Essential Data Requirements for Industrial Energy Efficiency with Digital Twins: A Case Study Analysis

Adoption of digital twins in smart factories, that model real statuses of manufacturing systems through simulation with real time actualization, are manifested in the form of increased productivity, as well as reduction in costs and energy consumption. The sharp increase in changing customer demands has resulted in factories transitioning rapidly and yielding shorter product life cycles. Traditional modeling and simulation approaches are not suited to handle such scenarios. As a possible solution, we propose a generic data-driven framework for automated generation of simulation models as basis for digital twins for smart factories. The novelty of our proposed framework is in the data-driven approach that exploits advancements in machine learning and process mining techniques, as well as continuous model improvement and validation. The goal of the framework is to minimize and fully define, or even eliminate, the need for expert knowledge in the extraction of the corresponding simulation models. We illustrate our framework through a case study.

Digital twin technology has proven to be a transformative tool in the development of smart factories by providing real-time data-driven simulations and virtual representations of physical assets and systems. This paper explores the integration of digital twins in the context of Industry 4.0 and sustainability, highlighting their potential to increase operational efficiency, optimize material usage and minimize waste. To demonstrate these benefits, case studies are presented in which a digital twin of a real manufacturing system was implemented. The digital twin enabled us to run different “what-if” scenarios to evaluate the improvements in the manufacturing system, efficiency and reduction of raw material consumption and waste by incorporating quality control operations at all assembly stations. Based on the analyzed results of both case studies, we demonstrated that additional quality control operations had a significant impact on the efficiency of the manufacturing system and its sustainability, as waste was significantly reduced. The proposed approach has proven to be highly effective for different types and sizes of manufacturing systems, especially those with high waste generation.

Digital Twins are increasingly being introduced for smart manufacturing systems to improve the efficiency of the main disciplines of such systems. Formal techniques, such as graphs, are a common way of describing Digital Twin models, allowing broad types of tools to provide Digital Twin based services such as fault detection in production lines. Obtaining correct and complete formal Digital Twins of physical systems can be a complicated and time consuming process, particularly for manufacturing systems with plenty of physical objects and the associated manufacturing processes. Automatic generation of Digital Twins is an emerging research field and can reduce time and costs. In this paper, we focus on the generation of Digital Twins for flexible manufacturing systems with Automated Guided Vehicles (AGVs) on the factory floor. In particular, we propose an architectural framework and the associated design choices and software development tools that facilitate automatic generation of Digital Twins for AGVs. Specifically, the scope of the generated digital twins is controlling AGVs in the factory floor. To this end, we focus on different control levels of AGVs and utilize graph theory to generate the graph-based Digital Twin of the factory floor.

Monitoring manufacturing systems using AI: A method based on a digital factory twin to train CNNs on synthetic data

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 aircrafts were top of the places that disrupted the seamless connectivity requirement of 5G and beyond. The Aeronautical Ad-hoc Networks (AANETs) take the attention of both industry and academia to satisfy this connectivity requirement with the low cost, easy deployment, and continuous coverage features. On the other hand, the ultra-dynamic characteristics of AANET with unstructured topology make its environment hard-to-follow. Here, Artificial Intelligence (AI)-based methodologies have an essential role in handling the management complexity of this hard-to-follow environment. However, these methodologies increase the computational complexity of aircraft due to the continuous update, convergence time, and scalability issues. At that point, we propose the utilization of the Digital Twin (DT) technology to handle the management complexity of AANET while solving the main issues of AI-based methodologies on it. The DT can virtually replicate the physical AANET components through closed-loop feedback in real-time. Therefore, this work introduces the utilization of DT technology for the AANET orchestration and, accordingly, proposes a DT-enabled AANET (DT-AANET) topology management framework. This framework consists of the Physical AANET Twin and Controller, including Digital AANET Twin with Operational Module. Here, the Digital AANET Twin virtually represents the physical environment while the operational module executes the AI-based computations on them through unsupervised learning-based training or supervised learning-based prediction. Finally, we present a case study based on Learning Vector Quantization (LVQ) to show the usability of the proposed framework and support this through evaluation results.

The current economic and manufacturing environment is characterized by increasing uncertainty and complexity. In this scenario, digitization, simulation and advanced automation are key technologies for the manufacturing of the future. A concept that has come out strongly is the Digital Twin. The digital production twin will allow manufacturers of production systems to offer services aimed at improving production throughout the entire life cycle of the facility. This article proposes a discrete event simulation procedure for the construction of the productive digital twin, in complex and highly automated production systems. As a demonstration of the developed procedure, a case study is shown on a line for the manufacture of railway axles, where simulation has been combined with multi-objective optimization. Keywords: digital twin, discrete event simulation, production systems, multi-objective optimization.

The production of food is highly complex due to the various chemo-physical and biological processes that must be controlled for transforming ingredients into final products. Further, production processes must be adapted to the variability of the ingredients, e.g., due to seasonal fluctuations of raw material quality. Digital twins are known from Industry 4.0 as a method to model, simulate, and optimize processes. In this vision paper, we describe the concept of a digital food twin. Due to the variability of the raw materials, such a digital twin has to take into account not only the processing steps but also the chemical, physical, or microbiological properties that change the food independently from the processing. We propose a hybrid modeling approach, which integrates the traditional approach of food process modeling and simulation of the bio-chemical and physical properties with a data-driven approach based on the application of machine learning. This work presents a conceptual framework for our digital twin concept based on explainable artificial intelligence and wearable technology. We discuss the potential in four case studies and derive open research challenges.

Migrating the industry and society into digital area have gradually been increased in recent years. Digitalization has made our life more efficient and faster. As a result of digitalization, the Fourth Industrial Revolution, also known as Industry 4.0, was born. The Fourth Industrial Revolution, like previous revolutions, aims to facilitate life and improve the quality of life. Two terms are especially important for digitalization. These terms are Industry 4.0 and Internet of Things (IoT). Industry 4.0 and the IoT focus on the networking and automation of devices both used human being and industry. Digitalization and IoT technologies play an important role in improving energy efficiency. Digital twin (DT) technology is one of the most important key of Industry 4.0 technologies under digitalization. DT represents a digital copy of a physical system or entity. Physical objects and DT models created interact with each other. DT technologies have a wide range from manufacturing to healthcare sectors and from maritime transportation to the heavy industry. DT provides many conveniences for industries to grow with digitalization. Meanwhile, DT draws attention with its development in the academic field. In this study, an overview of DT is presented, a DT roadmap is proposed, some algorithms emerging in the concept of DT for industrial processes are explained and a case study is analyzed. This roadmap shows different methods to be applied in order to make accurate forecasting for the future applications and sustainable manufacturing with DT. As a result, DT concept is thought to attract much attention in the near future.

A digital twin is a powerful new concept in computational modelling that aims to produces a one-to-one mapping of a physical structure, operating in a specific context, into the digital domain. This technology could therefore provide improved and robust decision making for asset management. Although the applications of digital twins vary, this paper focuses on digital twins for structural dynamic systems. A key consideration in developing a digital twin is in the construction of a workflow that defines decisions and interactions within the modelling framework. This process will generally be bespoke to specific applications, however key principles will apply. Furthermore, a workflow will provide a methodology for identifying poor predictive performance and systematically improving predictions via optimal decision making. In this paper a three storey building structure is introduced as a case study in order to motivate the challenges and technologies required of a digital twin. The context of this case study is to develop a digital twin of the building structure that consistently predicts the acceleration response of the three floors given an unknown structural state, caused by a contact nonlinearity between two floors. This reflects realistic challenges for a digital twin in that the physical twin will degrade with age, and its response may change under various loading scenarios, unforeseen in the initial model development phase. Key elements within a potential workflow for this application are discussed. These include indicating when model updating schemes become problematic and how augmenting physics-based models with a data-based component can provide information about poor predictive performance. These techniques are linked to hybrid testing, as a potential method for improving model development based on the physical structure in a controlled offline manner. Finally, the impact of these procedures are discussed for model based control methods in terms of vibration attenuation performance, but also robustness against model uncertainties and external disturbances. The workflow and key technologies investigated in this specific case study are expected to outline the general processes that apply to digital twins more broadly, and provide a clearer understanding of how a digital twin should be implemented.KeywordsDigital twinValidationHybrid testingRobust control

Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation, optimisation and accurate forecasting. However, the theoretical framework and practical implementations of digital twin (DT) are yet to fully achieve this vision at scale. Although an increasing number of successful implementations exist in research and industrial works, sufficient implementation details are not publicly available, making it difficult to fully assess their components and effectiveness, to draw comparisons, identify successful solutions, share lessons, and thus to jointly advance and benefit from the DT methodology. This work first presents a review of relevant DT research and industrial works, focusing on the key DT features, current approaches in different domains, and successful DT implementations, to infer the key DT components and properties, and to identify current limitations and reasons behind the delay in the widespread implementation and adoption of digital twin. This work identifies that the major reasons for this delay are: the fact the DT is still a fast evolving concept; the lack of a universal DT reference framework, e.g. DT standards are scarce and still evolving; problem- and domain-dependence; security concerns over shared data; lack of DT performance metrics; and reliance of digital twin on other fast-evolving technologies. Advancements in machine learning, Internet of Things (IoT) and big data have led to significant improvements in DT features such as real-time monitoring and accurate forecasting. Despite this progress and individual company-based efforts, certain research and implementation gaps exist in the field, which have so far prevented the widespread adoption of the DT concept and technology; these gaps are also discussed in this work. Based on reviews of past work and the identified gaps, this work then defines a conceptualisation of DT which includes its components and properties; these also validate the uniqueness of DT as a concept, when compared to similar concepts such as simulation, autonomous systems and optimisation. Real-life case studies are used to showcase the application of the conceptualisation. This work discusses the state-of-the-art in DT, addresses relevant and timely DT questions, and identifies novel research questions, thus contributing to a better understanding of the DT paradigm and advancing the theory and practice of DT and its allied technologies.

Methodology to develop Digital Twins for energy efficient customizable IoT-Products

Energy digital twins in smart manufacturing systems: A case study

Digital Twin-based bottleneck prediction for improved production control

Additive manufacturing is a promising manufacturing process with diverse applications, but ensuring the quality and reliability of the manufactured products is a key challenge. The digital twin has emerged as a technology solution to address this challenge, allowing real-time monitoring and control of the manufacturing process. This paper proposes a digital twin system framework for additive manufacturing that integrates machine learning models, employing Unity, OctoPrint, and Raspberry Pi for real-time control and monitoring. Particularly, the system utilizes machine learning models for defect detection, achieving an Average Precision (AP) score of 92%, with specific performance metrics of 91% for defected objects and 94% for non-defected objects, demonstrating high efficiency. The Unity client user interface is also developed for control and visualization, facilitating easy additive manufacturing process monitoring. This research article presents a detailed description of the proposed digital twin framework and its workflow for implementation, the machine learning models, and the Unity client user interface. It also demonstrates the effectiveness of the integrated system through case studies and experimental results. The main findings show that the proposed digital twin system met its functional requirements and effectively detects defects and provides real-time control and monitoring of the additive manufacturing process. This paper contributes to the growing field of digital twin technology and additive manufacturing, providing a promising solution for enhancing the quality and reliability in the field of additive manufacturing.