Constructing Human-Machine Interface and Machine Learning for the Soldering Sensing System

• Main Authors: Tien-Tsan Yeh, 葉典瓚
• Other Authors: 陳怡呈
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/455xa7

碩士 === 國立中央大學 === 光機電工程研究所 === 107 === Electronic soldering is the most important part of the connector manufacturing process. Because electronic soldering can produce solder joints, which directly leads to product failure, it is important to develop predictive maintenance. In this study, the Drag Soldering station was used as the intelligent target, and the RJ45 connector was used as the soldering object. The machine learning model was constructed by using the parameters of the production, and the sensing system was established to assist the production line personnel to judge whether the product passed. The research is divided into three parts, namely, machine state evaluation and failure literature review, design implementation of software and hardware integration, and analysis of experimental results. First, the soldering process was evaluated, the types of soldering defect were sorted and the priority of the sensor arrangement was evaluated, and the failure of the soldering iron was prioritized. Then use the Queued Message Handler as the software system architecture, and implement the software and hardware modules and integrate them into the host computer interface. The image and temperature signals related to the soldering iron are extracted and pre-processed. Signal feature acquisition, stepper motor control related to soldering platform, solder joint image capture, image analysis, subordinate and external system or function serial connection such as MySQL database module, machine learning model for predicting production status. Finally, the soldering experiment is planned, and multiple classification algorithms are used as the production prediction state, and the regression algorithm is used as the causal relationship confirmation. Taking the production parameters as input, the soldering pass or fail and the defect rate are respectively used as model classification labels. The best classification model is support vector machine, the experimental prediction accuracy is 88.88%, the AUC value is 0.97. The parameters used in the regression model and the yield rate were significant, indicating that the results were not random.