Abstract
AbstractLaser beam machining is a non-traditional manufacturing method which melts, vaporizes and thermally changes the characteristics of the material by focusing a monochromatic coherent light beam on the workpiece. This research article focuses on studying the laser beam machining of mild steel material by varying the input parameters like cutting speed, gas pressure and laser power. The parameters were varied in three levels, and totally, nine experiments were conducted based on orthogonal array. The output parameters such as material removal rate and surface roughness were analyzed, and the experimental results revealed that cutting speed and laser power were the most dominating parameter for getting good surface refinish and better material removal rate, respectively. The interaction effects of the parameters were also significant. The fuzzy c-means (FCM) clustering and fuzzy subtractive clustering (FSC)-based fuzzy inference systems are modeled to predict the output characteristics material removal rate (MRR) and surface roughness. It is found that the fuzzy subtractive clustering-based fuzzy inference systems are very effective in predicting the outputs.KeywordsLaser beam machiningSurface finishMaterial removal rateFuzzy subtractive clusteringFuzzy c-means
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