International Journal of Engineering & Tech Development

E-ISSN: 3067-672X

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

Call for Paper Volume 3 Issue 10 October 2026 Submit your research before last 3 days of this month to publish your research paper in the issue of October.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 3 Issue 10
October 2026

Submit your research paper

AI-Enabled Quality Control Systems for Smart Manufacturing

Author(s) Wolfgang Irrek
Country Germany
Abstract The transformation of conventional manufacturing into smart manufacturing environments has increased the importance of intelligent quality control systems capable of processing large volumes of production data in real time. Artificial intelligence provides advanced capabilities for detecting defects, identifying process abnormalities, predicting quality failures, and optimizing manufacturing parameters. AI-enabled quality control systems integrate machine learning, computer vision, industrial Internet of Things technologies, edge computing, and data analytics to improve the accuracy and responsiveness of quality management activities. This paper examines the application of artificial intelligence in quality control for smart manufacturing environments and evaluates its potential contribution to defect detection, process monitoring, predictive quality management, and production optimization. A conceptual and analytical research methodology is employed to synthesize established research on AI-based inspection, machine vision, intelligent manufacturing, and Industry 4.0 quality systems.
The analysis demonstrates that AI-enabled quality control can improve inspection consistency, accelerate defect identification, support early intervention, and reduce dependence on manual inspection processes. Computer vision systems can identify visual defects, while machine learning models can analyze process variables to predict quality deviations before defective products are produced. Industrial IoT infrastructure further enables continuous collection of production information, allowing quality decisions to be integrated with real-time manufacturing operations. Despite these benefits, implementation is constrained by challenges related to data quality, model interpretability, interoperability, cybersecurity, computational requirements, and workforce skills. The study concludes that AI-enabled quality control systems can provide a strong foundation for proactive and adaptive quality management in smart manufacturing. Successful deployment requires integration of intelligent algorithms with reliable sensing infrastructure, appropriate human oversight, secure industrial networks, and continuous model validation.
Keywords Artificial Intelligence, Quality Control, Smart Manufacturing, Machine Learning, Computer Vision, Industry 4.0, Predictive Quality, Industrial IoT.
Field Engineering
Published In Volume 3, Issue 5, May 2026
Published On 2026-05-01
View / Download PDF File

Share this



E-ISSN: 3067-672X
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJETD DOI prefix is
10.00000/IJETD

Downloads
Research Paper Format Copyright Permission and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJETD
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
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +1-872-302-3851 editor@uscse.com

Published by: U.S. Center for Scholarly Excellence

Address: 265 E River Park Circle, 1st Floor, Fresno, CA 93720, USA.