Abstract
Automated quality control is a critical aspect of modern manufacturing, ensuring that products meet the required standards of quality and safety. Neural networks, a branch of artificial intelligence, have shown significant promise in automating the quality control process by analyzing large datasets from production lines and identifying defects or inconsistencies in real-time. This article explores the use of neural networks in automated quality control, focusing on their applications in defect detection, predictive maintenance, and process optimization. It also discusses the challenges, benefits, and future trends in the integration of neural networks into manufacturing quality control systems.
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