Investigation on the effect of machining parameters on surface roughness during turning of kenaf fiber-reinforced composite using non-contact vision method

Too, M. X.; Ratnam, Mani Maran; Akil, Hazizan Md

doi:10.1007/s00170-020-05691-2

Cited by 13 publications

(2 citation statements)

References 30 publications

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“…The HSS blade is generally used to machine NFRCs because of its toughness and wear resistance 17 . Yet, it is important to mention that, in general, for RH with significant silicon content and microhardness, an HS blade could not be the best option; still, it was examined again to maintain uniformity 18 .…”

Section: Experimental Workmentioning

confidence: 99%

Effects of machining parameters on surface quality of composites reinforced with natural fibers

Lakshmaiya,

Murugan

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 20

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Section: Experimental Workmentioning

confidence: 99%

Effects of machining parameters on surface quality of composites reinforced with natural fibers

Lakshmaiya,

Murugan

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 20

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“…This method used discrete Zernike moments to obtain a total of three complex masks to compute all the edge parameters for sub-pixel detection. In addition, In the research of spindle speed and feed rate on the surface finish quality of kenaf fiber-reinforced composites, M. M. Ratnam et al detected the pin gauges using the invariant moment sub-pixel edge detection method [16]. And Huang et al proposed a sub-pixel edge detection algorithm based on Canny-Zernike moment [17].…”

Section: Related Workmentioning

confidence: 99%

A Novel Sub-pixel Refinement Method for Fillet Weld Under Structured Light Vision

Shengfeng

Chen

Liu

2021

Preprint

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Intelligent welding robots based on structured light vision are widely used in industrial production. With the demands of low cost, miniaturization and flexibility, the development of embedded structured light vision systems for seam tracking is a general trend. The core is how to efficiently and precisely position weld seam with low-configuration hardware. Sub-pixel refinement can break through the limitation of physical resolution, while also reducing hardware cost to achieve the required accuracy. To fill the gap in the sub-pixel refinement for fillet weld joint, a novel sub-pixel refinement method for fillet weld joint under structured light vision is proposed, which can sub-pixel refine the fillet weld joint under various working conditions. The main novelties of the proposed method include: (1) a novel sub-pixel refinement method for fillet weld joint by using Mean Shift, weighted least square and directional maximum projection is proposed, which is robust, universal, and accurate. (2) A directional maximum projection algorithm for refining weld is proposed for the first time. (3) The method can accurately refine fillet weld joint with low-resolution image. The proposed method is robust, universal, and accurate, and as demonstrated by the following performances: the average and maximum bias are 0.73 and 3 pixels in the accurate test, positioning accuracy rate is 100% in the test of noise-free, rusty, highly reflective and arc radiation-and-spatter working conditions. the method can be expanded to a common sub-pixel refinement method for structured light intersections through simple transformation.

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Innovative surface roughness detection method based on white light interference images

Yang,

Su,

et al. 2024

Machine Vision and Applications

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Investigation on the effect of machining parameters on surface roughness during turning of kenaf fiber-reinforced composite using non-contact vision method

Cited by 13 publications

References 30 publications

Effects of machining parameters on surface quality of composites reinforced with natural fibers

Effects of machining parameters on surface quality of composites reinforced with natural fibers

A Novel Sub-pixel Refinement Method for Fillet Weld Under Structured Light Vision

Innovative surface roughness detection method based on white light interference images

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