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A Predictive Abnormality Detection Model Using Ensemble Learning in Stencil Printing Process
IEEE Transactions on Components, Packaging and Manufacturing Technology ( IF 2.2 ) Pub Date : 2020-07-28 , DOI: 10.1109/tcpmt.2020.3012501
Shrouq Alelaumi , Haifeng Wang , Hongya Lu , Sang Won Yoon

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.

中文翻译:

模版印刷过程中基于集合学习的预测异常检测模型

本文旨在提出一种可预测的模版印刷过程(SPP)中的异常检测模型。SPP是造成表面贴装技术(SMT)焊接缺陷的主要原因。为了提高首过合格率并减少印刷电路板(PCB)装配线的返工成本,必须对异常情况进行预测。在这项研究中,提出了一种新颖的多阶段智能异常预后(IAP)框架。该模型包括两个阶段:异常检测阶段和异常预测阶段。第一阶段是开发基于森林的随机指数加权移动平均值(基于RF的EWMA)控制图。目的是正确监视高度自相关的SPP过程并有效识别现有模式。在第二阶段 可以在出现SPP之前准确预测异常。提出了自适应增强(AdaBoost)预测模型与移动识别窗口方法的集成。为了区分不同的模式,使用滑动窗口提取特征,然后采用AdaBoost模型预测SPP中异常模式的发生。实验结果证实了所提出框架在早期和准确地预测SPP工艺中异常图案的有效性和可靠性,以防止锡膏印刷缺陷并减少大规模生产的高返工成本。为了区分不同的模式,使用滑动窗口提取特征,然后采用AdaBoost模型预测SPP中异常模式的发生。实验结果证实了所提框架在SPP工艺中早期准确预测异常图案的有效性和可靠性,以防止锡膏印刷缺陷并减少大规模生产的高返工成本。为了区分不同的模式,使用滑动窗口提取特征,然后采用AdaBoost模型预测SPP中异常模式的发生。实验结果证实了所提框架在SPP工艺中早期准确预测异常图案的有效性和可靠性,以防止锡膏印刷缺陷并减少大规模生产的高返工成本。
更新日期:2020-09-22
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