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Kuppuswamy, Ramesh
- Micro-Grinding of Poly Crystalline Diamond Insert Using a Controlled force Technique
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Authors
Affiliations
1 Department of Mechanical Engineering, University of Cape Town, ZA
2 Spectra Mapal SA Pte Ltd, Cape Town, ZA
3 Department of Mechanical Engineering, University of Cape Town, IN
1 Department of Mechanical Engineering, University of Cape Town, ZA
2 Spectra Mapal SA Pte Ltd, Cape Town, ZA
3 Department of Mechanical Engineering, University of Cape Town, IN
Source
Manufacturing Technology Today, Vol 13, No 5 (2014), Pagination: 3-8Abstract
Increasing use of Poly Crystalline Diamond (PCD) for cutting tools and wear parts is vividly seen in automobile, aerospace, marine and precision engineering applications. The PCD inserts undergo series of manufacturing processes such as: grinding to form the required shape and polishing to give a fine finish. These operations are not straight forward as PCD is extremely resistant to grinding and polishing. Single crystal diamond tools can be easily ground by choosing a direction of easy abrasion, but grinding PCD imposes serious difficulties as the grains are randomly oriented. Prior studies on PCD grinding using a vitrified bond diamond wheel produces surface defects such as: micro-grooving, edge chipping, cracks, and gouge marks. In this project, a new Micro-grinding technology that applies controlled amount of grinding force was attempted for PCD inserts grinding. The experimental results indicate that the developed method has produced ductile or partial ductile modes of fractures on the ground surface of PCD insert in the form of fine grooves and steaks. Also while applying a controlled force of value 66.75∼80 N a surface finish (Ra) 0.1 ∼0.11 μm was achieved.Keywords
Micro Grinding, Poly Crystalline Diamond, Force Control.
- Use of Resnet Modelling for Tig Weld Feature Digitization And Correlation – A Technique for AI Based Welding System
Abstract Views :51 |
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Authors
Affiliations
1 University of Cape Town, ZA
2 Indian Institute of Technology, Kanpur, IN
1 University of Cape Town, ZA
2 Indian Institute of Technology, Kanpur, IN
Source
Manufacturing Technology Today, Vol 22, No 1 (2023), Pagination: 25-32Abstract
TIG Welding is being practiced in the manufacturing industry and it demands highly skilled labour. Artificial Intelligence (AI) is developing rapidly as researchers are constantly finding new ways in which intelligent machines can add value to their industry. An AI-based welding system stands to add value by increasing production rates, improving safety, and decreasing the human input required. Weld monitoring is a key activity in the TIG welding process and successful use of AI system will enable failure prediction and the proactive corrective actions. The aim of this project is to explore, test, and compare ResNet modelling based machine learning algorithms and examine their ability to monitor welds. In this project the weld monitoring process includes collecting images of weld joint for weld feature digitization. Also, the study enables predicting whether the weld shows good quality, contamination, burn through, misalignment, lack of fusion, or lack of penetration through a ResNet modelling based image analysis.Keywords
ResNet Modelling, TIG Welding, Image Analysis, AI Based Weld System.References
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- Kesse, M., Buah, E., Handroos, H., Ayetor, G. (2020). Development of an artificial intelligence powered TIG welding algorithm for the prediction of bead geometry for TIG welding processes using hybrid deep learning, Metals, 10(4), 451, 2020. 10.3390/met10040451
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- Xia, C., Pan, Z., Fei, Z., Zhang, S., Li, H. (2020). Vision based defects detection for Keyhole TIG welding using deep learning with visual explanation. Journal of Manufacturing Processes, 56, 845-855. 10.1016/j.jmapro.2020.05.033