使用Zernike环形时刻和机器学习分类的轮胎模式形状分类.
Sudharsan Parthasarathy1, Junhyeon Seo2, Rakesh K Kapania1
1Kevin T. Crofton Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA.
Scientific reports
|April 25, 2024
概括
本研究介绍了一种机器学习 (ML) 框架,用于自动分类辐射轮胎模式形状. 这一进步有助于开发数字双胞胎,以提高轮胎性能和安全性,减少手动解释的需求.
科学领域:
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 轮胎模式形状分类对于优化驾驶性能和安全至关重要.
- 目前用于模式分类的方法是劳动密集型的,需要大量的手动解释.
- 开发精确的数字双胞胎用于轮胎分析需要高效的模式形状分类.
研究的目的:
- 使用已识别的特征建立一个分类轮胎模式形状的数据库.
- 开发基于机器学习 (ML) 的替代模型,用于自动化轮胎模式形状分类.
- 为了消除对解释轮胎模式的手动努力的需要.
主要方法:
- 使用Zernike环形时刻描述器 (ZAMD) 来创建轮胎模式形状的特征图.
- 使用模式保证标准 (MAC) 与ZAMD的相关值用于模式分类和标签.
- 实施监督学习算法,包括决策树,随机森林和XGBoost,用于替代模型开发.
主要成果:
- 成功创建了一个分类轮胎模式形状的数据库.
- 开发了一个基于ML的替代模型,能够对轮胎模式形状进行分类.
- 与表现最好的模型实现了高分类准确率99.5%,消除了手工劳动.
结论:
- 拟议的ML框架有效地自动化了辐射轮胎模式形状的分类.
- 这种自动化方法显著提高了用于轮胎性能分析的数字双胞胎的开发.
- 这项研究展示了一种非常准确和高效的方法来分类轮胎模式形状,提高安全性和性能预测.
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