Performance evaluation of chip breaker utilizing neural network

The continuous chip generated during turning operation deteriorates the workpiece precision and causes safety hazards for the operator. Appropriate control of the chip shape becomes a very important task for maintaining reliable machining process. In particular, effective chip control is necessary for a CNC machine or automatic production system because any failure in chip control can cause the lowering in productivity and the worsening in operation due to frequent stop. Therefore, a grooved chip breaker has been widely used for obtaining reliable discontinuous chips. In general, in order to develop a new cutting insert with a chip breaker, extensive time, research, and expense are required because several processes such as forming, sintering, grinding, and coating of products as well as different evaluation tests are necessary. In this study, the performance of commercial chip breakers was evaluated using a neural network that was trained through a back propagation algorithm. Important form elements (depth of cut, land, breadth, and radius) that directly influenced the chip formation were chosen among commercial chip breakers, and were used as input values of the neural network. As a result, the performance evaluation method has been developed and applied to commercial tools, which resulted in excellent performance. If the training data in the neural network is collected with greater consideration given to the effect of cutting conditions and the performance of chip breakers, it can be used for the design of chip breakers in the future.