Product Lifecycle Management. PLM in the Age of Model-Based Engineering in Industry

Inhaltsverzeichnis

1. Frontmatter

2. Digital Twin and Smart Systems

   1. Frontmatter

   2. Expanding the Data Usability for Digital Product Twins

      Arnaud Ngamakoua Hakoua, Achim Ebert, Karl-Gerhard Faißt, Thomas Eickhoff, Jens C. Göbel

      Abstract

      The potential of digital product twins is determined significantly by the availability and the usability of product data from both, the digital and the physical product twin. Expanding the data availability and usability could enable better analysis and optimization of physical entities in various scenarios, enabling new business models based on accurate predictions. Data stored in a digital twin at a reduced resolution can be approximated using numerical or machine learning approaches. This paper investigates the application of AI models to expand the availability and usability of digital twin data, allowing more precise and resilient predictions. Using the example of a remote-controlled miniature racing car, the main objective is to develop a robust virtual and AI-driven model that can accurately estimate the real-time displacement of the physical miniature car on a racetrack. This is achieved by estimating car positions between discrete measuring points transmitted by the track’s onboard systems using linear regression, cubic regression, and an LSTM neural network. The estimated results are then compared to each other and validated using original data.

   3. Scenario-Driven Engineering Data Integration for the Generation of Digital Product Twins

      Thomas Eickhoff, Karl-Gerhard Faißt, Jens C. Göbel

      Abstract

      Increasing complexity in the interdisciplinary development of smart products necessitates the integration of an increasing number of data sources for analysis scenarios like the development of digital twins. Ongoing trends like the digitization of engineering processes and smart products result in a large amount of data about products, their development process, and their usage phase. Using this data is not without its challenges. Especially in Small and Medium-sized Enterprises (SMEs), the data is typically stored in heterogeneous data formats and IT systems across several departments. Integrating data for specific application scenarios, like the development of digital twins, typically requires a large amount of manual effort.

      This contribution describes a process for the collection of data from heterogeneous sources across the product life cycle. The process is based on the phases of the Cross-industry standard process for data mining (CRISP-DM) known from data mining projects. It also describes a set of methods that support each step in the process of an envisioned scenario in SMEs with heterogeneous IT systems. In this context, the mapping of corresponding data objects across different sources is assisted by an Artificial Intelligence (AI-)based tool support. The method prescribes several steps to ensure the correctness of the AI’s suggestions and leaves the final decision to a human expert.

   4. Integration of Digital Twins with Product Lifecycle Management Using Digital Threads

      Ilkka Donoghue, Suraj Jaiswal, Mira Timperi, Kirsi Kokkonen

      Abstract

      The purpose of the research was to investigate and propose what a digital twin and thread are and how they can be integrated into Product Lifecycle Management (PLM). PLM has been advocating for the management of data throughout the lifecycle of the solution, from development, manufacturing, to service life, and removal from operations and recycling back into the value chain. PLM can support the physical and digital sustainability goals if the relationship between the virtual and real world are understood. Digital Twins and Digital Threads are enabling these goals. However, the definitions of the areas are fragmented and lack a common vision. Digital twins are at the core of digitalisation and sustainable business models, which can be realized with multi-body physics-based simulation and event data. The alignment and definition of PLM, Digital Twin and Digital Threads are and how they relate to each other is unclear. The goal of this paper is to propose an answer to this question through research that has been carried out with manufacturing companies. Through this study B2B manufacturing companies can create a foundation for Industry 5.0 based goals.

   5. A Framework Proposal for Fast-Improved Learning by Virtual Simulations in the Digital-Twin Context

      Marcelo Rudek, Wilcilene Maria Kowal

      Abstract

      The problem addressed in this paper is to define how human skills can be improved to match the new trends in Industry 5.0. Several technologies can help us to develop enough learning to solve complex tasks, such as the virtual approaches to iterative simulation in immersive environments. The Digital Twin (DT) concept has appeared more frequently in the industrial scenario. Thus, the training laboratories must follow technological development and be more attractive to engineering students. From this scenario, the objective is to create shared experiments at a low cost by proposing a framework to be adapted to training in the context of a digital twin operation. As a result, we exemplify the main concepts of operating a didactic robotic arm for remote access to both the real side and the corresponding digital twin. We simulated virtual AI-based students learning an activity and evaluated the learning curve of an experimental example. The contribution of this research deals with a virtual mirrored model of a real process as an important strategy for training because the user can try many times and explore alternative ways to solve a problem and learn from errors by themselves without interfering with the real side of a process.

   6. A Digital Twin Framework for Enhanced Robot Control Through Data Exchange Protocols

      Hadria Nada, Valeria Croce, Philippe Véron

      Abstract

      Digital Twin (DT) technology is transforming robotics by enabling synchronization, data consistency and precise servo control. In the context of Product Lifecycle Management, this study proposes a DT framework to achieve integration and synchronization between a physical robotic system and its virtual counterpart through bi-directional data exchange. Specifically, we develop a DT-based control and monitoring system for the Tinkerkit Braccio robotic arm, leveraging the 3DEXPERIENCE platform and Dymola while ensuring command and feedback synchronization via an Arduino board. By integrating physics-based and data-driven models to ensure synchronization between the physical robot and its virtual counterpart, the research addresses mechanical modelling, robot simulation, control architecture design and refinement of Functional Mockup Units (FMU). The integration of logical references and a robust control architecture align with PLM strategies of promoting robust operations and collaboration for smarter and more efficient robotic systems.

   7. DIGITAL AERO. Integrated System for Industrial Product Manufacturing

      Andrés Padillo, Jesús Racero, J. Carlos Molina, Ignacio Eguía

      Abstract

      The digitization of production environments has become a key priority in the industrial sector due to its substantial benefits, including significant reductions in production costs and response times. The emergence of digital twins (DTs) stems from the industry’s continuous pursuit of advanced digitalization technologies. However, their implementation remains a considerable challenge due to the complexity of many systems, resulting in prolonged deployment times and high financial costs for companies. This study aims to define an architecture that demonstrates the capabilities of Product Lifecycle Management (PLM) systems in facilitating the implementation of DTs in industrial manufacturing environments. The proposed approach leverages cost effective systems with interoperability capabilities, thereby making DT technology more accessible to small and medium sized enterprises (SMEs).

   8. A No-Code Digital Thread Graph Prototype: Runtime Configurable, Cloud-Native, and AI-Driven

      Nico Kasper, Martin Eigner

      Abstract

      Traditional PLM systems face two key challenges: decades-old technology inadequate for today's industrial complexity, and customization that consumes approximately 71% of implementation costs yet remains necessary as standard solutions rarely satisfy specific requirements. This paper introduces a configurable Digital Thread using a graph-based approach. The Digital Thread connects different lifecycle phases and IT systems through consistent, integrated data. The adaptability of the data models is achieved through a No-Code Modeling Engine based on a defined System Meta-Model – allowing alignment with existing IT infrastructures and customer-specific use cases while maintaining a standardized core and cloud compatibility. The prototype includes a No-Code Graph Builder for populating the knowledge graph, a No-Code Graph Explorer supporting visual and tabular searches, an open REST API for seamless integration, and an AI Lifecycle Assistant enabling natural language interaction and comprehensive CRUD operations. All components, including the UI, dynamically adapt via the modeling engine during runtime, ensuring continuous adaptability.

   9. Scalability Matters: Benchmarking Graph Databases for High-Performance Digital Threads

      Nico Kasper, Martin Eigner

      Abstract

      Traditional PLM systems built on decades-old software and database technology fundamentally struggle to manage the increasingly complex lifecycle data generated by rising product variability, interdisciplinarity, and heterogeneous IT landscapes. Digital Thread Knowledge Graphs have emerged as a solution, but their database persistence and performance remain open research questions. This study benchmarks three paradigms: a disk-based native graph database, an in-memory native graph database, and an in-memory relational SQL database with a graph extension. The evaluation examines filtering performance, graph traversal algorithms, scalability with increasing graph size, and the impact of connectivity (total average degree) in Digital Thread applications. Results show in-memory native graph databases excel for highly connected datasets and recursive queries, while SQL-based graph extensions are viable for small graphs, existing SQL infrastructures, or non-recursive queries. However, SQL-based solutions show exponential query time growth as graph size increases. These insights guide database selection for scalable, high-performance PLM applications.

   10. International Situational Diagnosis on the Implementation of Smart Energy in Agribusiness

       Daniele Romanin da Silva Cunha, Izamara Cristina Palheta Dias, Jones Luís Schaefer

       Abstract

       The Smart Energy revolution represents a profound transformation of the energy sector in its generation, distribution, storage, and consumption dimensions. This revolution transcends the modernization of physical infrastructure, incorporating advanced technologies that enable the integration of renewable sources, bidirectional networks, and intelligent management. Applying the Smart Energy concept to agribusiness addresses challenges of seasonality and geographic dispersion of agricultural activities. This study presents an international situational diagnosis of Smart Energy implementation in agribusiness using a mixed-methods approach, combining a qualitative analysis of 30 key articles mapping the historical evolution of the field with a bibliometric analysis of 11,666 publications. Findings highlight four major paradigm shifts over 25 years and identify eight key emerging trends shaping the field. Notable trends include integration of distributed energy systems, intelligent energy storage, and customized Energy Cloud platforms for rural contexts. Critical research gaps also emerge, notably in agricultural machinery electrification, the integration of energy storage with bioenergy, and establishing specific regulatory frameworks and sustainable business models. This diagnosis consolidates knowledge on Smart Energy in agribusiness and offers insights for researchers, practitioners, and policymakers seeking to accelerate the sector's intelligent energy transition.

   11. Towards Knowledge-Driven Decision Support in Smart Manufacturing: Challenges and Research Opportunities

       Leonardo Cavalcanti Hernandes, Anderson Luis Szejka

       Abstract

       The growing complexity of manufacturing processes has intensified the demand for advanced, knowledge-driven decision support systems. This paper highlights the need to integrate three fundamental pillars: (i) smart manufacturing, (ii) emerging and disruptive technologies such as generative AI and semantic web, and (iii) multivariable and multi-domain contexts that define modern production environments. A comprehensive review of the literature identified key research efforts, challenges, and gaps related to semantic interoperability, heterogeneous data integration, and the reliability of knowledge-based decisions. Special attention is given to the role of ontologies and intelligent systems in addressing data fragmentation and fostering scalable, adaptive, and ethically aligned decision-making. Despite advances in technology, a lack of cohesive frameworks unifies these dimensions under dynamic, real-world industrial conditions. The research proposes a conceptual foundation to guide future research toward robust, flexible, and human-centred knowledge support in manufacturing. The study concludes by outlining open research questions to advance semantic integration, generative AI applications, and the development of reliable and transparent decision systems.

   12. A Discussion of Data-Driven and Knowledge-Driven Approaches in Manufacturing Process: Challenges and Opportunities for Industry 5.0

       Matheus Herman Bernardim Andrade, Anderson Luis Szejka

       Abstract

       The evolution of manufacturing has reached a new paradigm with the emergence of Industry 5.0, which places an increasing emphasis on the collaboration between humans and Artificial Intelligence (AI) while prioritizing sustainability and resilience. Unlike Industry 4.0, which focuses on automation, cyber-physical systems, and process optimization with AI, Industry 5.0 seeks to integrate advanced technologies with human knowledge and expertise, creating manufacturing systems that are not only automated and intelligent, but also adaptable, explainable, and human-centric. This paper discusses Data-driven and knowledge-based approaches in the context of manufacturing, highlighting their limitations and contributions. By analyzing the strengths and weaknesses of these two paradigms, the paper aims to shed light on the challenges and opportunities of the convergence between AI and human knowledge, raising the idea of a more balanced, sustainable and human-centred manufacturing model in line with the principles of Industry 5.0.

   13. Evolutive Digital Twin Modeling: From Requirements to Detailed Design

       Dmitrii Ershenko, Yana Brovar, Andreas Panayi, Clement Fortin

       Abstract

       Digital Twins have garnered significant interest across various industries. However, their applications is predominantly focused on later lifecycle stages, particularly in operational monitoring and predictive maintenance, rather than full lifecycle integration. To unlock their full potential, DTs must be co-developed with the physical system from the early design stages. Embedding Digital Shadows and Digital Twins into digital prototyping workflows enables advanced testing and validation of design decisions, fostering a more adaptive and data-driven development process. Moreover, designing a system with specific constraints to accommodate its Digital Twin ensures a deeper integration between the physical and virtual domains and bridging the gap between design and operation while reinforcing a holistic lifecycle perspective. This paper explores this approach through the design of the Prescriptive Analytics Demonstrator, demonstrating how Digital Twins can influence both product architecture and development processes from the outset.

   14. A Digital Twins Framework for Managing Building Environmental Parameters

       Sidi Mohammed Yelles Chaouche, Samuel Gomes, Sebti Foufou, Sihao Deng, Rui Wang

       Abstract

       The development of smart cities and smart campuses is changing as a result of the combination of Artificial Intelligence (AI), Digital Twins (DT), and the Internet of Things (IoT), which allows for improved automation, lossless interoperability, and intelligent decision-making. In this paper, we propose a framework to support the application of DT technology in the context of smart cities. Node-RED is the main hub for managing IoT sensor data, real-time monitoring, and actuator control in the smart city-oriented system presented in this study. As a hypervision dashboard, ThingBoard is incorporated into the suggested design to guarantee the smooth integration of diverse IoT devices. Furthermore, natural language-based interactions are made possible by Large Language Models (LLMs), such as ChatGPT-4, which allow AI-driven automation through text and voice instructions via a Telegram bot. Additionally, a Digital Twin built with Unity 3D is created to simulate and visualize urban infrastructure in real-time. The paper also discusses future enhancements, including predictive analytics, machine learning-driven optimization, and edge AI deployment, to further enhance the scalability and adaptability of smart city solutions.