Machine Learning of Surface Layer Property Prediction for Milling Operations

Tool wear and cutting parameters have a significant effect on the surface layer properties in milling. Since relation between tool wear, parameters, is mostly unknown, latter cannot be controlled during production may vary from part to as progresses. To account for this uncertainty prevent premature failure, components often need oversized or adjusted subsequent manufacturing processes. Several approaches been made obtain models that predict induced by However, those calibrated with considerable number of experimental trials. As trials are time-consuming measurements laborious, no industrial applications realized. Complex one major drawback. They re-parameterized soon process characteristics change. Therefore, manual parameterization does not appear feasible approach application. A highly automated machine learning presented paper. The amount obtained measurement data allows fundamental analysis approach, which paves way further developments.