Prediction of Production Line Status for Printed Circuit Boards

Haichuan Tang, Yin Tian, Junyan Dai, Yuan Wang, Jian-li Cong, Qi Liu, Xuejun Zhao, Yunxiao Fu
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引用次数: 1

Abstract

This paper focuses on the problem of predicting production line status for Printed Circuit Boards (PCBs). The problem contains three prediction tasks regarding PCB producing process. Firstly, data exploration is carried out and it reveals several data challenges, including data imbalance, data noise, small sample size, and component difference. To predict production line status for components of PCBs using records of inspection on pins, we proposed two possible feature extraction methods to compress the pin-level data into component-level. A statistical feature extraction method, which retrieves descriptive statistics such as mean, standard deviation, maximum, and minimum of pins on the same component, is applied to Task 1, while a PinNumber-based feature extraction method, which keep original values for 2-pin components, is applied to Task3. In addition, a neural-net model with feeding imbalance control is established for Task 1. and a random forests model is applied for both Task 2 and Task 3. Moreover, a threshold moving technique is proposed to optimize the threshold selection. Finally, the result shows that our models achieved f1-scores of 0.44, 0.54, and 0.71 on the test set for the three tasks, respectively.
印刷电路板生产线状态预测
本文主要研究印制电路板生产线状态预测问题。该问题包含三个关于PCB生产过程的预测任务。首先,对数据进行挖掘,揭示了数据不平衡、数据噪声、小样本、成分差异等数据挑战。为了利用引脚检测记录预测pcb组件的生产线状态,我们提出了两种可能的特征提取方法,将引脚级数据压缩到组件级。将统计特征提取方法应用于任务1,该方法检索同一组件上引脚的平均值、标准差、最大值和最小值等描述性统计数据,而将基于pinnumber的特征提取方法应用于任务3,该方法保留2引脚组件的原始值。此外,针对任务1,建立了具有进料不平衡控制的神经网络模型。任务2和任务3均采用随机森林模型。此外,提出了一种阈值移动技术来优化阈值的选择。最后,结果表明,我们的模型在三个任务的测试集上分别获得了0.44,0.54和0.71的f1分数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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