Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques
In modern manufacturing industries with Industry 4.0 capabilities, the automated identification and classification of machined surfaces based on their texture will play a crucial role. Texture analysis through computer vision, image processing, classification using artificial neural networks (ANN),...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/59/1/92 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849342515060146176 |
|---|---|
| author | Raghavendra C. Kamath G. S. Vijay Ganesha Prasad P. Krishnananda Rao Uday Kumar Shetty Gautham Parameshwaran Aniket Shenoy Prithvi Shetty |
| author_facet | Raghavendra C. Kamath G. S. Vijay Ganesha Prasad P. Krishnananda Rao Uday Kumar Shetty Gautham Parameshwaran Aniket Shenoy Prithvi Shetty |
| author_sort | Raghavendra C. Kamath |
| collection | DOAJ |
| description | In modern manufacturing industries with Industry 4.0 capabilities, the automated identification and classification of machined surfaces based on their texture will play a crucial role. Texture analysis through computer vision, image processing, classification using artificial neural networks (ANN), and various machine learning techniques have been prominent research areas in recent decade. Tamura features are very popular in selecting optimum textural features from an image, especially in the medical domain. These textural features correspond to human visual perception and play a significant role in identifying and shortlisting the best features from the photographs. Despite the popularity of Tamura features in the medical domain, their usage in extracting the features from machined surface photographs is seldom reported. Hence, the present study investigates the feasibility of using Tamura features to classify machined surface images produced using turning, milling, grinding, and shaping operations in manufacturing. Photographs of the surfaces produced are obtained using smartphone cameras. Further, the photographs are preprocessed and divided into sixteen different portions. Then, Tamura features are extracted and are given as input to ANN, support vector machines (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF). The result shows that each machine learning (ML) algorithm performs differently while classifying the same set of machined surface images. Amongst the ML algorithms considered in the study, RF classified the photographs of surfaces machined using different machining operations with the highest accuracy. On the other hand, SVM performed poorly. |
| format | Article |
| id | doaj-art-762faa11ab4542a79fa0ebeeed0139ea |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-762faa11ab4542a79fa0ebeeed0139ea2025-08-20T03:43:21ZengMDPI AGEngineering Proceedings2673-45912023-12-015919210.3390/engproc2023059092Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing TechniquesRaghavendra C. Kamath0G. S. Vijay1Ganesha Prasad2P. Krishnananda Rao3Uday Kumar Shetty4Gautham Parameshwaran5Aniket Shenoy6Prithvi Shetty7Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaDepartment of Artificial Intelligence and Machine Learning, Shri Madhwa Vadiraja Institute of Technology and Management, Vishwothama Nagar, Bantakal 574115, Karnataka, IndiaDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaIn modern manufacturing industries with Industry 4.0 capabilities, the automated identification and classification of machined surfaces based on their texture will play a crucial role. Texture analysis through computer vision, image processing, classification using artificial neural networks (ANN), and various machine learning techniques have been prominent research areas in recent decade. Tamura features are very popular in selecting optimum textural features from an image, especially in the medical domain. These textural features correspond to human visual perception and play a significant role in identifying and shortlisting the best features from the photographs. Despite the popularity of Tamura features in the medical domain, their usage in extracting the features from machined surface photographs is seldom reported. Hence, the present study investigates the feasibility of using Tamura features to classify machined surface images produced using turning, milling, grinding, and shaping operations in manufacturing. Photographs of the surfaces produced are obtained using smartphone cameras. Further, the photographs are preprocessed and divided into sixteen different portions. Then, Tamura features are extracted and are given as input to ANN, support vector machines (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF). The result shows that each machine learning (ML) algorithm performs differently while classifying the same set of machined surface images. Amongst the ML algorithms considered in the study, RF classified the photographs of surfaces machined using different machining operations with the highest accuracy. On the other hand, SVM performed poorly.https://www.mdpi.com/2673-4591/59/1/92Tamura featuresmachined surfacesimagesclassificationANNmachine learning algorithms |
| spellingShingle | Raghavendra C. Kamath G. S. Vijay Ganesha Prasad P. Krishnananda Rao Uday Kumar Shetty Gautham Parameshwaran Aniket Shenoy Prithvi Shetty Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques Engineering Proceedings Tamura features machined surfaces images classification ANN machine learning algorithms |
| title | Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques |
| title_full | Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques |
| title_fullStr | Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques |
| title_full_unstemmed | Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques |
| title_short | Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques |
| title_sort | feasibility analysis of tamura features in the identification of machined surface images using machine learning and image processing techniques |
| topic | Tamura features machined surfaces images classification ANN machine learning algorithms |
| url | https://www.mdpi.com/2673-4591/59/1/92 |
| work_keys_str_mv | AT raghavendrackamath feasibilityanalysisoftamurafeaturesintheidentificationofmachinedsurfaceimagesusingmachinelearningandimageprocessingtechniques AT gsvijay feasibilityanalysisoftamurafeaturesintheidentificationofmachinedsurfaceimagesusingmachinelearningandimageprocessingtechniques AT ganeshaprasad feasibilityanalysisoftamurafeaturesintheidentificationofmachinedsurfaceimagesusingmachinelearningandimageprocessingtechniques AT pkrishnanandarao feasibilityanalysisoftamurafeaturesintheidentificationofmachinedsurfaceimagesusingmachinelearningandimageprocessingtechniques AT udaykumarshetty feasibilityanalysisoftamurafeaturesintheidentificationofmachinedsurfaceimagesusingmachinelearningandimageprocessingtechniques AT gauthamparameshwaran feasibilityanalysisoftamurafeaturesintheidentificationofmachinedsurfaceimagesusingmachinelearningandimageprocessingtechniques AT aniketshenoy feasibilityanalysisoftamurafeaturesintheidentificationofmachinedsurfaceimagesusingmachinelearningandimageprocessingtechniques AT prithvishetty feasibilityanalysisoftamurafeaturesintheidentificationofmachinedsurfaceimagesusingmachinelearningandimageprocessingtechniques |