A New Architecture Paradigm for Tool Wear Prediction during AISI 9840 Drilling Operation

Abstract

In conventional machining processes based on the chip removal mechanism, the progressive wear of the tool determines a change of the geometric characteristics of the cutting edge. Tool wear is a complex phenomenon and related tool life depends on several factors, such as cutting parameters, lubrication, and tool-workpiece relative trajectories. Tool wear progression affects the quality of the machined parts, making the tool replacement necessary even before its breakage. Moreover, in industrial practice, tool replacement cannot depend on the subjectivity of the operator, thus, the definition of an optimized strategy for cutting tool wear monitoring before tool failure is mandatory. This work compares Random Forest and Neural Network models for predicting tool wear in drilling. For the development of predictive models, tool life tests were performed by drilling through holes on AISI 9840 steel parts, with a coated tungsten carbide drill of 8 mm of diameter and by using constant cutting parameters. Flank wear of the tool was monitored. A set of statistical features computed from the vibration signals, the acoustic emission signals, the power signals and the torque signals constitute the input of the algorithms. The classification accuracy was 88% and 91% for Neural Network and Random Forest models respectively. In order to correctly define the tool replacement policy during production, the developed Random Forest model has been implemented in an industry, through production management software, achieving promising results.