用可解释机器学习预测铣削表面粗糙度，识别冗余传感器降本不降效。
Efficient Milling Quality Prediction with Explainable Machine Learning
- 基于随机森林回归和特征重要性分析建模。
- 移除部分传感器后预测精度不变，成本显著降低。
- 适合关注智能制造降本增效的工程人员。
本文提出一种可解释机器学习方法，用于预测铣削铝合2017A时的表面粗糙度。基于实际加工数据，采用随机森林回归模型并结合特征重要性分析。实验表明，通过识别冗余传感器（特别是法向切削力传感器）并移除，可在不牺牲预测精度的前提下显著降低系统成本。该方法展示了可解释机器学习在提升机械加工成本效益方面的潜力。
原文摘要 · Abstract (English)
This paper presents an explainable machine learning (ML) approach for predicting surface roughness in milling. Utilizing a dataset from milling aluminum alloy 2017A, the study employs random forest regression models and feature importance techniques. The key contributions include developing ML models that accurately predict various roughness values and identifying redundant sensors, particularly those for measuring normal cutting force. Our experiments show that removing certain sensors can reduce costs without sacrificing predictive accuracy, highlighting the potential of explainable machine learning to improve cost-effectiveness in machining.
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