A Comparative Study of a Machine Learning Approach and Response Surface Methodology for Optimizing the HPT Processing Parameters of AA6061/SiCp Composites

Authors

Waleed H. El-Garaihy, Department of Mechanical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia,Mechanical Engineering Department, Faculty of Engineering, Suez Canal University, Ismailia 41522, Egypt
Abdulrahman I. Alateyah, Department of Mechanical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia
Mahmoud Shaban, Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia,Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt
Mohammed F. Alsharekh, Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia

Document Type

Research Article

Publication Title

Journal of Manufacturing and Materials Processing

Publication Date

8-10-2023

doi

10.3390/jmmp7040148

Recommended Citation

APA Citation

El-Garaihy, W. Alateyah, A. Shaban, M. & Alsharekh, M. (2023). A Comparative Study of a Machine Learning Approach and Response Surface Methodology for Optimizing the HPT Processing Parameters of AA6061/SiCp Composites. Journal of Manufacturing and Materials Processing, 7, 10.3390/jmmp7040148
https://fount.aucegypt.edu/faculty_journal_articles/5318

MLA Citation

El-Garaihy, Waleed H., et al. "A Comparative Study of a Machine Learning Approach and Response Surface Methodology for Optimizing the HPT Processing Parameters of AA6061/SiCp Composites." Journal of Manufacturing and Materials Processing, vol. 7, 2023,
https://fount.aucegypt.edu/faculty_journal_articles/5318