Purpose

The purpose of this study is to present a novel approach for the evaluation of tribological properties of brake friction materials (BFM).

Design/methodology/approach

In this study, a BFM was newly formulated with 16 different ingredients and produced using an industrial hot compression molding process. Experimentation was carried out on the brake tester, which was developed for this purpose according to SAE J661 standards. The braking applications, sliding speed and braking pressure were considered as performance parameters, whereas coefficient of friction (CoF) and wear rate as output parameters. The influence of the performance parameters on the output was studied using response surface plots. Analysis of variance and regression analysis was accomplished for post-experimental evaluation of results. Multi-criteria decision-making (MCDM) and multi-objective genetic algorithm (MOGA) were applied for estimating the most critical performance parameter combination to evaluate the BFM.

Findings

The present experimental model was significant and effectively used to predict the performance. MCDM generates the optimal values for the parameters braking applications, braking pressure (Bar) and sliding speed (rpm) as 1000, 30 and 915, whereas MOGA as 1008, 10.503 and 462.8202, respectively.

Originality/value

An efficient model for performance evaluation of the BFM considering maximum CoF and minimum wear rate was experimentally presented and statistically verified. Also, the two multi-objective optimization methodologies were implemented and compared. A comparison between the results of MCDM and MOGA reveals that MOGA yields 30% better results than MCDM.

中文翻译：

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基于多目标优化方法的制动摩擦材料摩擦学性能评价

目的

本研究的目的是提出一种评估制动摩擦材料 (BFM) 摩擦学性能的新方法。

设计/方法/方法

在这项研究中，BFM 是用 16 种不同成分新配制的，并使用工业热压成型工艺生产。在制动测试仪上进行了实验，该测试仪是根据 SAE J661 标准为此目的开发的。制动应用、滑动速度和制动压力被视为性能参数，而摩擦系数 (CoF) 和磨损率被视为输出参数。使用响应面图研究了性能参数对输出的影响。完成方差分析和回归分析以对结果进行实验后评估。应用多标准决策（MCDM）和多目标遗传算法（MOGA）来估计最关键的性能参数组合来评估 BFM。

发现

本实验模型具有重要意义，可有效地用于预测性能。MCDM 生成的参数制动应用、制动压力 (Bar) 和滑动速度 (rpm) 的最佳值分别为 1000、30 和 915，而 MOGA 分别为 1008、10.503 和 462.8202。

原创性/价值

通过实验提出并统计验证了考虑最大 CoF 和最小磨损率的 BFM 性能评估的有效模型。此外，还实施并比较了两种多目标优化方法。MCDM 和 MOGA 的结果之间的比较表明，MOGA 产生的结果比 MCDM 好 30%。