1Nuthan Raj B M, 2Sandeep N, 3T. Christy Bobby, 4Ramaswamy Karthikeyan B
1Department of Robotics and Automation, Ramaiah University of Applied Science
2Department of Mechanical and Manufacturing Engineering, Ramaiah University of Applied Scienc
3,4Department of Electronics and Communication Engineering, Ramaiah University of Applied Science
DOI : https://doi.org/10.47191/ijmra/v7-i12-10Google Scholar Download Pdf
ABSTRACT:
This paper presents an AI-based robotic vision inspection system designed to enhance quality control in the manufacturing of sheet-metal components. The system integrates a sequential AI model with advanced image processing techniques to automate real-time defect detection and classification. Utilizing a high-resolution camera, the system captures images of components on a conveyor, which are pre- processed using grayscale conversion, Gaussian blurring, and Canny edge detection to emphasize structural details. A deep learning model then classifies isolated regions of interest based on normalized, resized images. Feature matching through ORB (Oriented FAST and Rotated BRIEF) enables accurate alignment with reference templates, while automated measurements convert pixel dimensions to physical units, ensuring reliable detection of deviations. With an average accuracy of 88.3%, the system consistently identifies subtle and complex defects, such as scratches and dimensional deviations, under variable lighting and noise conditions. This AI-driven approach reduces the need for manual inspection, minimizes error, and enhances workflow efficiency, representing a major step toward robust, real-time quality assurance solutions in industrial environments.
KEYWORDS:Conveyor-based inspection, Gaussian blurring, high-resolution imaging, normalized image pre-processing, real- time defect classification, deep learning, canny edge detection, deep neural network, template matching, manufacturing defect analysis, automated quality inspection, industrial vision systems.
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Volume 07 Issue 12 December 2024
There is an Open Access article, distributed under the term of the Creative Commons Attribution – Non Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting and building upon the work for non-commercial use, provided the original work is properly cited.
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