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Review of current vision-based robotic machine-tending applications

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Abstract

The manufacturing sector is a fundamental pillar of worldwide economies, contributing markedly to global economic growth. However, the manufacturing industry is persistently confronted with issues impeding its development and expansion, such as manpower shortages, safety concerns, high initial investment for installation, and long return on investment. Within this context, machine tending has become a crucial component of the manufacturing process and potentially serves as a viable solution to the afore-mentioned predicaments. Over the past 5 years, implementing automated machine-tending systems has widely extended from simulation or laboratory environments to practical applications in manufacturing workshops as robotics and artificial intelligence develop rapidly. To fully benefit from the potential of machine-tending applications, it is necessary to comprehend and tackle its associated challenges. Therefore, this paper aims to contribute to the evolution of machine-tending applications by investigating the impacts of emerging trends of advanced technologies, such as autonomous mobile robots, computer vision, machine learning, and deep learning. This systematic literature review is based on the Protocol of Preferred Reporting Items for Systematic Review and Meta-Analyses to analyze the 50 scientific literature related to machine tending in the last five years.

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Acknowledgements

We express our gratitude to the Ministry of Economic Development, Trade, and Tourism of the Government of Alberta for funding this project through Autonomous Systems Initiative of the Major Innovation Funds, and the Go Productivity funding. The authors also would like to acknowledge the NSERC (Grant Nos. NSERC RGPIN-2017-04516 and NSERC CRDPJ 537378-18) for further funding this project.

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Authors and Affiliations

  1. Smart & Sustainable Manufacturing Systems Laboratory (SMART LAB), Department of Mechanical Engineering, University of Alberta, Edmonton, Canada

    Feiyu Jia & Rafiq Ahmad

  2. Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China

    Yongsheng Ma

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  1. Feiyu Jia
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  2. Yongsheng Ma
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  3. Rafiq Ahmad
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Contributions

Feiyu Jia: conceptualization, investigation, methodology, software, validation, writing – original draft. Yongsheng Ma: methodology, writing – review & editing. Rafiq Ahmad: conceptualization, supervision, methodology, writing – review & editing, project administration, funding acquisition.

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Correspondence to Rafiq Ahmad.

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Jia, F., Ma, Y. & Ahmad, R. Review of current vision-based robotic machine-tending applications. Int J Adv Manuf Technol 131, 1039–1057 (2024). https://doi.org/10.1007/s00170-024-13168-9

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