Pál Varga and Péter Olaszi
Machine Learning in Industry 5.0
Industry 5.0 extends the productivity-oriented perspective of Industry 4.0 by placing greater emphasis on humancentricity, resilience, and sustainability in manufacturing. Although deep neural networks dominate much of current AI research, traditional machine learning (ML) methods remain widely used in industrial practice. Their continued relevance stems from their interpretability, data efficiency, and suitability for deployment on resource-constrained hardware. This paper examines their role through the European AIMS5.0 project. More than 40 traditional ML, optimization, and signal-processing methods are mapped to 13 industrial use cases covering machine tools, intralogistics, semiconductor manufacturing, supply chains, and other production settings. We describe the main concepts behind the different method families and summarize the observed selection logic in a decision diagram based on task type, label availability, and the need for operator-facing explanations. Across the use cases, a common human-in-the-loop pattern emerges: expert feedback is used to refine the models, while operator actions connect model outputs back to the physical process. The reported outcomes include an increase in overall equipment effectiveness and reduction in electricity cost, as well. At the same time, the use cases show that strong technical performance does not necessarily translate into scalable deployment. Based on these findings, we derive a research agenda centered on dataefficient learning, controlled adaptation to concept drift, decisionoriented and resource-aware ML, and evaluation criteria that extend beyond predictive accuracy.
Reference:
DOI: 10.36244/ICJ.2026.5.1
Please cite this paper the following way:
Pál Varga and Péter Olaszi "Machine Learning in Industry 5.0", Infocommunications Journal, Industrial AI for Industry 5.0, 2026, pp. 3-24., https://doi.org/10.36244/ICJ.2026.5.1