UNCERTAINTY QUANTIFICATION OF ELECTROCHEMICAL MACHINING PROCESS PERFORMANCE

Abstract

The SACE\ECDM process is most desirable to machine ‘difficult to machine’ materials like glass, ceramics etc. To machine non-conducting material, SACE fits in between process like USM, AJM (low cost, low energy) that are well suited to cut shallow cavities over worksurface and process like EBM, PBM, LBM (high cost, high energy) that can cut holes with high aspect ratios, in terms of process capabilities. ECDM process is a relatively new process and still a lot of work and research is needed to understand the process completely. No analytical or theoretical formula has been proposed yet to quantify MRR/roughness with the help of measurable process variables. An effort has been made to quantify the influence and inter-dependability of different process variables with the help of a directed acyclic graph (DAG). A Bayesian network has been prepared and the outcome are quantified in terms of probability distribution. Bayesian network is used in predictive modelling and descriptive analysis of real-life based phenomena to get a better understanding and hold on process outcome by altering different input variables. Cause and effect diagram has been used to establish relationship between dependent and independent variables based on the findings from reading journals and books. Due to the pandemic disruption, I was not able to perform as much work as needed for the completion of research on this topic, therefore I have prepared a comprehensive action plan and explanations to elaborate in details all the necessary steps during the process based on my findings.