Developing digital twins for production enterprises

Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework

Digital Energy Twins are IT systems, interconnecting sensor data, simulation models and user interfaces to formulate a virtual representation of the behavior of real energy systems. Digital Energy Twins are useful to predict the behavior of energy systems under varying boundary conditions and to optimize their operation considering economic and ecologic impact. Two different concepts of Digital Twins applicable to industrial energy systems were demonstrated: Digital Energy Twins and Digital Energy Shadows. While in literature, the term “Digital Twin” is widely used as synonym, for rather different applications involving simulations and virtual models connected to real-world data, this paper elaborates on the differences between digital twins and digital shadows in more detail. Given by the complexity of real-world energy systems (heat and electricity) and their implications on real time simulation, the concepts are demonstrated on different TRL levels. The results show the benefits and limitations of Digital Energy Twin and Digital Energy Shadow applications in relevant environments.

As health-care budgets are continuously under increasing demands, Artificial Intelligence resources such as digital heart twins could save millions of dollars by predicting results and preventing unnecessary surgery. Can we start to make digital human body twins to plant and predict health outcomes for a patient? By using a way to design competent simulation models from real objects, digital twins were created through IoT. But the digital twin is a complicated system and a very long-drawn step away from its possibilities. Researchers must design all components of entities or structures. There is a need to collect and merge various types of data. Many engineering researchers and participants aren’t sure about which technologies and resources to use. The 3D digital twin model offers a reference guide for digital twin comprehension and implementation. This paper aims to investigate and outline the recent technologies and tools used for digital twin applications from a 3-D digital model perspective, such as references to technologies and tools for future digital twin applications.

Positive Energy Districts (PED) require integration of different systems and infrastructures for the optimal interactions among buildings, stakeholders, mobility, energy systems and ICT systems. Digital twin is a coupled approach for new forms of modelling and analysis based on big data and machine learning/artificial intelligence, which combines capacities of virtual model, data management, analytics, simulation, system controls, visualization and information sharing. Digital twin is regarded as a potential solution to optimize PEDs. This chapter presents a comprehensive review about digital twins for PED from aspects of concepts, working principles, tools/platform and applications, in order to address the issues of both ‘how digital PED twin is made’ and ‘how digital PED twin optimizes liveability’. Further challenges and opportunities are brought forward for discussion. The outcome of the review is expected to provide useful information for optimizing the liveability of the urban environment in line with social, economic and environmental sustainability.

In this paper, we review the emerging concept of digital twins (DTs) for urban water systems (UWS) based on the literature, stakeholder interviews and analyzing the current DT implementation process in the utility company VCS Denmark (VCS). Here, DTs for UWS are placed in the context of DTs at the component, unit process/operation or hydraulic structure, treatment plant, system, city, and societal levels. A UWS DT is characterized as a systematic virtual representation of the elements and dynamics of the physical system, organized in a star-structure with a set of features connected by data links that are based on standards for open data. This allows the overall functionality to be broken down into smaller, tangible units (features), enabling microservices that communicate via data links to emerge (the most central feature), facilitated by application programing interfaces (APIs). Coupled to the physical system, simulation models and advanced analytics are among the most important features. We propose distinguishing between living and prototyping DTs, where the term “living” refers to coupling observations from an ever-changing physical twin (which may change with, e.g., urban growth) with a simulation model, through a data link connecting the two. A living DT is thus a near real-time representation of an UWS and can be used for operational and control purposes. A prototyping DT represents a scenario for the system without direct coupling to real-time observations, which can be used for design or planning. By acknowledging that different DTs exist, it is possible to identify the value-creation from DTs achieved by different end-users inside and outside a utility organization. Analyzing the DT workflow in VCS shows that a DT must be multifunctional, updateable, and adjustable to support potential value creation across the utility company. This study helps clarify key DT terminology for UWS and identifies steps to create a DT by building upon digital ecosystems (DEs) and open standards for data.

Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, and energy efficiency. This paper explores the application of digital twin technology to tackle these challenges and enhance FMS optimization and control. A digital twin, constructed by integrating simulation models, data acquisition, and machine learning algorithms, was employed to replicate the behavior of a real-world FMS. This digital twin enabled real-time dynamic optimization and adaptive control of manufacturing operations, facilitating informed decision making and proactive adjustments to optimize resource utilization and process efficiency. Computational experiments were conducted to evaluate the digital twin implementation on an FMS equipped with robotic material handling, CNC machines, and automated inspection. Results demonstrated that the digital twin significantly improved FMS performance. Productivity was enhanced by 14.53% compared to conventional methods, energy consumption was reduced by 13.9%, and quality was increased by 15.8% through intelligent machine coordination. The dynamic optimization and closed-loop control capabilities of the digital twin significantly improved overall equipment effectiveness. This research highlights the transformative potential of digital twins in smart manufacturing systems, paving the way for enhanced productivity, energy efficiency, and defect reduction. The digital twin paradigm offers valuable capabilities in modeling, prediction, optimization, and control, laying the foundation for next-generation FMS.

Purpose: This paper provides an overview of the emerging concept of digital twins (DTs) for urban water systems (UWS), drawing from literature review, stakeholder interviews, and analysis of ongoing DT implementation at the utility company VCS Denmark (VCS). Methodology: Within the realm of UWS, DTs are situated across various levels, including component, unit process/operation, hydraulic structure, treatment plant, system, city, and societal levels. A UWS DT is described as a structured virtual representation of the physical system's elements and dynamics, organized in a star-structure format with interconnected features linked by data connections conforming to open data standards. Findings:This modular structure facilitates the breakdown of overall functionality into smaller units (features), fostering the emergence of microservices that communicate through data links, primarily facilitated by application programming interfaces (APIs). Integration with the physical system is achieved through simulation models and advanced analytics. Unique Contribution to theory, practice and policy: The paper suggests distinguishing between living and prototyping DTs, where "living" DTs entail coupling real-time observations from a dynamic physical twin with a simulation model through data links, while prototyping DTs represent system scenarios without direct real-time observation coupling, often used for design or planning purposes. Recognizing the existence of different types of DTs enables the identification of value creation across utility organizations and beyond. Analysis of the DT workflow at VCS underscores the importance of multifunctionality, upgradability, and adaptability in supporting potential value creation throughout the utility company. This study clarifies essential DT terminology for UWS and outlines steps for DT creation by leveraging digital ecosystems (DEs) and adhering to open data standards.

Sustainable manufacturing as a concept as well as the practices that are deployed are well established in the literature. Tools that guide the development of manufacturing to have less environmental impact are being deployed and documented. The power of digital technology to enable manufacturing systems to be more productive is well established and advances continue to be made. What is less well known is how digital technology can support the pursuit of sustainable manufacturing, especially using digital models, shadows and twins. Here a digital simulation model is configured to analyse, replicate and drive the real production respectively to improve resource productivity in the widest sense. This paper considers the literature and practice of such digital tools in manufacturing operations as well as across the lifecycle. Literature considered is mostly from 2018 onwards as this is the point at which digital twin empirical work emerges. Whilst the work on digital twins is advancing fast, the work on sustainable manufacturing is limited to energy and, to some extent, resource efficiency. Further when compared to documented practice, the academic field appears to be lagging depending on how the loose definition of digital twins in practice is interpreted. The paper concludes with potential avenues for further research on digital models, shadows and twins in the pursuit of sustainable manufacturing.

The project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability. Applying the Industry 4.0 Digital Twin concept to the Danish horticulture greenhouse industry provides digital models for simulating and evaluating the physical greenhouse facility’s performance. A Digital Twin combines modeling, AI, and Big Data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under varying operational conditions. The Digital Twins support the co-optimization of the production schedule, energy consumption, and labor cost by considering influential factors, including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. The ecosystem of digital twins extends the state-of-the-art by adopting a scalable distributed approach of “system of systems” that interconnects Digital Twins in a production facility. A collection of specialized Digital Twins are linked together to describe and simulate all aspects of the production chain, such as overall production capacity, energy consumption, delivery dates, and supply processes. The contribution of this project is to develop an ecosystem of digital twins that collectively capture the behavior of an industrial greenhouse facility. The ecosystem will enable the industrial greenhouse facilities to become increasingly active participants in the electricity grid.

The Department of Computer Integrated Design (DiK) at the TU Darmstadt deals with the Digital Twin topic from the perspective of virtual product development. A concept for the architecture of a Digital Twin was developed, which allows the administration of simulation input and output data. The concept was built under consideration of classical CAE process chains in product development. The central part of the concept is the management of simulation input and output data in a simulation data management system in the Digital Twin (SDM-DT). The SDM-DT takes over the connection between Digital Shadow and Digital Master for simulation data and simulation models. The concept is prototypically implemented. For this purpose, real product condition data were collected via a sensor network and transmitted to the Digital Shadow. The condition data were prepared and sent as a simulation input deck to the SDM-DT in the Digital Twin based on the product development results. Before the simulation data and models are simulated, there is a comparison between simulation input data with historical input data from product development. The developed and implemented concept goes beyond existing approaches and deals with a central simulation data management in Digital Twins.

The concept of the digital twin can be thought of as a virtual representation of a physical product, engineering system or facility. This paper presents the role of predictive engineering analytics, alongside operating data, in the digital twin. Using case studies, the authors demonstrate how predictive approaches can be developed to provide data where it cannot be measured and predict future operating data to improve performance, life and integrity of equipment, systems and facilities. The digital twin is fundamentally based on data, larger datasets provide greater insight. Sensor and inspection data are critical. However, there are scenarios in which engineers require data where it cannot be measured or requires data that cannot be measured. Engineers require ways to extract this data, this can be done through predictive engineering analytics. Predictive engineering analytics, in the form of science-based simulations, combines multiple approaches, often based on fundamental principles of physics and engineering. This paper will demonstrate how high-fidelity approaches, such as Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD), combined with system-level simulation and reduced-order modelling can work together with field data to provide this data in real-time. Two case studies presented show how combinations of different levels of science-based modelling approaches can help. A subsea thermal digital twin demonstrates how high-fidelity simulations, undertaken during system design, can be the foundation for reduced order system models capable of capturing critical thermal performance in real time; aiding hydrate risk management during operation. The methods used to train the real-time predictive approach are demonstrated. The second case presented focuses on structural integrity of a heat exchanger showing how real-time sensor data can be translated into structural integrity data and insight through simulation. The cases presented demonstrate the value of science-based predictions to generate data that cannot be obtained from operational sensor data alone. The authors aim to show how the predictive element of the digital twin can be first generated during design and evolved into real-time predictive approaches that provide operations with data that cannot be gained from sensors; when and where it is needed. Digital twins typically use physical data, limiting operators of in-field equipment to make operational decisions based on information from sensor locations and historical data alone. This can limit assessment of operational performance and integrity of complex production and process systems. In this paper the authors aim to show how, by combining predictive approaches, at differing levels of fidelity and based on fundamental scientific principles, it is possible to generate the missing data required, in both space and time. This can inform and guide operations; filling the gaps where and when physical data is not available.

The concept of digital twin (DT) is undergoing rapid transformation and attracting increased attention across industries. It is recognised as an innovative technology offering real-time monitoring, simulation, optimisation, accurate forecasting and bi-directional feedback between physical and digital objects. Despite extensive academic and industrial research, DT has not yet been properly understood and implemented by many industries, due to challenges identified during its development. Existing literature shows that there is a lack of a unified framework to build DT, a lack of standardisation in the development, and challenges related to coherent goals of DT in a multi-disciplinary team engaged in the design, development and implementation of DT to a larger scale system. To address these challenges, this study introduces a unified framework for DT development, emphasising reusability and scalability. The framework harmonises existing DT frameworks by unifying concepts and process development. It facilitates the integration of heterogeneous data types and ensures a continuous flow of information among data sources, simulation models and visualisation platforms. Scalability is achieved through ontology implementation, while employing an agent-based approach, it monitors physical asset performance, automatically detects faults, checks repair status and offers operators feedback on asset demand, availability and health conditions. The effectiveness of the proposed DT framework is validated through its application to a real-world case study involving five interconnected air compressors located at the Connected Facility at Devonport Royal Dockyard, UK. The DT automatically and remotely monitors the performance and health status of compressors, providing guidance to humans on fault repair. This guidance dynamically adapts based on feedback from the DT. Analyses of the results demonstrate that the proposed DT increases the facility’s operation availability and enhances decision-making by promptly and accurately detecting faults.

Framework for planning and implementation of Digital Process Twins in the field of internal logistics

The Internet of Things (loT) has paved the way for significant efficiency gains in the oil and gas industry. One concept that has garnered significant attention is the "digital twin". However, there remains a great deal of confusion surrounding what a digital twin actually is and how it can be harnessed to add value to oil and gas operations. Some use digital twin as a synonym for their 3D plant models, others for their predictive maintenance solutions, or their simulation models. The bottom line is that the digital twin is all of these and more and unless operators look at it holistically, they are likely to miss out on some of the benefits. Digital twins afford companies a number of advantages that would otherwise not be possible, including the ability to run risk analyses, health assessments, and what-if scenarios in real-time; the ability to train personnel in a 3D immersive, risk-free environment; and the capability to detect faults early before control limits are reached. This paper/ presentation will elaborate on how digital twins can be used to enhance efficiency and will address their use in the wider context of the oil and gas industry – with a particular focus on its impact on reducing risk and cost during both the project and operational phases of the asset lifecycle. The objective is to demystify the digital twin, outline the advanced capabilities it enables and illustrate how oil and gas operators can use this concept to improve their competitive advantage.

(a) Situation faced: Arcelik is a major manufacturer of durable consumer goods. As one of the primary products of Arcelik, refrigerators constitute 35% of its annual production. Thermoforming is a critical process of manufacturing a refrigerator’s inner body, which consumes more than 20,000 tons of plastics every year. The company has decided to develop its production quality further, reduce plastic consumption, and improve its environmental footprint by integrating a digital twin into its production planning and management. (b) Actions taken: Arcelik has partnered with Simularge from Istanbul, a startup specializing in digital twins. The project team has developed a digital model of the thermoforming process by combining high-end engineering formulations, simulation modeling, and real-time sensor data. They have integrated and fine-tuned the digital twin in one plant. Currently, the company plans worldwide deployment. (c) Results achieved: Arcelik’s partnership with Simularge has successfully generated a digital twin of the thermoforming process. Implementing the digital twin with real-time operational data has improved the product quality, has decreased scrap ratios, and has reduced plastic consumption. It has resulted in an initial cost-saving of more than $2 million annually. Gaining know-how about the manufacturing processes’ digitalization has promoted a shared vision. It has also provided a strong example to encourage the digitalization of other manufacturing processes. (d) Lessons learned: The digital twin has enabled resource efficiency and improved manufacturing execution. Additionally, integrating the Internet of Things data into the digital twin has enabled better feature engineering results and improved algorithms with extracted features from data mining. Furthermore, the Internet of Things and programmable logic controller infrastructures and engineering capabilities at Arcelik has been crucial for the digital twin’s success. Arcelik’s Atolye 4.0 Lab and its relationship with Istanbul Technical University’s ITU Cekirdek Incubator also have a significant role in cultivating a collaborative project. Lastly, effectiveness in project management appears as a significant driver of success for a digital twin project.