Rubi Charles Sarala, Prakash Jayavelu Udaya, Juliyana Sunder Jebarose, Salunkhe Sachin, Cep Robert, Nasr Emad Abouel
Department of Physics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.
Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.
PLoS One. 2025 Jun 26;20(6):e0326086. doi: 10.1371/journal.pone.0326086. eCollection 2025.
Aluminium based composites with hybrid reinforcement hold significant potential to replace Al-alloys in a variety of automotive sectors where cheap cost, a significant ratio of strength to weight, and better wear resistance are required.
Stir casting was utilized to make aluminium matrix composites (AMCs) with 3%, 6%, and 9% of B4C/Fly ash particles. The wear was examined with various Sliding Speed, S (1 m/s, 1.5 m/s and 2 m/s), Sliding Distance, D (500 m, 1000 m and 1500 m), applied load, L (15 N, 30N and 45 N) and reinforcement %, R (3, 6 and 9%). Grey Relational Analysis was used to optimise the wear variables. Taguchi's L27 Orthogonal array (OA) was selected for this statistical approach in order to analyse responses like Specific wear rate (SWR) and Coefficient of Friction (CoF). Furthermore, analysis of variance (ANOVA) was utilized to investigate the influence of input parameters on wear behavior by choosing "smaller is better" feature.
Based on this study, the optimal values of S - 1.5 m/s, D - 500 m, L - 30 N, and R% - 9 wt% Hybrid (4.5% Fly ash and 4.5% B4C) are found to yield the lowest SWR and CoF. Wear rate of composite decreased with an increase in reinforcement particles. Increase in hardness was also the reason for decrease in wear rate. The responses have a narrow margin of error, according to confirmation studies. There exists a good agreement between them.
The research on LM6/B4C/fly ash composite fabrication using Grey Relational Analysis (GRA) has significantly contributed to the development of high-performance materials for wear-related applications. Through the optimization of wear parameters, GRA allows for the improvement of wear resistance, strength, and sustainability.
具有混合增强相的铝基复合材料在各种汽车领域具有巨大潜力，可替代铝合金，这些领域需要低成本、高强度重量比和更好的耐磨性。
采用搅拌铸造法制备含有3%、6%和9% B4C/粉煤灰颗粒的铝基复合材料（AMC）。在不同的滑动速度S（1 m/s、1.5 m/s和2 m/s）、滑动距离D（500 m、1000 m和1500 m）、施加载荷L（15 N、30 N和45 N）以及增强相含量R（3%、6%和9%）条件下对磨损情况进行研究。使用灰色关联分析来优化磨损变量。为此统计方法选择了田口的L27正交阵列（OA），以分析诸如比磨损率（SWR）和摩擦系数（CoF）等响应。此外，通过选择“越小越好”的特性，利用方差分析（ANOVA）来研究输入参数对磨损行为的影响。
基于本研究，发现S - 1.5 m/s、D - 500 m、L - 30 N和R% - 9 wt%混合（4.5%粉煤灰和4.5%B4C）的最佳值可产生最低的SWR和CoF。复合材料的磨损率随着增强颗粒的增加而降低。硬度增加也是磨损率降低的原因。根据验证研究，响应的误差范围很窄。它们之间存在良好的一致性。
使用灰色关联分析（GRA）对LM6/B₄C/粉煤灰复合材料制造的研究对与磨损相关应用的高性能材料的开发做出了重大贡献。通过优化磨损参数，GRA有助于提高耐磨性、强度和可持续性。
