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Performance Analysis of Surface Roughness modeling using Soft Computing Approaches |
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PP: 1209-1217 |
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doi:10.18576/amis/120616
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Author(s) |
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B. Radha krishnan,
V. Vijayan,
G. Senthilkumar,
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Abstract |
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| 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. |
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