Computer vision measurement and optimization of surface roughness using soft computing approaches

This paper proposes an efficient methodology for predicting surface roughness using different soft computing approaches. The soft computing approaches are artificial neural network, adaptive neuro-fuzzy inference system and genetic algorithm. The proposed surface roughness prediction procedure has the following stages as feature extraction from the materials, classifications using random forests, adaptive neuro-fuzzy inference system (ANFIS). In this paper, the statistical features are extracted from material images as skewness, kurtosis, mean, variance, contrast, and energy.The surface roughness accuracy value varied between ANFIS and random forest classification in every measurement sequence. This limitation can be overcome by the genetic algorithm to optimize the best results. The optimization technique can produce more accurate surface roughness results for more than 98% and reduce the error rate up to 0.5%.