Probabilistic investigation of geometric responses in Wire EDM machined complex-shaped profile: A machine learning based approach

Wire electric discharge machining (WEDM) has gained tremendous market share due to its potential to create complex profiles and ease in machining exotic materials. The inherent problem of wire lag, however, has a significant impact on the accuracy and precision of complex profiles in WEDM. Furthermore, if parametric uncertainties are not taken into account during machining of complex profiles, problems like geometrical inaccuracy and imprecision are likely to worsen. Hence, in the present study, we focused on the role of parametric uncertainties on geometrical parameters such as corner error (CE) and undercut (UC) of complicated profile. In this regard, the machine learning (ML) driven framework is proposed, wherein the Gaussian process regression model is integrated with the experimental responses gathered from the WEDM. The sample-space to train and validate the ML model is constructed by performing the WEDM for the experimental plan, which is designed on the basis of face-centered central composite design. The constructed ML model is rigorously tested and validated to ensure its predictive accuracy. The ML-based WEDM framework is further extended to perform data-driven uncertainty quantification and sensitivity analysis to reveal the likelihood of variations in geometrical precision due to inherent parametric uncertainties. The constructed model is further deployed for the data-driven sensitivity analysis and to reveal the probabilistic behavior of responses in the presence of parametric uncertainties. The results of this study will help to develop and expand the WEDM robust control technique.

Automated summary

Wire electric discharge machining (WEDM) is widely used due to its ability to create complex profiles and machine exotic materials. However, wire lag and parametric uncertainties can affect the accuracy and precision of complex profiles in WEDM. This study proposes a machine learning framework to understand the impact of parametric uncertainties and perform data-driven uncertainty quantification and sensitivity analysis.