Abstract
Abstract Machining is done to obtain dimensional accuracy and surface finish; several automated systems are available for the evaluation of dimensional accuracy, whereas surface finish evaluation systems are rare. Face milling operation is performed at diverse cutting parameters (speed, feed rate, and depth of cut) on aluminum 6101 alloys. In this work, a machine vision system is developed for the evaluation of surface finish. Curvelet transforms based advanced image processing techniques are used to extract texture features from machined surface captured images. An ANN-PSO model is developed to map the texture feature and the measured surface finish. The model evaluated the surface finish accurately for given texture features. Machine vision systems as such are non-tangible; scratch protective, less time consuming, cost-effective, and productive.
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