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Abstract
In this study, we examine the assessment of surface roughness on turned surfaces of Ti 6Al 4V using a computer vision system. We utilize the Dual-Tree Complex Wavelet Transform (DTCWT) to break down the images of the turned surface into sub-images oriented in directions. Three different methods of feature generation have been compared, i.e., the use of Gray-Level Co-Occurrence Matrix (GLCM) and DTCWT-based extraction of second-order statistical features, DTCWT Image fusion, and the use of GLCM for feature extraction, and DTCWT image fusion using Particle Swarm Optimization (PSO) based GLCM features. Principal Component Analysis (PCA) was utilized to identify and select features. The model was developed using a Radial Basis Function Neural Network (RBFNN). Accordingly, six models were designed based on the three feature generation methods, considering all features and features selected using PCA. The RBFNN model, which incorporates DTCWT Image fusion and utilizes PSO with PCA features, achieved a training data prediction accuracy of 100% and a test data prediction accuracy of 99.13%.
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Details
1 NITTE (Deemed to be University), Department of Mechanical Engineering, NMAM Institute of Technology, Nitte, India (GRID:grid.449504.8) (ISNI:0000 0004 1766 2457)
2 Department of Mechanical Engineering, Bengaluru, India (GRID:grid.449504.8)
3 NITTE (Deemed to be University), Department of Biotechnology Engineering. NMAM Institute of Technology, Nitte, India (GRID:grid.449504.8) (ISNI:0000 0004 1766 2457)
4 Hindustan Aeronautics Limited, Aircraft Research and Design Centre, Bangalore, India (GRID:grid.464878.1) (ISNI:0000 0004 0500 8503)
5 BGS College of Engineering and Technology, Department of Mechanical Engineering, Bangalore, India (GRID:grid.464878.1) (ISNI:0000 0004 5937 9932)
6 BMS College of Engineering, Department of Mechanical Engineering, Bangalore, India (GRID:grid.444321.4) (ISNI:0000 0004 0501 2828)
7 Bangalore Institute of Technology, Department of Mechanical Engineering, Bengaluru, India (GRID:grid.444321.4) (ISNI:0000 0004 0501 2828)
8 King Khalid University, Department of Mechanical Engineering, College of Engineering, Abha, Saudi Arabia (GRID:grid.412144.6) (ISNI:0000 0004 1790 7100)
9 King Khalid University Abha Saudi Arabia, Department of Industrial Engineering, Abha, Saudi Arabia (GRID:grid.412144.6) (ISNI:0000 0004 1790 7100)
10 Dire-Dawa University, School of Civil Engineering and Architecture, Institute of Technology, Dire Dawa , Ethiopia (GRID:grid.449080.1) (ISNI:0000 0004 0455 6591)