HCSL Publications
Neural Networks based Quality Control
1. Chatzimichail RA, Hatjimihail AT. Quality Control Using Convolutional Neural Networks Applied to Samples of Very Small Size. arXiv:2310.10608 [cs.NE].
DOI: 10.48550/arXiv.2310.10608.
Abstract
Although there is extensive literature on the application of artificial neural networks in quality control (QC), to monitor the conformity of a process to quality specifications, at least five QC measurements are required, increasing the related cost. To explore the application of neural networks to samples of QC measurements of very small size, four one-dimensional convolutional neural networks were designed, trained, and tested with datasets of n-tuples of simulated standardized normally distributed QC measurements, for 1 ≤ n ≤ 4. The designed neural networks were compared to statistical single-value QC functions with equal probabilities for false rejection, applied to samples of the same size. When the n-tuples included at least two quality control measurements distributed as Ν(μ,σ2 ), where 0.2 < |μ| ≤ 6.0, and 1.0 < σ ≤ 7.0, the designed neural networks outperformed the respective statistical QC functions. Therefore, one-dimensional convolutional neural networks applied to samples of 2-4 quality control measurements can replace statistical single-value QC functions to increase the probability of detection of the nonconformity of a process to the quality specifications, with lower cost.
2. Chatzimichail RA, Hatjimihail AT. Quality Control Using Convolutional Neural Networks Applied to Samples of Very Small Size, Stochastics and Quality Control, 2023.
DOI: 10.1515/eqc-2023-0001.
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. 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 Ν(0,s2), where 1.2 < s ≤ 9.0, the designed CNNs outperformed their statistical QC functions counterparts. Therefore, the use of 1-D CNNs applied to samples of two to four quality control measurements can effectively enhance the detection of nonconformity of a process to quality specifications. This approach has the potential to significantly reduce the costs of QC measurements and improve the overall efficiency of the QC process.