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66 SMT007 MAGAZINE I JUNE 2021 Article by Feng Xue, IBM, SINGAPORE, Charisse Lu, IBM, YORKTOWN HEIGHTS, NEW YORK, Christine Ouyang, IBM, YORKTOWN HEIGHTS, NEW YORK, James Hoey, IBM, POUGHKEEPSIE, NEW YORK, Rogelio Fernando Gutierrez Valdez, IBM, GUADALAJARA, MEXICO, Richard B Finch, IBM, POUGHKEEPSIE, NEW YORK Editor's note: is article/paper was presented at IPC APEX EXPO 2021 and was published in the Proceedings. Abstract In recent years, neural network-based deep learning models have demonstrated high accu- racy in object detection and classification in the area of digital image processing. Manufac- turing industry has successfully implemented prototypes and small-scale deployment to employ artificial intelligence (AI) models for quality inspection. It has been proven that AI-assisted quality inspection can improve inspection accuracy, operation throughput, and efficiency, significantly through those pro- totypes and small-scale deployment. However, A Framework for Large-Scale AI-Assisted Quality Inspection Implementation in Manufacturing Using Edge Computing the industry-known challenge of Operational Technology (OT) and Information Technol- ogy (IT) integration arises when scaling up AI- assisted quality inspection in manufacturing operation. While model accuracy is the main concern from an inspection point of view, IT implementation has to meet the requirements of high availability, scalability, security, and model and device lifecycle management. is article discusses in detail the chal- lenges in large-scale deployment of AI mod- els for quality inspection operation and intro- duces a framework for large-scale AI-assisted quality inspection in a manufacturing environ- ment using edge computing architecture. e framework focuses on IT architectural deci- sions to fulfill the OT requirement, including user experience in the quality inspection eco- system. Introduction Quality inspection serves as one of the criti- cal quality assurance tools in electronics man- ufacturing. e execution of quality inspec- tion is strictly governed by the control plan

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