在加工过程中工具寿命的估计-测试回归模型
Andrzej Paszkiewicz1, Grzegorz Piecuch2, Tomasz Żabiński2
1Department of Complex Systems, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland.
本研究确定了最佳的回归模型,用于预测切削工具在削中的寿命. 二维神经网络和支持向量回归 (SVR) 为智能制造应用提供了最佳的精度.
科学领域:
- 制造业 工程 制造工程
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 精确估计切割工具寿命对于高效的削操作至关重要.
- 来自加工过程的传感器数据需要复杂的模型来捕获依赖关系.
- 工业4.0倡议强调智能制造和工艺监控.
研究的目的:
- 确定最有效的回归模型来估计切削工具在削中的使用寿命.
- 评估支持向量回归 (SVR),决策树和神经网络的性能.
- 通过改善生产过程监控和减少损失,为智能制造做出贡献.
主要方法:
- 利用了来自哈斯VF-1机与振动传感器和贝克霍夫PLC数据收集器的实验数据.
- 应用回归模型,包括支持向量回归 (SVR),决策树和神经网络,以分析连续传感器数据.
- 基于R2参数和错误值评估模型性能.
主要成果:
- 使用LBFGS解决器的二维神经网络实现了最高的预测准确率93.9%.
- 支持向量回归 (SVR) 显示了可比性能,准确度为93.4%.
- 神经网络和SVR都在测试模型中提供了最低的错误值.
结论:
- 神经网络和SVR对于预测削过程中的切削工具寿命非常有效.
- 这些发现支持将先进的机器学习模型集成到工业4.0中,以增强制造业.
- 准确的工具寿命预测可以实现更好的服务规划,并最大限度地减少材料和工具损坏.
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