<![CDATA[At present, the life expectancy of tool has become a vital aspect in the manufacturing industries, especially where materials with high hardness have more importance. Nowadays hard steel has been widely used for manufacturing of commercial parts in military aircrafts, car systems and hydraulic tools, etc. Manufacturing industries, mainly concentrates on mass production of the products with precision and accuracy. In such cases the continuous machining may weaken the tool causing tool wear which ultimately affects the quality with production rate. To avoid such an unwanted scenario, different tool wear prediction techniques have been introduced which uses cutting force signals or average chip-tool interface temperature or surface roughness signals, etc. According to the literature, different techniques are available for the pre-judgment of the tool wear that shows variation in the accuracy of prediction. Sensor fusion technique can be employed to overcome this problem by combining data from different sensors intelligently to improve the process. Sensor fusion uses this combined data to correct deficiencies of individual sensors and predict the tool wear accurately which compensate for the sudden breakage of the tool in real life applications. In this paper, a new approach of tool wear prediction has been introduced to correlate different available sensor by using sensor fusion technique. Also, a mathematical approach has been derived for the sensor fusion purpose. The experimentation has been carried out using coated carbide inserts on 55 HRC hardened steel.]]>
