20.500.12556/DKUM-96619 Enhanced product defect forecasting using partitioned attributes and ensemble machine learning This study addresses a critical challenge in industrial big data analytics for smart manufacturing: conventional machine learning methods often fail to account for data discontinuities caused by scrapped defective intermediates in multi-stage production processes, inadvertently treating non-conforming products as qualified during model training. We propose a novel process-aware data analytics framework specifically designed for process industries, featuring: (1) intelligent attribute partitioning based on information flow discontinuity points, and (2) an ensemble modelling approach combining Random Forest and C5.0 Decision Tree algorithms to generate interpretable prediction rules with quantified feature importance rankings. Validated using real-world production data from a Chinese rail steel manufacturer, our methodology demonstrates superior performance by explicitly incorporating process-specific data correlations. The proposed solution effectively mitigates information distortion caused by scrapped intermediates while maintaining operational interpretability – a crucial requirement for industrial implementation. The research results increased the accuracy rate of the test set of the random forest experiment from 88.39 % to 92.69 %, and the accuracy rate of the test set of the decision tree experiment from 71.89 % to 79.15 %. Additionally, the experimental results verify that, compared with the traditional methods, our framework has better applicability in capturing product quality in the manufacturing industry when process attributes are considered. intelligent manufacturing process industry industrial data mining defect prediction C5.0 decision tree algorithms random forest process-oriented analytics machine learning inteligentna proizvodnja procesna industrija napovedovanje napak analiza procesov strojno učenje true false true Fakulteta za strojništvo Angleški jezik Slovenski jezik https://apem-journal.org/ Članek v reviji 2026-01-21 10:45:26 2026-01-21 10:55:37 2026-01-22 03:35:08 0000-00-00 00:00:00 2025 0 0 str. 157-172 no. 2 Vol. 20 June 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-03-27 2025-06-10 2025-07-29 658.511.3:004.8 265536003 10.14743/apem2025.2.533 1854-6250 APEM20-2_157-172.pdf APEM20-2_157-172.pdf 1 9A589A04EEFABDA52534EEE92A07E635 43b29be9c75f0ea0514748968aacb584eaf9f1ffae304c0480e0d8a4eaf7014a 2a00e4e0-f6af-11f0-80d4-00155d580e02 https://dk.um.si/Dokument.php?lang=slv&id=202315 https://apem-journal.org/Archives/2025/VOL20-ISSUE02.html 0 39ac02bb-f6af-11f0-80d4-00155d580e02 https://dk.um.si/Dokument.php?lang=slv&id=202316 Fakulteta za strojništvo Advances in production engineering & management 0 0 0