Abstract
Wire electrode discharge machining (WEDM) is an accurate but an expensive and time-consuming process. In order to establish a stable connection between input and output variables, implementation of soft computing techniques can be useful. Therefore, the current study focuses on comparing adaptive neuro-fuzzy inference system (ANFIS)-based subtractive clustering algorithm with numerous input combinations as well as multivariate regression models in order to simulate and map the output variables with the process parameters used during experimentations, namely pulse-on time (Ton), servo voltage (Sv), wire feed (Wf), and wire tension (Wt). Results show that ANFIS models have the ability to estimate the edge roughness (Er) and kerf width (Kw) more accurately with 96.2 and 97.3% accuracy. ANFIS model is more reliable, accurate, and productive as it uses the learning of neural networks to predict. Also, the developed model has been used to study and explain the effect of various input variables upon the quality of machining. High pulse-on time directly decreases the quality increasing the edge roughness and kerf width which are both undesirable. Low wire feed has shown to decrease both the response parameters regardless of other input parameters. Wire tension has shown much less significant effect as compared to the other three variables.
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Chopra, K., Payla, A., Kaur, G., Mussada, E.K. (2019). ANFIS-Based Subtractive Clustering Algorithm for Prediction of Response Parameters in WEDM of EN-31. In: Narayanan, R., Joshi, S., Dixit, U. (eds) Advances in Computational Methods in Manufacturing. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-32-9072-3_43
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