Surface roughness prediction by extreme learning machine constructed with abrasive water jet

In this study, the novel method based on extreme learning machine (ELM) is adapted to estimate roughness of surface machined with abrasive water jet. Roughness of surface is one of the main attributes of quality of products derived from water jet processing, and directly depends on the cutting parameters, such as thickness of the workpiece, abrasive flow rate, cutting speed and others. In this study, in order to provide data on influence of parameters on surface roughness, extensive experiments were carried out for different cutting regimes. Measured data were used to model the process by using ELM model. Estimation and prediction results of ELM model were compared with genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ELM approach in comparison with GP and ANN. Moreover, achieved results indicate that developed ELM models can be used with confidence for further work on formulating novel model predictive strategy for roughness of the surface machined with abrasive water jet. In conclusion, it is conclusively found that application of ELM is particularly promising as an alternative method to estimate the roughness of the surface machined with abrasive water jet.