A Predictive Abnormality Detection Model Using Ensemble Learning in Stencil Printing Process

Abstract

This article aims to propose a predictive abnormality detection model in the stencil printing process (SPP). The SPP is the main contributor to surface mounting technology (SMT) soldering defects. The prediction of abnormal conditions is necessary to enhance the first-pass yield and reduce the reworking costs of the printed circuit board (PCB) assembly line. In this research, a novel multiphase intelligent abnormality prognosis (IAP) framework is proposed. The model comprises two phases: the abnormality detection phase and the abnormality prediction phase. The first phase is to develop the random forest-based exponential weighted moving average (RF-based EWMA) control chart. The goal is to properly monitor the highly autocorrelated SPP process and effectively recognize the existing patterns. In the second phase, the accurate prediction of anomalies within the SPP before they arise is achieved. The integration of adaptive boosting (AdaBoost) predictive modeling and a moving recognition window approach is proposed. To discriminate the different patterns from each other, features are extracted using the sliding window, and then, the AdaBoost model is adopted to predict the occurrence of abnormal patterns in the SPP. The experimental results confirm the effectiveness and reliability of the proposed framework in early and accurate prediction of abnormal patterns within the SPP process to prevent solder paste printing defects and reduce the high reworking costs for large-scale production.