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Influence of ECAP Parameters on the Structural, Electrochemical and Mechanical Behavior of ZK30: A Combination of Experimental and Machine Learning Approaches

Authors :
Mahmoud Shaban
Abdulrahman I. Alateyah
Mohammed F. Alsharekh
Majed O. Alawad
Amal BaQais
Mokhtar Kamel
Fahad Nasser Alsunaydih
Waleed H. El-Garaihy
Hanadi G. Salem
Source :
Journal of Manufacturing and Materials Processing, Vol 7, Iss 2, p 52 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Several physics-based models have been utilized in material design for the simulation and prediction of material properties. In this study, several machine-learning (ML) approaches were used to construct a prediction model to analyze the influence of equal-channel angular pressing (ECAP) parameters on the microstructural, corrosion and mechanical behavior of the biodegradable magnesium alloy ZK30. The ML approaches employed were linear regression, the Gaussian process, and support vector regression. For the optimization of the alloy’s performance, experiments were conducted on ZK30 billets using different ECAP routes, channel angles, and number of passes. The adopted ML model is an adequate predictive model which agreed with the experimental results. ECAP die angles had an insignificant effect on grain refinement, compared to the route type. ECAP via four passes of route Bc (rotating the sample 90° on its longitudinal axis after each pass in the same direction) was the most effective condition producing homogenous ultrafine grain distribution of 1.92 µm. Processing via 4-Bc and 90° die angle produced the highest hardness (97-HV) coupled with the highest tensile strength (344 MPa). The optimum corrosion rate of 0.140 mils penetration per year (mpy) and the optimum corrosion resistance of 1101 Ω·cm2 resulted from processing through 1-pass using the 120°-die. Grain refinement resulted in reducing the corrosion rates and increased corrosion resistance, which agreed with the ML findings.

Details

Language :
English
ISSN :
25044494
Volume :
7
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Journal of Manufacturing and Materials Processing
Publication Type :
Academic Journal
Accession number :
edsdoj.3d6cbf49e6dc46db96212e185ec03c4a
Document Type :
article
Full Text :
https://doi.org/10.3390/jmmp7020052