从使用机器学习方法转向表面拓数据评估高频测量错误
Przemysław Podulka1, Monika Kulisz2, Katarzyna Antosz1
1Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959 Rzeszow, Poland.
Materials (Basel, Switzerland)
|April 13, 2024
概括
机器学习优化了无接触表面粗度测量的过. 支持矢量机器 (SVM) 准确地识别出最好的过器,在转向表面分析中减少高频噪声.
科学领域:
- 制造业 工程 制造工程
- 计量学 计量学 计量学
- 数据科学数据科学数据科学
背景情况:
- 表面地形评估对于工业制造控制至关重要.
- 无接触式测量技术提供速度,但容易受到环境噪音的影响,特别是高频振动.
- 准确的表面粗度评估对于产品质量和性能至关重要.
研究的目的:
- 研究机器学习方法,以减少无接触地表地形测量中的高频噪声.
- 优化数字过技术,以提高翻转表面的表面粗度评估.
- 为了比较各种机器学习模型在预测最佳过参数方面的有效性.
主要方法:
- 数字过器 (高斯回归,spline) 的应用,用于无接触地测量转面的粗度.
- 利用机器学习模型,包括神经网络,支持矢量机 (SVM) 和决策树.
- 在特定的加工条件下分析表面地形数据,以确定降噪策略.
主要成果:
- 高斯回归过器和线过器被认为在22.5μm的切断界面对降低噪声最有效.
- 支持矢量机器 (SVM) 在预测最佳过方法方面表现出卓越的性能.
- 该研究在确定有效的降噪技术方面取得了高准确度和灵敏度.
结论:
- 机器学习,特别是SVM,可以通过优化过来显著提高无接触表面粗度测量的准确性.
- 这些发现为减轻工业表面计量学中振动引起的错误提供了坚实的框架.
- 使用ML进行优化过,提高了用于制造过程控制的表面地形数据的可靠性.
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