20.500.12556/DKUM-97159 A new approach for quality prediction and control of multistage production and manufacturing process based on Big Data analysis and Neural Networks As consumers care more and more about product quality, it is important to mine the deep correlations between production and manufacturing parameters and the evaluation of product quality through the analysis of industrial big data. The existing research of product quality prediction faces several major problems: the lack of diverse quality features, the poor tractability of abnormal parameters, the strong nonlinearity of parameters, the obvious sequential property of data, and the severe time lag of data. To solve these problems, this paper explores the quality prediction and control of multistage MP process (MPMP) based on big data analysis. Firstly, the prediction strategy and flow were specified for MPMP product quality prediction, and the features were extracted from MPMP product quality. After that, the MPMP product quality features were described in multiple dimensions, the attention mechanism was introduced to the prediction process. In addition, the recurrent neural network was improved, and an MPMP product quality prediction model was established on bidirectional long short-term memory (BiLSTM) network. Our model was compared with AdaBoost and XGBoost through experiments. The effectiveness of our model was demonstrated by the results of the appearance quality PQ1, and the area under the curve (AUC) for each process parameter. In general, our model is superior to other algorithms in the accuracy, mean accuracy, and precision of product quality prediction. big data analysis multistage production and manufacturing process (MPMP) quality prediction machine learning artificial neural network recurrent neural network bidirectional long short-term memory (BiLSTM) analiza velikih podatkov kakovost produkta napoved kakovosti strojno učenje nevronske mreže true false true Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering Angleški jezik Slovenski jezik https://apem-journal.org/ Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Članek v reviji 2026-02-20 12:27:04 2026-02-20 12:27:05 2026-02-21 03:11:39 0000-00-00 00:00:00 2022 0 0 str. 326-338 no. 3 Vol. 17 Sep. 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2022-05-09 2022-08-20 2022-09-30 658.5.012.7:004.8 269156099 10.14743/apem2022.3.439 1854-6250 APEM17-3_326-338.pdf APEM17-3_326-338.pdf 1 3CB609A1A19EE22F520D7860D1209699 e5cfccc9300885ddbf5cddbe16025b29554033bfce5af3d199dc3569a50e679c 42982282-0e4f-11f1-86bb-00155d580e1a https://dk.um.si/Dokument.php?lang=slv&id=203589 https://apem-journal.org/Archives/2022/APEM17-3_326-338.pdf 0 485f3bfd-0e4f-11f1-86bb-00155d580e1a https://dk.um.si/Dokument.php?lang=slv&id=203590 Fakulteta za strojništvo Advances in production engineering & management 0 0 0