mlsanityData to 7 Oct 2026

A Review of Explainable Artificial Intelligence in Manufacturing

arXiv 2107.02295 ↗First posted 5 Jul 2021cs.AI cs.LGcited by 5

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Abstract (arXiv)

The implementation of Artificial Intelligence (AI) systems in the manufacturing domain enables higher production efficiency, outstanding performance, and safer operations, leveraging powerful tools such as deep learning and reinforcement learning techniques. Despite the high accuracy of these models, they are mostly considered black boxes: they are unintelligible to the human. Opaqueness affects trust in the system, a factor that is critical in the context of decision-making. We present an overview of Explainable Artificial Intelligence (XAI) techniques as a means of boosting the transparency of models. We analyze different metrics to evaluate these techniques and describe several application scenarios in the manufacturing domain.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 39 of its 63 references that are in the map. First posted on arXiv 5 Jul 2021.

Builds on

39 of 63 references.

References · 39

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27 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2023Shapley-based Explainable AI for Clustering Applications in Fault Diagnosis and Prognosiscited by 5
  2. 2024Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methodscited by 1
  3. 2025Explainable AI for Correct Root Cause Analysis of Product Quality in Injection Mouldingcited by 1
  4. 2023In-situ surface porosity prediction in DED (directed energy deposition) printed SS316L parts using multimodal sensor fusioncited by 0
  5. 2026CausalPulse: An Industrial-Grade Neurosymbolic Multi-Agent Copilot for Causal Diagnostics in Smart Manufacturingcited by 0