@article {10.3844/jcssp.2026.25.35,
article_type = {journal},
title = {Artificial Intelligence in Quality Control: Transforming Manufacturing Processes},
author = {Kathiresan, Gopinath},
volume = {22},
number = {1},
year = {2026},
month = {Feb},
pages = {25-35},
doi = {10.3844/jcssp.2026.25.35},
url = {https://thescipub.com/abstract/jcssp.2026.25.35},
abstract = {The integration of Artificial Intelligence (AI) into Quality Control (QC) is transforming manufacturing by enhancing accuracy, efficiency, and productivity. Traditional QC methods, reliant on manual inspection and basic automation, often fall short in addressing the complexities of modern production environments. This paper presents a review on the application of AI, particularly machine learning, deep learning, and reinforcement learning, in manufacturing QC processes. It explores how AI improves defect detection, predictive maintenance, and process optimization while also identifying key benefits such as increased consistency, reduced operational costs, and data-driven decision-making. The review highlights current challenges, including data quality, high implementation costs, integration with legacy systems, and the need for specialized expertise. Additionally, emerging trends such as edge AI, explainable AI, and collaborative robotics are discussed as future directions. The findings underscore AI’s pivotal role in reshaping quality assurance and offer insights into how manufacturers can leverage these technologies for sustainable and scalable improvements in production quality.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}