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Journal of System Simulation

Abstract

Abstract: In response to the complex manufacturing process of multi-process products, a multi-process product quality prediction model based on the kernel principal component analysis (KPCA) - and improved sparrow search algorithm (ISSA) optimized bi-directional long short term memory (BiLSTM) was proposed to address the uncertain factors that affect product quality, while improving the capacity for each process and ensuring the stability, in multi-process production. Firstly, KPCA was used for data preprocessing, and a kernel function was established on the basis of principal component analysis together with kernel methods. As redundant features were removed through dimension reduction, an improved Gaussian mutation and the uniform mutation operator η were introduced to improve the sparrow search algorithm. Secondly, the ISSA was introduced into the BiLSTM, and the dimensionality reduced data were imported into the ISSA-BiLSTM model to achieve the quality prediction of multiprocess products. Finally, the TFT-LCD manufacturing process was analyzed as an example and compared with the existing methods. The experimental results show that the prediction model has a high prediction accuracy, with the root mean square error less than 10%, effectively improving the accuracy of multi-process product quality prediction.

First Page

2321

Last Page

2332

CLC

TP391.9

Recommended Citation

Zhang Tianrui, Liu Yuting, Wang Yike. Research on Multi-process Product Quality Prediction Based on Improved BiLSTM[J]. Journal of System Simulation, 2023, 35(11): 2321-2332.

Corresponding Author

Liu Yuting

DOI

10.16182/j.issn1004731x.joss.23-FZ0805

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