Determining Average Error of Flank Wear in Turning Duplex Stainless Steel Using Canny's Edge Detection Algorithm in Comparison with Fuzzy Logic Algorithm

To obtain the average error of flank wear in turning Duplex Stainless Steel (DSS) using Canny's Edge Detection Algorithm and compare it with Fuzzy Logic Algorithm. The average error accuracy of flank wear is computed for Canny's Edge Detection Algorithm and Fuzzy Logic Algorithm separately and are categorized into Group 1 and Group 2 respectively, each consisting of 27 samples, providing a total sample size of 54. It was computed using clinicalc.com with an enrollment ratio of one, an alpha error of 0.05, a G-power of 80%, and a confidence interval of 95%. The average error accuracy determined using the Novel Canny's Edge Detection Algorithm (92.78
^%
) is more accurate and statistically significant than the Fuzzy Logic Algorithm (88.91 %) and the significance value obtained is p = 0.000 (p<0.05). The proposed Novel Canny's Edge Detection Algorithm is significantly better at predicting flank wear and the average error accuracy than the Fuzzy Logic Algorithm.