El-Sanabary Samar, Kouta Hanan, Shaban Mahmoud, Alrumayh Abdulrahman, Alateyah Abdulrahman I, Alsunaydih Fahad Nasser, Alawad Majed O, El-Taybany Yasmine, El-Asfoury Mohamed S, El-Garaihy Waleed H
Department of Production Engineering and Mechanical Design, Port Said University, Port Fouad, 42526, Egypt.
Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah, Saudi Arabia.
Heliyon. 2024 Jul 2;10(13):e33967. doi: 10.1016/j.heliyon.2024.e33967. eCollection 2024 Jul 15.
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.
镁因其轻质、可回收性和生物相容性而备受重视，但它面临着诸如磨损性能差和机械性能不佳等挑战，这些限制了其在众多应用中的适用性。在本研究中，采用了各种统计分析和机器学习（ML）技术来优化等通道转角挤压（ECAP）工艺参数，以改善Mg-3wt.%Zn-0.7wt%Ca合金的磨损性能。ECAP在250°C下通过Bc路线进行了多达四道次。使用干滑动球-平面磨损方法在不同载荷、速度和时间下对退火态（AA）和经过ECAP处理的合金进行了磨损测试。与AA相比，一道次（1P）和4Bc-ECAP分别使晶粒显著均匀细化了86%和91%。X射线衍射（XRD）分析证实了由于广泛的动态再结晶而形成的细化结构。机械磨损测试表明，与AA样品相比，1P和4Bc样品的体积损失（VL）分别显著降低了56%和28.5%，这得到了观察到的织构强度的支持。摩擦系数（COF）稳定在0.30 - 0.45，表明具有低摩擦特性。接下来，通过实验设计（DOE）调整磨损载荷和速度，对磨损输出响应VL和COF进行了实验研究。在第二步中，使用ML、三维响应曲面图和方差统计分析（ANOVA）对输出响应进行了预测。根据回归模型，在施加5N载荷时达到了最小VL。此外，不同道次的磨损速度和VL成反比。另一方面，在不同道次下，施加约2 - 3N的载荷和250mm/s的速度时获得了最佳COF。然后使用不同的优化技术，即遗传算法（GA）、混合DOE - GA和多目标遗传算法（MOGA）方法对磨损工艺变量进行了优化。
