A Systematic Review of Machine Vision Applications in Factory and Manufacturing Processes: From Quality Control to Predictive Diagnostics
- Authors
- Kim, Seongje; Nguyen, Thong Phi; Yoon, Jonghun
- Issue Date
- Aug-2025
- Publisher
- 한국정밀공학회
- Keywords
- Inspection; Machine Vision; Predictive Diagnostics; Robotic Guidance And Control; Safety; Computer Vision; Industrial Research; Inspection; Machine Vision; Precision Engineering; Process Control; Robotics; Guidance And Control; Labor Shortages; Machine-vision; Manufacturing Process; Manufacturing Sector; Predictive Diagnostics; Robotic Controls; Robotic Guidance; Systematic Review; Vision Applications; Accident Prevention
- Citation
- International Journal of Precision Engineering and Manufacturing-Green Technology, pp 1 - 31
- Pages
- 31
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- International Journal of Precision Engineering and Manufacturing-Green Technology
- Start Page
- 1
- End Page
- 31
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/126299
- DOI
- 10.1007/s40684-025-00769-2
- ISSN
- 2288-6206
2198-0810
- Abstract
- The manufacturing sector faces numerous challenges, including labour shortages, safety concerns, and the need for increased efficiency and precision. Machine vision technology has emerged as a crucial solution to these issues, offering advanced tools for automated inspection, quality control, robotic guidance, predictive diagnostics, and safety compliance. This comprehensive review of machine vision applications in manufacturing over the past five years focuses on the integration of advanced digital technologies and automation. The study systematically analyses 79 research papers and explores current trends, unresolved issues, and future research directions in the domain of machine vision. This review emphasises the importance of machine vision in enhancing manufacturing processes, its role in mitigating industry challenges, and the potential for further advancements through interdisciplinary research. By providing critical insights into the capabilities and limitations of current systems, it can inform future innovation in machine vision and manufacturing automation. © 2025 Elsevier B.V., All rights reserved.
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