Prediction of Tool Wear Using Artificial Neural Networks during Turning of Hardened Steel

摘要: The ability to effectively predict tool wear during machining is an extremely important part of diagnostics that results in changing the at relevant time. Effective assessment rate increases efficiency process and makes it possible replace before catastrophic occurs. In this context, value effectiveness predicting turning hardened steel using artificial neural networks, multilayer perceptron (MLP), was checked. Cutting forces acceleration mechanical vibrations were used monitor process. As a result analysis suitability individual physical phenomena monitoring assessed.