Using of Artificial Neural Networks to Predict Drill Wear in machining processes

In machining operations a hard tool is engaged with work piece along with process. Tool is harder than work piece. However, tool wear occurrence in machining processes is inevitable. Tool wear will results in scraped parts and also it makes tool to weaken and then a tool failer will happen in the end. Therefore, an operator is needed to follow the process and change the tool when it is going to break. But this is a serious problem against automation. To create an automation system, we need to develop a monitoring system to predict tool wear rate by on-line and substitute it with an operator. In this paper by using of a wear model and experimental data and also motor current block diagram ,tool wear rate in drilling process will be predicted .To investigate the results, neural network method is used .The results compared with the real data show that the neural network results have a close fitness with the real data.