On-line measurement of tool wear of face milling cutter using machine vision

Kiran, M.B.

doi:10.1016/j.matpr.2022.03.509

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…The time spent on corrections and repair actions is often detrimental, and in many cases, locating the source of the problem can cause long delays. In cases where machine vision was employed in Industry 4.0, the methods followed were intact, and managed to have high measurement speeds and even carry out the measurements online, providing high accuracies [80]. Based on the findings of this research, quality control is the priority application, since, in the manufacturing sector, the quality of the products is decisive.…”

Section: Contribution Of Machine Vision In Industry 40 521 Main Uses ...mentioning

confidence: 93%

“…Based on the findings of this research, quality control is the priority application, since, in the manufacturing sector, the quality of the products is decisive. Detection is also a fundamental task, such as detection, e.g., of surface irregularities [72], measurement, e.g., wear measurement [80], recognition, e.g., of materials [8], and monitoring [94].…”

Section: Contribution Of Machine Vision In Industry 40 521 Main Uses ...mentioning

confidence: 99%

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Machine Vision—Moving from Industry 4.0 to Industry 5.0

Tzampazaki,

Zografos,

Vrochidou

et al. 2024

Applied Sciences

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The Fourth Industrial Revolution combined with the advent of artificial intelligence brought significant changes to humans’ daily lives. Extended research in the field has aided in both documenting and presenting these changes, giving a more general picture of this new era. This work reviews the application field of the scientific research literature on the presence of machine vision in the Fourth Industrial Revolution and the changes it brought to each sector to which it contributed, determining the exact extent of its influence. Accordingly, an attempt is made to present an overview of its use in the Fifth Industrial Revolution to identify and present the changes between the two consequent periods. This work uses the PRISMA methodology and follows the form of a Scoping Review using sources from Scopus and Google Scholar. Most publications reveal the emergence of machine vision in almost every field of human life with significant influence and performance results. Undoubtedly, this review highlights the great influence and offer of machine vision in many sectors, establishing its use and searching for more ways to use it. It is also proven that machine vision systems can help industries to gain competitive advantage in terms of better product quality, higher customer satisfaction, and improved productivity.

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Section: Contribution Of Machine Vision In Industry 40 521 Main Uses ...mentioning

confidence: 93%

Section: Contribution Of Machine Vision In Industry 40 521 Main Uses ...mentioning

confidence: 99%

Machine Vision—Moving from Industry 4.0 to Industry 5.0

Tzampazaki,

Zografos,

Vrochidou

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Thus, by measuring the acoustic emission, the researcher assessed the extent of tool wear (Domfield et al, 1980). Kiran (2022) studied the tool wear of a face milling cutter using a Vision system. In the study, the equations (1) to (13) are used for computing the texture features.…”

Section: Indirect Tool Wear Measurementmentioning

confidence: 99%

Tool Condition Monitoring-A Review

Proceedings of the International Conference on Industrial Engineering and Operations Management

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The condition of the tool is the most significant factor in the manufacture of a quality product. Manufacturing a product involves different cutting tools, and the tool will wear out during product manufacturing. Using a worn-out tool would result in the formation of the defective product. Thus monitoring of cutting tools assumed special significance. Many researchers have been contributing techniques for tool wear measurement. An attempt has been made in this paper (i) for exploring the literature for the different tool wear monitoring techniques, (ii) advantages and limitations of the different techniques, (iii) research gaps, and to provide directions for future research. Finally, the paper concludes that much research is required in the design and development of methods using the application of machine learning and artificial intelligence for achieving accuracy, robustness, and economy in tool condition monitoring. This would help improve the reliability of the machine tool and help deliver quality products to the customer.

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A critical review on materials used in additive manufacturing

Kiran

2024

4th Symposium on Industrial Science and Technology (Sistec2022)

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On-line measurement of tool wear of face milling cutter using machine vision

Cited by 3 publications

References 11 publications

Machine Vision—Moving from Industry 4.0 to Industry 5.0

Machine Vision—Moving from Industry 4.0 to Industry 5.0

Tool Condition Monitoring-A Review

A critical review on materials used in additive manufacturing

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