Quality Control Using Convolutional Neural Networks Applied to Samples of Very Small Size

Abstract

Abstract Artificial neural networks (NNs) have been extensively studied for their application to quality control (QC) to monitor the conformity of processes to quality specifications. However, the requirement of at least five QC measurements increases the associated costs. This study explores the potential of using NNs on samples of QC measurements of very small size. To achieve this, three one-dimensional (1-D) convolutional NNs (CNNs) were designed, trained, and tested on datasets of n -tuples of simulated, standardized, normally distributed QC measurements, where 2 ≤ n ≤ 4 {2\leq n\leq 4} . The performance of the designed CNNs was compared to that of statistical QC functions applied to samples of equal sizes, maintaining equal probabilities for false rejection. The results demonstrated that for n -tuples of QC measurements distributed as 𝒩 ⁢ ( 0 , s 2 ) \mathscr{N}(0,s^{2}) , where 1.2 < s ≤ 9.0 1.2

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应用于小样本的卷积神经网络的质量控制

摘要人工神经网络(neural networks, NNs)在质量控制(QC)中的应用得到了广泛的研究，用于监控过程是否符合质量规范。然而，至少五个QC测量的要求增加了相关的成本。本研究探索了在非常小尺寸的QC测量样本上使用神经网络的潜力。为了实现这一目标，设计了三个一维卷积神经网络(cnn)，并在n -元组的模拟、标准化、正态分布QC测量数据集上进行了训练和测试，其中2≤n≤4 {2\leq n \leq 4}。设计的cnn的性能与应用于等大小样本的统计QC函数的性能进行了比较，保持了相等的误拒绝概率。结果表明，对于n -元组的QC测量值分布为:(0,s2)\mathscr{N} (0,s{²})，其中1.2 ＆lt;s≤9.0 1.2＆lt;s \leq 9.0，设计的cnn优于统计QC功能的同类。因此，将一维cnn应用于两到四次QC测量的样品中，可以有效地增强对过程不符合质量规范的检测。这种方法有可能显著降低质量控制测量的成本，提高质量控制过程的整体效率。

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