Evidence map›Paper›PMID 39071718›Full record

ArticleHeliyon2024

A comparative study of machine learning and response surface methodologies for optimizing wear parameters of ECAP-processed ZX30 alloy.

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

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Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Samar El-SanabaryDepartment of Production Engineering and Mechanical Design, Port Said University, Port Fouad, 42526, Egypt.
Hanan KoutaDepartment of Production Engineering and Mechanical Design, Port Said University, Port Fouad, 42526, Egypt.
Mahmoud ShabanDepartment of Electrical Engineering, College of Engineering, Qassim University, Buraydah, Saudi Arabia.
Abdulrahman AlrumayhDepartment of Mechanical Engineering, College of Engineering, Qassim University, Buraydah, Saudi Arabia.
Abdulrahman I AlateyahDepartment of Mechanical Engineering, College of Engineering, Qassim University, Buraydah, Saudi Arabia.
Fahad Nasser AlsunaydihDepartment of Electrical Engineering, College of Engineering, Qassim University, Buraydah, Saudi Arabia.
Majed O AlawadCenter of Excellence for Nanomaterials for Clean Energy Applications, King Abdulaziz City for Science and Technology (KACST), Riyadh, 12354, Saudi Arabia.
Yasmine El-TaybanyDepartment of Production Engineering and Mechanical Design, Port Said University, Port Fouad, 42526, Egypt.
Mohamed S El-AsfouryDepartment of Production Engineering and Mechanical Design, Port Said University, Port Fouad, 42526, Egypt.
Waleed H El-GaraihyDepartment of Mechanical Engineering, College of Engineering, Qassim University, Buraydah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Magnesium, valued for its lightweight, recyclability, and biocompatibility, faces challenges like its poor wear behavior and mechanical properties that limit its adaptation for a multitude of applications. In this study, various statistical analyses, and machine learning (ML) techniques were employed to optimize equal channel angular pressing (ECAP) process parameters for improving the wear behavior of Mg-3wt.% Zn-0.7 wt% Ca alloy. ECAP was conducted up to four passes via route Bc at 250 °C. Wear testing of both as-annealed (AA) and ECAP-processed alloys was performed using the dry ball-on-flat wear method under varying loads, speeds, and time. One pass (1P) and 4Bc-ECAP resulted in a notable uniform grain refinement of 86 % and 91 %, respectively, compared to the AA. X-ray diffraction (XRD) analysis confirmed a refined structure attributed to extensive dynamic recrystallization. Mechanical wear testing revealed a significant reduction in volume loss (VL), up to 56 % and 28.5 % after 1P and 4Bc samples, respectively, compared to the AA sample, supported by the observed texture intensity. The coefficient of friction (COF) stabilizes at 0.30-0.45, indicating low friction characteristics. Next, by adjusting wear load and speed through design of experiments (DOE), the wear output responses, VL and COF, were experimentally investigated. The output responses were predicted in the second step using ML, 3D response surface plots, and statistical analysis of variance (ANOVA). According to the regression model, the minimal VL was attained at a 5 N applied load. Also, the wear speed and VL at different passes are inversely proportional. On the other hand, the optimal COF was obtained at applied load about 2-3 N and 250 mm/s at different passes. The wear process variables were then optimized using different optimization techniques namely, genetic algorithm (GA), hybrid DOE-GA, and multi-objective genetic algorithm (MOGA) approaches.

Indexed as

ECAPMachine learningMg- ZX30 alloyMicrostructural evolutionOptimizationWear behavior

Identifiers

PMID39071718
PMCPMC11283115

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.