Tool wear and remaining useful life prediction in micro-milling along complex tool paths using neural networks

Abstract

Monitoring tool wear state and prediction of the remaining useful life in micro-milling help avoid down time due to unalarmed failure of the cutting tool. In this regard, investigations based on experiments, and data-based models, along straight tool paths have been reported in the literature. However, industrial applications often require machining along complex tool paths, where the tool failure possibilities increase multifold, have not been studied elaborately in the literature. Therefore, the main objective of this work is to develop a data-based model, which can predict the tool wear state, and remaining useful life of the tool, considering the effect of tool path complexities due to varying tool path radius in micro-milling. The data for developing the model were generated by performing micro-milling experiments under different processing parameters, such as feed, depth of cut, spindle rotation speed, and tool path radius. The tool images were captured in-situ without removing the tool from the spindle, and image binarization and alignment operations were performed to extract corresponding cutting edge wear features, such as diameter reduction as well as the wear of individual cutting edges. The tool wear classification criteria were defined to categorize the tool condition in three regions: initial wear, steady-state wear, and critical wear. To capture and model the complex mechanism of micro tool wear and failure, artificial neural networks, as well as deep belief networks, were implemented to predict the wear state as well as the remaining-useful-life (RUL) of the tools. It was found that the wear rate increased with increasing tool path radii and the larger radii could lead to catastrophic tool failure. The neural-network models gave 93–99% accuracy for the prediction of wear state classification and RUL.