, Egypt.
⁴ Department of Mechanical Engineering, College of Engineering, Qassim University, 56452, Unaizah, Saudi Arabia.
⁵ Department of Mechanical Engineering, College of Engineering, Qassim University, 56452, Unaizah, Saudi Arabia. a.alateyah@qu.edu.sa.
⁶ Center of Excellence for Nanomaterials for Clean Energy Applications, King Abdulaziz City for Science and Technology (KACST), 12354, Riyadh, Saudi Arabia.
⁷ Department of Mechanical Engineering, College of Engineering, Qassim University, 56452, Unaizah, Saudi Arabia. w.nasr@qu.edu.sa.
⁸ Mechanical Engineering Department, Faculty of Engineering, Suez Canal University, Ismailia, 41522, Egypt. w.nasr@qu.edu.sa.

PMID: 38649457
DOI: 10.1038/s41598-024-59880-0

Abstract

The present research applies different statistical analysis and machine learning (ML) approaches to predict and optimize the processing parameters on the wear behavior of ZK30 alloy processed through equal channel angular pressing (ECAP) technique. Firstly, The ECAPed ZK30 billets have been examined at as-annealed (AA), 1-pass, and 4-passes of route Bc (4Bc). Then, the wear output responses in terms of volume loss (VL) and coefficient of friction (COF) have been experimentally investigated by varying load pressure (P) and speed (V) using design of experiments (DOE). In the second step, statistical analysis of variance (ANOVA), 3D response surface plots, and ML have been employed to predict the output responses. Subsequently, genetic algorithm (GA), hybrid DOE-GA, and multi-objective genetic algorithm techniques have been used to optimize the input variables. The experimental results of ECAP process reveal a significant reduction in the average grain size by 92.7% as it processed through 4Bc compared to AA counterpart. Furthermore, 4Bc exhibited a significant improvement in the VL by 99.8% compared to AA counterpart. Both regression and ML prediction models establish a significant correlation between the projected and the actual data, indicating that the experimental and predicted values agreed exceptionally well. The minimal VL at different ECAP passes was obtained at the highest condition of the wear test. Also, the minimal COF for all ECAP passes was obtained at maximum wear load. However, the optimal speed in the wear process decreased with the number of billets passes for minimum COF. The validation of predicted ML models and VL regression under different wear conditions have an accuracy range of 70-99.7%, respectively.

Keywords: Equal channel angular pressing; Machine learning; Response surface methodology; Wear performance; ZK30 alloy.