Image-processing-based model for surface roughness evaluation in titanium based alloys using dual tree complex wavelet transform and radial basis function neural networks.

Autor: Vishwanatha JS; Department of Mechanical Engineering, NMAM Institute of Technology, NITTE (Deemed to be University), Nitte, Karnataka, 574110, India., Srinivasa Pai P; Department of Mechanical Engineering, NMAM Institute of Technology, NITTE (Deemed to be University), Nitte, Karnataka, 574110, India., D'Mello G; Department of Mechanical Engineering, NMAM Institute of Technology, NITTE (Deemed to be University), Nitte, Karnataka, 574110, India., Sampath Kumar L; Department of Mechanical Engineering, Sir MVIT, Bengaluru, Karnataka, 562157, India., Bairy R; Department of Biotechnology Engineering. NMAM Institute of Technology, NITTE (Deemed to be University), Nitte, Karnataka, 574110, India., Nagaral M; Aircraft Research and Design Centre, Hindustan Aeronautics Limited, Bangalore, Karnataka, 560037, India. madev.nagaral@gmail.com., Channa Keshava Naik N; Department of Mechanical Engineering, BGS College of Engineering and Technology, Bangalore, Karnataka, 560086, India. naikphd.sit@gmail.com., Lamani VT; Department of Mechanical Engineering, BMS College of Engineering, Bangalore, India., Chandrashekar A; Department of Mechanical Engineering, Bangalore Institute of Technology, 560004, Bengaluru, India., Yunus Khan TM; Department of Mechanical Engineering, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia., Almakayeel N; Department of Industrial Engineering, King Khalid University Abha Saudi Arabia, Abha, Saudi Arabia., Ahmad Khan W; School of Civil Engineering and Architecture, Institute of Technology, Dire-Dawa University, 1487, Dire Dawa , Ethiopia. wkhan9450@gmail.com.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Nov 16; Vol. 14 (1), pp. 28261. Date of Electronic Publication: 2024 Nov 16.
DOI: 10.1038/s41598-024-75194-7
Abstrakt: 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%.
Competing Interests: Declarations Competing interests he authors declare no competing interests.
(© 2024. The Author(s).)
Databáze: MEDLINE
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