Investigation of the Effect of ECAP Parameters on Hardness, Tensile Properties, Impact Toughness, and Electrical Conductivity of Pure Cu through Machine Learning Predictive Models

Mahmoud Shaban; Mohammed F Alsharekh; Fahad Nasser Alsunaydih; Abdulrahman I Alateyah; Majed O Alawad; Amal BaQais; Mokhtar Kamel; Ahmed Nassef; Medhat A El-Hadek; Waleed H El-Garaihy

doi:10.3390/ma15249032

Investigation of the Effect of ECAP Parameters on Hardness, Tensile Properties, Impact Toughness, and Electrical Conductivity of Pure Cu through Machine Learning Predictive Models

Materials (Basel). 2022 Dec 17;15(24):9032. doi: 10.3390/ma15249032.

Authors

Affiliations

¹ Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia.
² Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt.
³ Department of Mechanical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia.
⁴ Materials Science Research Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 12354, Saudi Arabia.
⁵ Department of Chemistry, College of Science, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
⁶ Mechanical Engineering Department, Faculty of Engineering, Suez Canal University, Ismailia 41522, Egypt.
⁷ Production Engineering and Mechanical Engineering Department, Faculty of Engineering, Port Said University, Port Fouad 42534, Egypt.
⁸ President of East Port Said University of Technology, North Sinai 45632, Egypt.

Abstract

Copper and its related alloys are frequently adopted in contemporary industry due to their outstanding properties, which include mechanical, electrical, and electronic applications. Equal channel angular pressing (ECAP) is a novel method for producing ultrafine-grained or nanomaterials. Modeling material design processes provides exceptionally efficient techniques for minimizing the efforts and time spent on experimental work to manufacture Cu or its associated alloys through the ECAP process. Although there have been various physical-based models, they are frequently coupled with several restrictions and still require significant time and effort to calibrate and enhance their accuracies. Machine learning (ML) techniques that rely primarily on data-driven models are a viable alternative modeling approach that has recently achieved breakthrough achievements. Several ML algorithms were used in the modeling training and testing phases of this work to imitate the influence of ECAP processing parameters on the mechanical and electrical characteristics of pure Cu, including the number of passes (N), ECAP die angle (φ), processing temperature, and route type. Several experiments were conducted on pure commercial Cu while altering the ECAP processing parameters settings. Linear regression, regression trees, ensembles of regression trees, the Gaussian process, support vector regression, and artificial neural networks are the ML algorithms used in this study. Model predictive performance was assessed using metrics such as root-mean-squared errors and R² scores. The methodologies presented here demonstrated that they could be effectively used to reduce experimental effort and time by reducing the number of experiments runs required to optimize the material attributes aimed at modeling the ECAP conditions for the following performance characteristics: impact toughness (I_T), electrical conductivity (E_C), hardness, and tensile characteristics of yield strength (σ_y), ultimate tensile strength (σ_u), and ductility (D_u).

Keywords: ANN; Cu and related alloys; ECAP parameters; electrical conductivity; machine learning; mechanical properties.

Grants and funding

This research received no external funding.