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Off-line process control improves process efficiency. This paper examines the influence of three cutting parameters on the surface roughness, tool wear and the cutting force components in face milling as part of the off-line process control. The experiments were carried out in order to define model for process planning. Cutting speed, feed per tooth and depth of cut were taken as influential factors. Two modeling methodologies, namely regression analysis and neural networks have been applied to experimentally determined data. Results obtained by the models have been compared. Both models have the relative prediction error below 10%. The research has shown that when training dataset is small neural networks modeling methodologies are comparable with regression analysis methodology and furthermore it can even offer better result, in this case average relative error of 3,35%. Advantages of off-line process control which utilizes process models by using this two modeling methodologies were explained in theory.

Modeling of the Influence of Cutting Parameters on the Surface Roughness, Tool Wear and Cutting Force in Face Milling in Off-Line Process Control