A response surface methodology based approach to machining processes: modelling and quality of the models

Various techniques for developing prediction models for various machining performance measures such as surface roughness/surface integrity, cutting force, tool life/tool wear etc in machining processes are available. These methods include, but are not limited to, analytical, numerical, empirical, and artificial intelligence (AI) based methods. While empirical modelling often employs the use of response surface methodology (RSM), however, proper understanding must be established regarding RSM-based models with respect to their development, validation and acceptability. Therefore, the general framework for developing RSM-based prediction models and testing their quality are discussed in this paper. This is followed by a practical surfacebroughness (Rt) model developed for precision grinding of silicon, a machining process that is very difficult to model. The result shows that the procedural modelling frameworks work well for the Rt developed model