Automatic Quality Inspection Using Machine Vision for Advanced Manufacturing Environments

Tonderai Andrew Damiyao; Didymus T Makusha

doi:10.71097/IJSAT.v16.i3.6798

Automatic Quality Inspection Using Machine Vision for Advanced Manufacturing Environments

Author(s)	Mr. Tonderai Andrew Damiyao, Didymus T Makusha
Country	Zimbabwe
Abstract	Automated quality inspection has become a vital requirement in advanced manufacturing environments due to the demand for higher productivity, precision, and reliability. This study presents a robust, end to end machine vision system for the automatic detection and classification of defects in metal nuts using deep learning techniques. A custom convolutional neural network (CNN) model was developed and trained on an augmented dataset comprising five defect categories bent, colour, flip, good, and scratch. The methodology includes image preprocessing, data augmentation to address class imbalance, and implementation of class-weighted loss functions. Results on a dedicated test set resulted in a classification accuracy of 60% and strong proof of concept for the real world application of the model as a first stage inspection tool. A systematic error analysis and an overview of the model's performance have also been presented, along with suggestions for future improvements, such as using transfer learning and including larger and more diverse datasets. This work advances the development of intelligent quality control systems in modern smart Industry and offers practical implications for academic researchers and production engineers
Keywords	Machine Vision; Deep Learning; Quality Inspection; Metal Nut Defect Detection; Convolutional Neural Network; Data Augmentation; Class Imbalance; Automatic Manufacture
Field	Engineering
Published In	Volume 16, Issue 3, July-September 2025
Published On	2025-07-09
DOI	https://doi.org/10.71097/IJSAT.v16.i3.6798
Short DOI	https://doi.org/g9sx6t

View / Download PDF File

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Automatic Quality Inspection Using Machine Vision for Advanced Manufacturing Environments

Share this