Mathematical modelling of burr height of the drilling process using a statistical-based multi-gene genetic programming approach

Drilling is one of the important machining processes performed extensively in production industry. Literature emphasises that the output process parameters such as burr height, surface roughness, strength, etc. are related to and can be improved by the appropriate settings of the input process parameters. Recently, researchers have applied well-known computational intelligence methods such as regression analysis, artificial neural networks (ANNs), support vector regression (SVR), etc. in the prediction of performance characteristics of the drilling process. Alternatively, an evolutionary approach of multi-gene genetic programming (MGGP) that evolves the model structure and its coefficients automatically can be applied. Despite of being widely applied, MGGP has the limitation for producing models that over-fit on the testing data. One of the reasons attributed for this behaviour is the over-size of the evolved models. Therefore, a statistical-based MGGP (S-MGGP) approach is proposed and applied to the burr height data obtained from the drilling of AISI 316L stainless steel. In this proposed approach, Bayesian information criterion is embedded in its paradigm, which punishes the fitness of larger size models. The performance of S-MGGP and ANN models is found to be better than those of the standardised MGGP and SVR. Further, the parametric and sensitivity analysis conducted validates the robustness of our proposed model and is proved to capture the dynamics of the drilling phenomenon by unveiling dominant input process parameters and the hidden non-linear relationships.