Browsing by Author "Wan Azhar Wan Ahmad"

Micro Electrical Discharge Machining (μEDM) is one of the most demanding manufacturing processes available today. The selection of μEDM parameters remains a challenge since it is frequently based on machinist intuition and heuristic approaches. In recent years, soft computing and artificial intelligence have been used to model and predict the μEDM machining process. However, artificial intelligence has not been established for predicting μEDM performances based on material properties. Therefore, this research proposed a model that considers the material properties, such as thermal conductivity, melting point, and electrical resistivity. Since μEDM is a non-linear and stochastic process, Coactive Neuro-Fuzzy Inference Systems (CANFIS) was proposed to model and predict the multiple μEDM performances on various materials. The material properties, feed rate, capacitance, and gap voltage are the input parameters in a three-level design based on a full factorial experiment. The CANFIS model can accurately predict the material removal rate (MRR), total discharge pulse, overcut, and taperness in a single model. The mean average percentage error (MAPE) from the model prediction for test dataset of various outputs such as MRR, total discharge pulse, overcut and taper angle were found to be 9.5% (90.5% accuracy), 8.9% (91.1% accuracy),16.9% (83.1% accuracy) and 15.7% (84.3% accuracy) respectively. This research proposes a novel approach in modelling and predicting μEDM performances by considering workpiece’s materials using artificial intelligence.