In this paper, classification algorithms are used to classify the test data samples for determining the error rate by comparing its classification response with actual response. In this paper, Random Forest (RF) and Adaptive Neuro Fuzzy Inference System (ANFIS) classification algorithms are used as soft computing techniques to determine the error rate for the prediction of surface roughness of the materials. The parameters feed, depth of cut, speed and mean are extracted from the test sample materials and they are given to classification mode of the ANFIS classifier which produces vision measurement value. The error rate is determined by subtracting the vision measurement values from the stylus instrument values. The performance is compared with other conventional methods.
Digital Object Identifier (DOI)
Radha krishnan, B.; Vijayan, V.; and Senthilkumar, G.
"Performance Analysis of Surface Roughness modeling using Soft Computing Approaches,"
Applied Mathematics & Information Sciences: Vol. 12:
6, Article 16.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol12/iss6/16