EDM process optimization via predicting a controller model

Purpose: Electro-discharge machining is an important manufacture technology in machining difficult-to-cut materials and to shape complicated contours and profiles with high material removal rate, low tool wear and good tolerances. Design/methodology/approach: In machining of carbon-based materials such as WC-Co and non-oxide ceramics which are growingly used, the complexity and non-linear nature of EDM is a serious problem. EDM is the best and nearly the only non-conventional method for machining of these kind of materials, but it shows high instability and tendency to arcing, compared with machining of steel. Occurrence of instability phenomenon due to the different input setting up parameters make the modeling of EDM process impossible with conventional methods. To achieve instantaneous data from machining condition, the new method of fuzzy analysis of single machining pulses and computing the magnitude of system condition in the form of a real number between 0 and 1, has been used. Findings: Some tests with WC-Co material are carried out and finally, the results of implementation of control system on a sinking ED machine and an EDM system that has been set with an expert user, has been compared. Practical implications: The optimization and control of EDM process using the neural model predictive control method. A genetic algorithm has also been employed to optimize the input parameters and to create the optimized setting collection of process. Originality/value: The testing results from ED machining of WC-Co confirms the capability of the system of predictive controller model based on neural network with 32.8% efficiency increasing in stock removal rate.