Optimization of Solder Vision Inspection using Fiber Optic Detection and Machine Learning Application
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Abstract
ABSTRACT
Product quality is the top priority in all manufacturing industries to ensure customer satisfaction. In-process inspection was a quality control method used to identify any abnormalities in the product during the manufacturing process. The most popular process inspection control was a conventional human visual inspection, where the operator conducted a 100% inspection of the product under the magnifying lamp. The developed automated optical inspection (AOI) checks and evaluates the product image condition without human intervention. However, if the target object is misaligned, the image captured by the AOI could be compromised, resulting in errors and potentially affecting the quality of the final product judgment.
In this paper, the application of fiber optic detection will be incorporated with the AOI machine to ensure the correct position of the target object (region of interest, or ROI) before the inspection process. A machine vision algorithm will process all images acquired from the AOI machine. Machin learning K Nearest Neighbor (KNN) classifier will guarantee that the AOI machine vision judgment meets the required performance metrics such as accuracy, precision, recall, f1 score, and ROC AUC.
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