Author: Dr. Rohan Mehta,Ananya Sharma
ABSTRACT:The increasing demand for precision, efficiency, and consistency in modern manufacturing has accelerated the adoption of automated quality control (QC) and inspection methods. Machine vision, leveraging high-resolution cameras, image processing algorithms, and artificial intelligence (AI), is transforming traditional QC approaches by enabling non-contact, real-time inspection with unparalleled accuracy. This paper explores the core components, methodologies, and industrial applications of machine vision-based automated inspection systems. Emphasis is placed on integration with manufacturing lines, benefits such as cost reduction and enhanced throughput, and the challenges including lighting variations, image noise, and algorithm robustness. Furthermore, advancements in deep learning for defect detection and predictive maintenance are examined. A case study demonstrates the practical impact of machine vision on productivity, quality, and operational reliability. The study concludes with future directions focusing on AI-driven adaptive vision systems for Industry 4.0 environments
KEYWORDS: Machine vision, automated quality control, defect detection, image processing, Industry 4.0
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