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Classification of Damages in Composite Material using Multi-support Vector Machine
https://orcid.org/http://orcid.org/0000-0001-8056-7258
The mechanical properties of composite-reinforced natural fibres depend on parameters like fibre strength, length and chemical behaviour of fibre–matrix interfacial bond. The clarification of the research and development of composite-reinforced natural fibres improve the mechanical properties along with end applications. In this paper, multi-support vector machine (MSVM) is used to test the durability of composite material and the classification of damages in composite material. The images are collected across a composite material with 5–7 mm impingement. The images are filtered initially using an anisotropic filter. The classification is finally estimated out with MSVM classifier. Experimental validation is conducted over various composite materials, and the results are tested in real-time images. The validation shows that the proposed method attains the improved rate of accuracy in classifying the images than other existing state-of-the art models. Further, the durability of the material is tested in terms of material removal rate, wear resistance rate and material strength.
Classification of Damages in Composite Material using Multi-support Vector Machine
https://orcid.org/http://orcid.org/0000-0001-8056-7258
The mechanical properties of composite-reinforced natural fibres depend on parameters like fibre strength, length and chemical behaviour of fibre–matrix interfacial bond. The clarification of the research and development of composite-reinforced natural fibres improve the mechanical properties along with end applications. In this paper, multi-support vector machine (MSVM) is used to test the durability of composite material and the classification of damages in composite material. The images are collected across a composite material with 5–7 mm impingement. The images are filtered initially using an anisotropic filter. The classification is finally estimated out with MSVM classifier. Experimental validation is conducted over various composite materials, and the results are tested in real-time images. The validation shows that the proposed method attains the improved rate of accuracy in classifying the images than other existing state-of-the art models. Further, the durability of the material is tested in terms of material removal rate, wear resistance rate and material strength.
Classification of Damages in Composite Material using Multi-support Vector Machine
https://orcid.org/http://orcid.org/0000-0001-8056-7258
The mechanical properties of composite-reinforced natural fibres depend on parameters like fibre strength, length and chemical behaviour of fibre–matrix interfacial bond. The clarification of the research and development of composite-reinforced natural fibres improve the mechanical properties along with end applications. In this paper, multi-support vector machine (MSVM) is used to test the durability of composite material and the classification of damages in composite material. The images are collected across a composite material with 5–7 mm impingement. The images are filtered initially using an anisotropic filter. The classification is finally estimated out with MSVM classifier. Experimental validation is conducted over various composite materials, and the results are tested in real-time images. The validation shows that the proposed method attains the improved rate of accuracy in classifying the images than other existing state-of-the art models. Further, the durability of the material is tested in terms of material removal rate, wear resistance rate and material strength.
Classification of Damages in Composite Material using Multi-support Vector Machine
J. Inst. Eng. India Ser. C
Rajiv, B. (author) / Kalos, Pritam (author) / Pantawane, Prakash (author) / Chougule, Vikas (author) / Chavan, Vishwanath (author)
Journal of The Institution of Engineers (India): Series C ; 103 ; 655-661
2022-08-01
7 pages
Article (Journal)
Electronic Resource
English
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