Towards cognitive manufacturing: integrating ontologies, digital twins, and large language models for industrial systems

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School of Engineering | Doctoral thesis (article-based) | Defence date: 2025-12-19
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Authors

Date

2025

Department

Energia- ja konetekniikan laitos
Department of Energy and Mechanical Engineering

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Mcode

Degree programme

Language

en

Pages

81 + app. 59

Series

Aalto University publication series Doctoral Theses, 257/2025

Abstract

The digital transformation of manufacturing is evolving industrial domains into highly interconnected, data-driven, and intelligent ecosystems. While these advancements offer unprecedented opportunities for operational optimization and informed decision-making, they also pose enduring challenges, including the unification of heterogeneous data, seamless integration of data and industrial knowledge, and the facilitation of flexible, human-centric interaction. To address these challenges, this thesis proposes a unified framework that integrates ontologies, digital twins, and Large Language Models (LLMs), thereby advancing the development of next-generation manufacturing systems that deliver context-enriched, explainable, and actionable insights. The research unfolds in three main stages. First, the Industrial Production workflow (InPro) ontology is developed as a formalized and standardized semantic framework for representing production workflows. This ontology ensures semantic interoperability and seamless data integration across heterogeneous manufacturing data sources. Second, a digital twin-driven industrial context-aware system is proposed. By coupling real-time data streams with structured domain knowledge, the system transforms raw data into high-level insights to support operator decision-making. This framework combines digital twin–based reflections with an ontology-driven semantic layer across external, user, and interface contexts. It is operationalized through an Augmented Reality (AR) interface that delivers personalized, situationally relevant information. This approach offers an endto-end solution that spans perception, integration, reasoning, and visualization within complex manufacturing environments. Third, a domain-specific Cypher query generation pipeline is introduced. It integrates schema-compliant synthetic training data generation, fine-tuning augmented with preference learning, and a structured inference process. This enables accurate and context-aware access to domain knowledge via natural language interactions. By integrating these three strands, the thesis advances industrial systems towards autonomous contextual understanding and decision support. The resulting framework strengthens interoperability, adaptability, and usability, thereby contributing to more cognitive manufacturing operations.

Description

Supervising professor

Tammi, Kari, Prof., Aalto University, School of Engineering, Finland

Thesis advisor

Ala-Laurinaho, Riku, Dr., Aalto University, Department of Energy and Mechanical Engineering, Finland

Keywords

cognitive manufacturing, digital twin, large language model, ontology

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Parts

  • [Publication 1]: Yang, Chao and Zheng, Yuan and Tu, Xinyi and Ala-Laurinaho, Riku and Autiosalo, Juuso, and Seppanen, Olli, and Tammi, Kari. Ontology-based knowledge representation of industrial production workflow. Advanced Engineering Informatics, Vol. 58, pp. 102185, October 2023.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202310046176
    DOI: 10.1016/j.aei.2023.102185 View at publisher
  • [Publication 2]: Yang, Chao and Yu, Hao and Zheng, Yuan and Feng, Lei and Ala-Laurinaho, Riku and Tammi, Kari. A digital twin-driven industrial context-aware system: A case study of overhead crane operation. Journal of Manufacturing Systems, Vol. 78, pp. 394-409, February 2025.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202501021001
    DOI: 10.1016/j.jmsy.2024.12.006 View at publisher
  • [Publication 3]: Yang, Chao and Li, Changyi and Hu, Xiaodu and Yu, Hao and Lu, Jinzhi. Enhancing knowledge graph interactions: A comprehensive Textto-Cypher pipeline with large language models. Information Processing & Management, Vol. 63, pp. 104280, January 2026.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202508206642
    DOI: 10.1016/j.ipm.2025.104280 View at publisher

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Permanent link to this item

https://urn.fi/URN:ISBN:978-952-64-2898-7

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[diss] Insinööritieteiden korkeakoulu / ENG