Vishwanatha J S, Srinivasa Pai P, D'Mello Grynal, Sampath Kumar L, Bairy Raghavendra, Nagaral Madeva, Channa Keshava Naik N, Lamani Venkatesh T, Chandrashekar A, Yunus Khan T M, Almakayeel Naif, Ahmad Khan Wahaj
Department of Mechanical Engineering, NMAM Institute of Technology, NITTE (Deemed to be University), Nitte, Karnataka, 574110, India.
Department of Mechanical Engineering, Sir MVIT, Bengaluru, Karnataka, 562157, India.
Sci Rep. 2024 Nov 16;14(1):28261. doi: 10.1038/s41598-024-75194-7.
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%.
在本研究中，我们使用计算机视觉系统来检测Ti 6Al 4V车削表面的粗糙度评估。我们利用双树复数小波变换（DTCWT）将车削表面的图像分解为不同方向的子图像。比较了三种不同的特征生成方法，即使用灰度共生矩阵（GLCM）和基于DTCWT的二阶统计特征提取、DTCWT图像融合，以及使用GLCM进行特征提取和基于粒子群优化（PSO）的GLCM特征的DTCWT图像融合。利用主成分分析（PCA）来识别和选择特征。该模型是使用径向基函数神经网络（RBFNN）开发的。因此，基于这三种特征生成方法，考虑所有特征和使用PCA选择的特征，设计了六个模型。结合DTCWT图像融合并利用带有PCA特征的PSO的RBFNN模型，训练数据预测准确率达到100%，测试数据预测准确率达到99.13%。
