Efficient Milling Quality Prediction with Explainable Machine Learning
This work addresses cost reduction in machining processes for manufacturers, but it is incremental as it applies existing methods to a specific dataset.
The paper tackles predicting surface roughness in milling by using explainable machine learning, achieving accurate predictions and identifying redundant sensors like those for normal cutting force, which can reduce costs without losing accuracy.
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.