Indirect tool wear measurement and prediction using multi-sensor data fusion and neural network during machining

Abstract

The quality produced of the machined workpiece is affected by various machining parameters including the tool wear. The condition of tool can be monitored by analyzing various machining responses through sensors. The use of multiple sensors allows for a comparison of data and information gathered from various sources, assisting in deciding the state of the tool and the workpiece. In current work, multiple sensors are attached to a lathe during the machining of AISI 4140. This paper presents a multi-sensor data fusion method for flank wear measurement and prediction using various parameters such as vibration, power, temperature, force, and surface roughness. Dry machining is performed by using PVD coated tool insert, and the flank wear is analyzed. Experiments are performed with a full factorial experimental design L27 orthogonal array. Taguchi approach has been utilized to evaluate the effect of machining parameters on tool wear. Also, the tool wear is predicted through an artificial neural network (ANN) model, and the results are compared with manually measured data. The results showcased the effectiveness of the proposed tool wear prediction model with the least measurement error. The work presents a novel neural network approach for the accurate prediction of tool wear and hence eliminates the necessity of manual measurement of tool wear and leading towards the increased productivity and quality of the work part.