ArticleNature communications2026
Explainable mechanism for production process anomalies based on digital twin.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Explainable mechanism for production process anomalies based on digital twin.Nature communications · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
In the manufacturing sector, abnormal production can disrupt production schedules, leading to significant economic and reputational losses for manufacturers. To address this issue, in this study, we present an explainable mechanism for production process anomalies (EM2PA) designed to clarify the complex coupling relationships among various manufacturing factors, analyze the impact of these factors on the production process, identify abnormal production, provide explanations for its causes, and enable trace-back analysis. EM2PA consists of three modules: the data augmenter, the influence factor recognizer, and the causal interpreter. Specifically, the data augmenter generates small sample data of abnormal production, the influence factor recognizer decouples the complex coupling relationships and identifies the factors influencing abnormal production, and the causal interpreter provides causal explanations. Furthermore, through a case study based on the actual production process of a discrete manufacturing workshop, we demonstrate the effectiveness of EM2PA in identifying the root causes of problems, while highlighting the importance of explainability and causal analysis of production process anomalies.
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Registered trials
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