Applying multivariate analysis to analyze and improve component rejection by pick and place machines
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Pick and place (P&P) machines are often adopted to precisely place electronic components onto a printed circuit board (PCB). If the component is not picked up successfully, then the pickup information is recorded as a “pickup miss.” If the component is picked up successfully, then the charge-coupled device camera at the bottom of the P&P is used in conjunction with a light source to examine the component from the bottom up, thereby determining whether the component’s appearance meets the specification requirements. If the component’s appearance does not meet the specification requirements, a “recognition miss” is recorded in the pickup information sheet and the component is sent into the rejection box; otherwise, the component is placed on the corresponding position of the PCB. Because passive components are tiny, component rejection due to pickup misses and recognition misses frequently occurs, thereby increasing material costs and production time. In this study, passive components of sizes 0402 and 0603 were explored. Specifically, rules were first compiled for component rejection that are attributable to recognition misses (i.e., the component size and angle exceeded the allowable errors for a P&P machine), followed by examining whether actual component rejection records met these rules. Next, components discarded into the rejection box were analyzed to determine abnormal appearance as the main factor causing component rejection. Pickup information was also collected concerning production line mass production and other types of production-related big data. Multiple regression analysis, the chi-square automatic interaction detection (CHAID), and JRip method were used to establish rules for predicting component rejection. The results revealed tape roll over, splice tape joints, excessively long nozzle maintenance intervals, and unstable feeders as key factors influencing the rejection rate. Accordingly, related suggestions were proposed for reducing the rejection rate. Finally, the prediction accuracy of the various methods was assessed, showing that the CHAID method attained prediction errors within 1.0% at various nodes.
KeywordsPick and place component rejection Pickup miss Recognition miss Big data Data mining Multiple regression analysis CHAID JRip method
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