Computer Vision-Based Automated Non-Contact Metrology Framework for Mechanical Component Evaluation

Abstract

Machine vision and artificial intelligence have reshaped quality inspection across modern manufacturing. In this project, we designed a non-contact metrology system that uses machine vision to inspect mechanical parts like bolts, nuts, and pinions. The setup relies on a high-resolution camera along with a controlled backlight to grab crisp, clear images of components. We run these images through image processing tools and machine learning algorithms to pull out accurate measurements and spot any defects. This system not only boosts accuracy and consistency, but it also runs in real time and slashes the need for manual labor. Our tests show it’s faster and more precise than older, manual methods. It\'s the kind of upgrade fit for Industry 4.0 and advanced industrial automation.

Introduction

The text describes an AI-powered machine vision system designed to improve quality inspection in manufacturing. Traditional manual inspection methods are slow, error-prone, and unable to keep up with high production speeds, making them inefficient for modern industries.

The proposed system uses a non-contact machine vision approach where components are placed on a platform with proper lighting, and images are captured using a high-resolution camera. These images are processed using techniques like edge detection, contour analysis, and feature extraction to measure dimensions and identify defects. A machine learning model then compares these features with standard specifications to classify parts as acceptable or defective.

The results show that the system achieves high accuracy in inspecting components like bolts, nuts, and pinions. It significantly reduces inspection time, improves consistency, and minimizes errors compared to manual methods, making it a reliable and efficient solution for quality control in manufacturing.

Conclusion

This machine vision-based system offers manufacturers a reliable and efficient way to automate inspection. It cranks through parts quickly and accurately, dodges the common pitfalls of human error, and keeps quality standards locked in. By cutting down dependency on manual labor, it helps spot rejects right away, reduces waste, and boosts overall process efficiency. It\'s a step forward for industrial quality control and paves the way for smarter, automated manufacturing.

References

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Copyright

Copyright © 2026 Vedant R. Pachare, Vikrant G. Mhaisne, Siddhart P. Dhabalia, Pritam P. Sarode, Parikshit D. Sakarde, Samiksha D. Patil, Om P. Thakare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.