Abstract
Abstract
Tool wear monitoring is one of the most crucial and inevitable processes in present-day manufacturing systems. With the growth of unmanned factories, the need for on-line monitoring systems is well recognized. Artificial intelligence techniques such as artificial neural networks, fuzzy logic and the neuro-fuzzy technique have proved their potential in monitoring the manufacturing processes. In shop-floor control, the condition of the cutting tool is of more concern than the actual tool wear value but, in research activities, the estimation of the actual value of the tool wear occupies a prominent place. The present work is concerned with the assessment of the tool condition and also the estimation of the tool wear value. In the present paper, artificial intelligence techniques are applied to estimate the tool condition and tool wear value on line. Kohonen's self-organizing map is applied in neural networks for estimating the tool condition. Fuzzy logic and the neuro-fuzzy technique are implemented by triangular membership functions. To assess the tool wear value, a back-propagation neural network is applied. In fuzzy logic and neuro-fuzzy techniques, the centroid method of defuzzification is applied to obtain the flank wear value. Experimental data are generated by machining EN-8 steel with a high-speed steel cutting tool. The obtained data are used to train and test the networks. To make the monitoring system user friendly, a user interface is developed using Microsoft Visual Basic 6.0.