使用ML方法分析曲滚动参数对C60钢制品表面粗度的影响
1Department of Metal Forming, Mechanical Engineering Faculty, Lublin University of Technology, 20-618 Lublin, Poland.
这项研究优化了C60钢的斜,通过使用机器学习来根据工艺参数预测表面粗度. 随机森林模型有效地确定了改善产品质量的最佳设置.
科学领域:
- 材料科学与工程 材料科学与工程
- 制造过程 制造过程 制造过程
- 计算材料科学科学 计算材料科学
背景情况:
- 轮是用于轴对称产品的金属成型工艺.
- 表面粗度是造钢件的关键质量指标.
- 优化工艺参数是提高C60级钢制造产品质量的关键.
研究的目的:
- 调查斜参数对C60级钢的表面粗度的影响.
- 开发一个可预测的机器学习模型,用于表面粗度.
- 为了优化斜滚动过程,提高表面质量.
主要方法:
- 用不同的成形角度,斜角,减小比率和下速度对斜滚动进行实验性调查.
- 数字模拟以支持实验数据和模型开发.
- 使用SHAP分析开发和评估机器学习回归模型 (随机森林).
主要成果:
- 随机森林模型根据过程参数准确预测表面粗度 (Ra).
- 确定了影响表面粗度的关键参数.
- 该模型基于MAE,RMSE和R2指标证明了高效率.
结论:
- 机器学习,特别是随机森林模型,提供了一种强大的工具,用于优化斜滚动过程.
- 准确预测表面粗度可以更好地控制产品质量.
- 这种方法有助于为增强的C60钢件选择最佳的工艺参数.
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