Digital Twin In Discrete Manufacturing: Benefits, Challenges, And Case Study
Phan, Long (2025)
2025
Automaatiotekniikan DI-ohjelma - Master's Programme in Automation Engineering
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
Hyväksymispäivämäärä
2025-07-31
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202507317981
https://urn.fi/URN:NBN:fi:tuni-202507317981
Tiivistelmä
The growing focus on Industry 4.0 has made digital twin (DT) technologies an essential part of modern manufacturing. While large enterprises have started to implement DT systems, small- and medium-sized enterprises (SMEs), especially in discrete manufacturing, face significant challenges in doing so. These include legacy equipment, limited documentation, software fragmentation, and high integration costs. This thesis investigates how a functional and adaptable digital twin system can be developed under such constraints. The study aims to support discrete manufacturing companies, with a focus on SMEs, by identifying common obstacles and proposing practical solutions for scalable DT implementation.
To achieve this, the research is guided by three questions: (1) What are the benefits and challenges of DT in discrete manufacturing SMEs? (2) What are the main challenges during DT development, including legacy integration? (3) What are the key requirements for building a functional and adaptable DT system? These questions are addressed through a combination of literature review and a proof-of-concept case study carried out in a university lab, using a collaborative robot (FANUC CRX-10iA/L), a Cleco nutrunner, and a height-adjustable assembly table by IT-Line.
The digital twin was developed using Visual Components, OPC UA, and MQTT to enable real-time, bi-directional communication. The system integrates both industrial and IoT hardware to accommodate legacy and modern devices. Key results include identification of integration barriers (e.g., vendor limitations, communication mismatches), validation of low-cost retrofit solutions, and a structured set of design requirements for DT systems. The study shows that even under limited resources, real-time DT systems are feasible and can serve as a foundation for broader digitalization efforts, such as MES-lite applications.
Future work could explore applying this guideline in actual SME production environments, scaling to multi-cell setups, and integrating AI capabilities for predictive maintenance and adaptive control. The outcomes aim to guide discrete manufacturing companies and researchers toward practical, cost-effective digital twin adoption.
To achieve this, the research is guided by three questions: (1) What are the benefits and challenges of DT in discrete manufacturing SMEs? (2) What are the main challenges during DT development, including legacy integration? (3) What are the key requirements for building a functional and adaptable DT system? These questions are addressed through a combination of literature review and a proof-of-concept case study carried out in a university lab, using a collaborative robot (FANUC CRX-10iA/L), a Cleco nutrunner, and a height-adjustable assembly table by IT-Line.
The digital twin was developed using Visual Components, OPC UA, and MQTT to enable real-time, bi-directional communication. The system integrates both industrial and IoT hardware to accommodate legacy and modern devices. Key results include identification of integration barriers (e.g., vendor limitations, communication mismatches), validation of low-cost retrofit solutions, and a structured set of design requirements for DT systems. The study shows that even under limited resources, real-time DT systems are feasible and can serve as a foundation for broader digitalization efforts, such as MES-lite applications.
Future work could explore applying this guideline in actual SME production environments, scaling to multi-cell setups, and integrating AI capabilities for predictive maintenance and adaptive control. The outcomes aim to guide discrete manufacturing companies and researchers toward practical, cost-effective digital twin adoption.