A review proposes a four-part functional taxonomy of ML-enhanced adaptive manufacturing control and analyzes where generative models fit, identifying gaps and future research directions.
DeepInspect: An AI-Powered Defect Detection for Manufacturing Industries
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abstract
Utilizing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), our system introduces an innovative approach to defect detection in manufacturing. This technology excels in precisely identifying faults by extracting intricate details from product photographs, utilizing RNNs to detect evolving errors and generating synthetic defect data to bolster the model's robustness and adaptability across various defect scenarios. The project leverages a deep learning framework to automate real-time flaw detection in the manufacturing process. It harnesses extensive datasets of annotated images to discern complex defect patterns. This integrated system seamlessly fits into production workflows, thereby boosting efficiency and elevating product quality. As a result, it reduces waste and operational costs, ultimately enhancing market competitiveness.
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cs.LG 1years
2025 1verdicts
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Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review
A review proposes a four-part functional taxonomy of ML-enhanced adaptive manufacturing control and analyzes where generative models fit, identifying gaps and future research directions.