Tool condition monitoring (TCM) using neural networks

Cutting tool conditions significantly influence the quality and precision of the machined parts. With the ability to monitor the cutting tool condition, machining quality can be maintained and catastrophic failure can be eliminated. In this manner, production automation can be achieved. Therefore, tool condition monitoring (TCM) is extremely important to achieve high quality and automation of boring processes. Neural networks have been widely used in condition monitoring. Counterpropagation neural networks (CPNs), which are based on competitive learning, have been utilized in TCM in this research for high quality and automated boring. The inputs of the CPNs were the indexes acquired from three-axis cutting force data. The output was either the tool state or the value of tool wear. Seventy CPN network structures have been utilized for both real-time recognition and real-time measurements. The performance of the CPNs for TCM depends on the network structures. The results of this research are exceedingly successful. Real-time recognition of tool states showed excellent results, using a 2 x 30 x 1 CPN, of being able to predict tool states real time with 100 % accuracy. Real-time measurements can achieve a minimum error of 8.46 % using a 3x69x1 CPN, which is sufficient for continuous assessment of the tool degradation. Control actions can be taken to stop the boring process in order to avoid catastrophic failure and to enhance quality and automation of the boring process.