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Related Experiment Video

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Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

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Published on: December 16, 2019

Image-processing-based model for surface roughness evaluation in titanium based alloys using dual tree complex

J S Vishwanatha¹, P Srinivasa Pai¹, Grynal D'Mello¹

¹Department of Mechanical Engineering, NMAM Institute of Technology, NITTE (Deemed to be University), Nitte, Karnataka, 574110, India.

Scientific Reports

|November 16, 2024

Summary

This summary is machine-generated.

This study developed a computer vision system for assessing surface roughness in Ti 6Al 4V. The best model achieved 99.13% accuracy using Dual-Tree Complex Wavelet Transform image fusion and Particle Swarm Optimization features.

Keywords:

Computer vision DTCWT GLCM PCA PSO RBFNN

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Last Updated: Jun 7, 2025

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Area of Science:

Materials Science and Engineering
Computer Vision and Image Processing
Manufacturing Technology

Background:

Surface roughness is a critical parameter in evaluating the performance and quality of machined components.
Traditional methods for surface roughness assessment can be time-consuming and subjective.
Developing automated, accurate, and efficient assessment methods is crucial for advanced manufacturing.

Purpose of the Study:

To investigate the effectiveness of a computer vision system for assessing surface roughness on turned Ti 6Al 4V surfaces.
To compare different feature extraction and selection methods for surface roughness analysis.
To develop a highly accurate predictive model for surface roughness using advanced signal processing and machine learning techniques.

Main Methods:

Utilized Dual-Tree Complex Wavelet Transform (DTCWT) for image decomposition and feature extraction.
Compared three feature generation approaches: GLCM, DTCWT-based statistical features, and DTCWT image fusion with PSO-optimized GLCM features.
Employed Principal Component Analysis (PCA) for feature selection and Radial Basis Function Neural Network (RBFNN) for model development.

Main Results:

Evaluated six different RBFNN models based on feature generation and selection methods.
The model combining DTCWT image fusion, PSO-optimized GLCM features, and PCA selection achieved 100% accuracy on training data.
This optimized model demonstrated a high prediction accuracy of 99.13% on test data.

Conclusions:

The proposed computer vision system, enhanced by DTCWT image fusion and PSO-optimized features, provides a highly accurate method for assessing surface roughness in Ti 6Al 4V.
Feature selection using PCA significantly improves model efficiency and predictive performance.
This approach offers a robust and automated solution for quality control in precision manufacturing.