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在使用Kohonen自组织地图的条形绘图测试中基于表面拓的摩擦系数的分类

Krzysztof Szwajka¹, Tomasz Trzepieciński², Marek Szewczyk¹

¹Department of Integrated Design and Tribology Systems, Faculty of Mechanics and Technology, Rzeszów University of Technology, ul. Kwiatkowskiego 4, 37-450 Stalowa Wola, Poland.

Materials (Basel, Switzerland)

|July 12, 2025

概括

本研究引入了一种人工智能方法,使用表面粗度数据对金属板成型中的摩擦系数 (CoF) 进行分类. 无监督方法实现了98%的准确性,提高了质量并减少了缺陷.

科学领域:

材料科学与工程材料科学与工程
人工智能和机器学习

背景情况:

精确的摩擦系数 (CoF) 监测对于产品质量和工艺效率至关重要.
传统的Cof监测方法难以识别对摩擦变化敏感的力信号特征.
在SMF期间的CoF变化受到不断变化的接触条件,表面地形,接触负荷和材料行为的影响.

研究的目的:

开发一种新的方法,用于在条纹拉动试验中对DP780钢板中的CoF进行分类.
应用无监督的人工智能 (AI) 技术来处理 CoF 分类的不平衡数据.
通过分析摩擦参数和金属板表面粗度来识别摩擦过程中的干扰.

主要方法:

利用无监督的人工智能,特别是Kohonen自组织地图 (SOM),用于CoF分类.
收集了表面地形学参数,包括Sq粗度参数和摩擦测试参数.
在不同的滑条件,接触压力和滑动速度下进行了带拉动试验.

主要成果:

在摩擦系数 (CoF) 中达到高达98%的高分类精度.
证明了使用SOM用于对不平衡数据的CoF进行分类的有效性.
展示了只使用Sq表面粗度参数和选择的摩擦试验参数,CoF分类是可行的.

结论:

提出的无监督人工智能方法有效地将CoF分类为板材成型工艺中的CoF.
表面地形学参数,特别是Sq,结合摩擦试验数据,是CoF分类的可靠指标.
这种方法为SMF中的摩擦监测和控制提供了一个有希望的解决方案,从而提高了产品质量和流程优化.

关键词:

深度绘制的深度绘制摩擦摩擦摩擦摩擦摩擦金成型金成型金成型钢板钢板是什么意思钢板钢板是什么意思表面的粗度表面的粗度

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