Estimation of the Diamond Disk Wear in Stone Cutting

Disk wear play a major role in the economic aspects of stone cutting process. Wear behavior of the disk is related to the machining conditions and this relation is to be recognized. In this survey, cutting of the granite stone by diamond disk under different machining conditions has been studied. In order to fulfill the detection requirements and optimize the process, an error back propagation neural network is implemented to estimate the interconnection of the disk wear to cutting condition. Different number of the layers and nodes in the artificial neural network has been examined to reach to an optimal structure. Training data is provided through a laboratory rig which simulates realistic cutting condition.The final structure approximates the training data with an average accuracy of 91%. It has been also found that disk wear is proportional to the depth of cut and cutting speed while by increasing the feed rate, it would be decreased.

کلیدواژه ها:

Stone cutting ، diamond disk ، wear ، neural network