镁,因其轻巧而受到重视,可回收性,和生物相容性,面临的挑战,如其不良的磨损行为和机械性能,限制了其适应多种应用。在这项研究中,各种统计分析,采用机器学习(ML)技术优化等通道角压制(ECAP)工艺参数,以改善Mg-3wt的磨损行为。%Zn-0.7wt%Ca合金。ECAP通过路线Bc在250°C下进行多达四次通过。退火(AA)和ECAP处理的合金的磨损测试均使用干式球在平面磨损方法在变化的载荷下进行,速度,和时间。一次通过(1P)和4Bc-ECAP导致86%和91%的显着均匀晶粒细化,分别,与AA相比。X射线衍射(XRD)分析证实了归因于广泛的动态再结晶的精细结构。机械磨损测试显示体积损失(VL)显著减少,1P和4Bc样品后高达56%和28.5%,分别,与AA样品相比,由观察到的纹理强度支持。摩擦系数(COF)稳定在0.30-0.45,表明低摩擦特性。接下来,通过实验设计(DOE)调整磨损载荷和速度,磨损输出响应,VL和COF,进行了实验研究。在第二步中使用ML预测输出响应,三维响应面图,和方差分析(ANOVA)。根据回归模型,在施加5N的载荷下达到最小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,在施加约2-3N和250mm/s的载荷下获得最佳COF。然后使用不同的优化技术优化磨损过程变量,即遗传算法(GA),混合DOE-GA,和多目标遗传算法(MOGA)方法。
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.