Fuzzy possibility regression integrated with fuzzy adaptive neural network for predicting and optimizing electrical discharge machining parameters

An electrical discharge machining (EDM) is one of the special production methods that are widely used in moldings, repairs and production of specific industrial components. Due to extensive production costs, optimal machining specifications are significant. Machining specifications are effective on output quality and thus attract more customers leading to higher profits. In this study, the impact of EDM parameters on surface roughness, material removal rate and electrode corrosion percentage have been investigated. In order to consider uncertainty of real production environments, the fuzzy theory is employed. Also, using the design of experiment (DOE) parameters calibration is performed and mathematical programming approach is applied for optimization purpose. The relationship between the machining parameters and the output process specification is examined by a fuzzy possibility regression model. Then, the mathematical relation of exact inputs and fuzzy outputs of the EDM process are extracted. The effectiveness of the three outputs is evaluated by interfacing models and fuzzy hypothesis testing. To determine the optimal levels of each output, a fuzzy adaptive neural network is used and appropriate models are prepared to be adapted with a fitted model of fuzzy possibility regression for comparison purposes. Validation tests imply the effectiveness of the proposed method. The integrated model is implemented in real case study. The results show that, fitted models can predict the material removal rate, surface fineness, and corrosion percentage of the electrode. The prediction accuracy of the proposed method is shown in comparison with the optimal fuzzy adaptive neural network outputs considering error value. Also, the proposed method is successful in identifying the optimal process parameters for EDM with reliable accuracy. The proposed integrated prediction and optimization model can be used as a calibration decision support in production systems to handle dynamic data structures and provide real time machining specifications to increase the output quality.