Intelligent Monitoring and Measurement of Tool Wear for the Turning of Stainless Steel Parts

Abstract

Cutting forces were used as indices in this research for the monitoring and measurement of tool wear during the turning of stainless steel parts. Virtual instrumentation was applied to extract the fourteen features from cutting force signals. The best combination of features, which would be used as input vectors for on-line monitoring and measurement, was selected by using a Sequential Forward Search (SFS) algorithm. Adaptive neuro-fuzzy inference systems (ANFIS) were used for the recognition of tool wear. The tool conditions, which are either usable or worn out, are the outputs for on-line monitoring. The outputs for on-line measurement are estimated values of tool wear. When ANFIS was applied, three features were needed for the monitoring of tool wear. They are the average of radial force, the average of tangential force, and the skewness of tangential force. For on-line measurement, four features were used as inputs. The input vector includes the average of radial force, the average of tangential force, the skewness of tangential force, and the kurtosis of longitudinal force. For the on-line monitoring of turning tool conditions, a 7 × 2 ANFIS can achieve a success rate of higher than 96% to distinguish usable tools from worn-out tools. For the on-line measurement of tool wear, the average flank wear estimation error is below 8.9% using a 3 × 3 ANFIS.