Framework for the parametric optimization of powder mixed EDM using PCA: A case study

The proposed framework in this paper tries to explain that how to integrate the design of experiment (DOE) tools with the principal component analysis (PCA) and grey relational analysis (GRA) techniques, such that which gives the best optimal results for the multi-performance characteristics (MPC’S) i.e material removal rate (MRR) and surface roughness (SR) of powder mixed electrical discharge machining process of difficult-to-cut material. These approaches not only helps to obtain the optimal solution but also helps to handled the element of uncertainty fuzziness associated with the uncertain, multi-input and discrete data. Then theoretical prediction of experimental results shows that there is an improvement in the experimental results for the optimal parameters than the predicted results. Finally, an analysis of variance (ANOVA) was performed to identify the sensitivity of each input parameter which may affect the process performance, it can tells the percentage contribution of each parameter to the study.