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Surface roughness inspection in robotic abrasive belt machining process is an off-line operation which is time-consuming. An in-process multisensor integration technique comprising of force, accelerometer and acoustic emission sensor was developed to predict state of the surface roughness during machining. Time and frequency-domain features extracted from sensor signals were correlated with the corresponding surface roughness to train the Support vector machines (SVM's) in Matlab toolbox and adoi:10.1016/j.procir.2016.03.126 fatcat:3utwp7plozel3hw3sj5o5afxia