Intelligent estimation of burning limits to aid in cylindrical grinding cycle planning

The drive towards productivity improvement in the competitive manufacturing environment has led to the increased use of artificial intelligence techno logies to optimise and control the machining process. This ensures the prod uction of high quality components at shorter cycle times. This paper presen ts a scheme for optimising the cycle time of a two-stage grinding cycle whi le grinding a series of pieces without intermediate wheel dressing. The sch eme presented uses a feedforward artificial neural network to estimate the burning limit under continuous grinding conditions, which is used as the wh eel life criterion.