As the world is evolving faster and technology gets cheaper and more powerful, simulations are proving to be more and more beneficial. Simulations allow for faster development with less lead time between iterations which means that product versions can be released more often and at less cost. The automotive industry is heavily affected by emission legislation and environmental politics. The development of more environmentally friendly engines forces the rate of development to speed up. Simulations allow the industry to keep up with the increasing requests for more complex systems. For software development, it speeds up the process significantly. Although real-life testing in engine test cells and test cars still sets the foundation for and validates the results of the simulations, simulations can drastically decrease the amount needed. Less unnecessary real-life testing with bugs in the software causing wasted time and cost. In the meantime, more iterations can be tested in a smaller time frame, making the actual real-life testing more valuable and giving. The aim of this thesis is to develop a method to convert an accurate but relatively slow simulation model of the airflow through an engine, into a faster-running format preparing it to run in close to Real-Time and with a fixed timestep. Then exporting the converted model as a functional mock-up unit, FMU, a standardised entity, part of the Functional Mock-up Interface standard. The FMU was to be used in Software in Loop, SiL, simulations using it in cooperation with Matlab Simulink and Synopsys Silver. The SiL environment is then to be used to develop and test calibration software for the engine in question.

Yes / This paper presents the development of a new chatter model using measured cutting forces instead of a mathematical model with empirical nature that describes them. The utilisation of measured cutting forces enables the prediction of real-time chatter conditions and stable machining. The chatter model is validated using fast Fourier transform (FFT) analyses for detection of chatter. The key contribution of the developed chatter model is that it can be incorporated in digital twins for process monitoring and control in order to achieve greater material removal rates and improved surface quality in future industrial applications involving machining processes. / Research Development Fund Publication Prize Award winner, Sep 2020.

Railway has the potential to become one of the most sustainable mediums for passenger and freight transport. This is possible by continuous updates to the asset management regime supporting Prognostics and Health Management (PHM). Railway tracks and catenaries are linear assets, and their length plays a vital role in maintenance. Railway catenary does not present many failures as compared to the rail track, but the failures that occur do not give enough opportunity for quick recovery. These failures cause extensive time delays disrupting railways operations. Such situations can be handled better by updating the maintenance approach. The domain of maintenance explores possible tools, techniques, and technologies to retain and restore the systems. PHM is dependent on data acquisition and analytics to predict the future state of a system with the least possible divergence. In the case of railway catenary and many other domains, this new technology of data acquisition is Light Detection And Ranging (LiDAR) device-based spatial point cloud collection. Current methods of catenary inspection depend on contact-based methods of inspection of railway catenary and read signals from the pantograph and contact wire while ignoring the rest of the wires and surroundings. Locomotive-mounted LiDAR devices support the collection of spatial data in the form of point-cloud from all the surrounding equipment and environment. This point cloud data holds a large amount of information, waiting for algorithms and technologies to harness it. A Digital Twin (DT) is a virtual representation of a physical system or process, achieved through models and simulations and maintains bidirectional communication for progressive enrichment at both ends. A systems digital twin is exposed to all the same conditions virtually. Such a digital twin can be used to provide prognostics by varying factors such as time, malfunction in components of the system, and conditions in which the system operates. Railways is a multistakeholder domain that depends on many organisations to support smooth function. The development of digital twins depends on the understanding of the system, the availability of sensors to read the state and actuators to affect the system’s state. Enabling a digital twin depends on governance restrictions, business requirements and technological competence. A concrete step towards enablement of the digital twin is designing an architecture to accommodate the technical requirements of content management, processing and infrastructure while addressing railway operations' governance and business aspects.The main objective of this work is to develop and provide architecture and a platform for the enablement of a DT solution based on Artificial Intelligence (AI) and digital technologies aimed at PHM of railway catenary system. The main results of this thesis are i) analysis of content management and processing requirements for railway overhead catenary system ii) methodology for catenary point cloud data processing and information representation iii) architecture and infrastructure requirements for enablement of Digital Twin and iv) roadmap for digital twin enablement for PHM of railway overhead catenary system.

Given recent advancements in technology and recognizing the evolution of smart manufacturing, the implementation of digital twins for factories and processes is becoming more common and more useful. Additionally, expansion in connectivity, growth in data storage, and the implementation of the Industrial Internet of Things (IIoT) allow for greater opportunities not only with digital twins but closed loop analytics. Discrete Event Simulation (DES) has been used to create digital twins and in some instances fitted with live connections to closely monitor factory operations. However, the benefits of a connected digital twin are not easily quantified. Therefore, a test bed demonstration factory was used, which implements smart technologies, to evaluate the effectiveness of a closed-loop digital twin in identifying and reacting to trends in production. This involves a digital twin of a factory process using DES. Although traditional DES is typically modeled using historical data, a DES system was developed which made use of live data with embedded machine learning to improve predictions. This model had live data updated directly to the DES model without user interaction, creating an adaptive and dynamic model. It was found that this DES with machine learning capabilities typically provided more accurate predictions of future performance and unforeseen near future problems when compared to the predictions of a traditional DES using only historic data

Der Wandel des Erhaltungsmanagements von Ingenieurbauwerken von einem reaktiven Vorgehen zu einem prädiktiven Lebenszyklusmanagement kann zur Erreichung einer zuverlässigen und verfügbaren Infrastruktur beitragen. Mit dem Einsatz von Monitoring besteht die Möglichkeit, zusätzliche Informationen zum Bauwerk und dessen zukünftiger Entwicklung abzuleiten. Der Einsatz von Monitoring ist komplex und kann daher über die Realisierung von Anwendungsfällen gestärkt werden. In Deutschland weit verbreitete Anwendungsfälle werden mit konkreten Beispielen dargestellt. Zusätzlich werden Anwendungsfälle mit ersten Umsetzungsbeispielen und mögliche zukünftige Anwendungen mit ihren Potenzialen skizziert. Die Möglichkeit der Zusammenführung der Anwendungsfälle in digitalen Zwillingen ist ein Zukunftsbild, welches im Rahmen des Artikels beschrieben wird.

Large-Scale Digital Twins Notification System (LSDTNS) monitors a Digital Twin (DT) cluster for a predefined critical state, and once it detects such a state, it sends a Notification Event (NE) to a predefined recipient. Additionally, the time from producing the DT's Complex Event (CE) to sending an alarm has to be less than a predefined deadline. However, addressing scalability and multi-objectives, such as deployment cost, resource utilization, and meeting the deadline, on top of process scheduling, presents a complex challenge. Therefore, this thesis presents a complex methodology consisting of three contributions that address system scalability, multi-objectivity and scheduling of CE processes using Reinforcement Learning (RL). The first contribution proposes the IoT Notification System Architecture based on a micro-service-based notification methodology that allows for running and seamlessly switching between various CE reasoning algorithms. Our proposed IoT Notification System architecture addresses the scalability issue in state-of-the-art CE Recognition systems. The second contribution proposes a novel methodology for multi-objective optimization for cloud provisioning (MOOP). MOOP is the first work dealing with multi-optimization objectives for microservice notification applications, where the notification load is variable and depends on the results of previous microservices subtasks. MOOP provides a multi-objective mathematical cloud resource deployment model and demonstrates effectiveness through the case study. Finally, the thesis presents a Scheduler for large-scale Critical Notification applications based on a Deep Reinforcement Learning (SCN-DRL) scheduling approach for LSDTNS using RL. SCN-DRL is the first work dealing with multi-objective optimization for critical microservice notification applications using RL. During the performance evaluation, SCN-DRL demonstrates better performance than state-of-the-art heuristics. SCN-DRL shows steady performance when the notification workload increases from 10% to 90%. In addition, SCN-DRL, tested with three neural networks, shows that it is resilient to sudden container resources drop by 10%. Such resilience to resource container failures is an important attribute of a distributed system.

Purpose – Digital twin (DT) is one of the promising technologies within the industry 4.0 umbrella. However, for manufacturing companies, the implementation of DT is a challenge due to the limited knowledge of its drivers and barriers. Therefore, the purpose of this study is to explore the main drivers and barriers when implementing digital twin (DT) in a manufacturing industry context. The purpose is fulfilled through three research questions. The first question investigates the drivers and barriers. The second question investigates how a visualization model can be developed to assist managers. The third question investigates which actions should management take to ensure successful DT implementation.    Method – The research methods in this study are a case study and a structured literature review.  The case is a large manufacturing company that has explicit plans for DT implementation. The data collection techniques consist of interviews and a focus group. The interviews enrich the data from the structured literature review. The focus group was used to find actions that management must take into consideration for DT implementation.  Findings – The findings of the structured literature review were grouped into three drivers and three barriers. The drivers are feasibility, flexibility, and optimization; while the barriers are system and process composition, data and information flow, and labor. It was found that after overcoming some barriers, they can become a driver.  Implications – The theoretical implications are gathered important factors to consider when a manufacturing company want to transform digitally and implement DT. Not only the technicality, but also what management should consider. The practical implications are how a manufacturing company step by step can follow the provided model to minimize the effects on common barriers by incorporating drivers. From the societal perspective, the model presented in this study can support companies with DT implementation which will lead to more controlled manufacturing. For more details of how applicable the results are for different companies and industries, further research needs to be conducted.  Limitations – The study is limited to the manufacturing function, and therefore it does not investigate drivers and barriers from other functions such as product design, among others.  Keywords – Decision making, Digital transformation, Digital Twin

In this paper we develop a digital twin based on the new One Record linked data standard. This enables short-term workload prediction for the various partners in the air cargo supply chain without the need for multiple data exchange interfaces. To the best of our knowledge, it is the first research on the potential benefits of One Record. The concept of the digital twin allows for an overarching optimization of operations in the air cargo supply chain without the necessity of full transparency between all the partners.

Two phenomena form the basis for this thesis; bad lighting for children and our connection to nature. The former has somehow largely stayed unchanged through the years, the second one is rapidly changing for the worse. Lighting in spaces designed for children in Sweden often seem like an afterthought focusing on the quantitative aspects, ignoring the qualitative; following standards but often forgetting who the space is meant for. At the same time, cities are becoming denser, making nature something many people actively have to seek out to experience - children’s definition of nature is slowly changing.How can outdoor lighting for children become better? Using the Ringmuren preschool in Uppsala, Sweden, as a case study, this thesis proposes an alternative way of thinking when designing light for children and how it can encourage a connection to nature. The design proposal was made using interviews, site analysis, research and experiments inside a digital twin custom made for RIngmuren preschool. The direct result of this project is a digital twin and a lighting concept, but it also argues that the practicalities of analysing and designing lighting is one thing; getting the people in power to understand why good lighting is important is the first, and largest, hurdle. Producing good, affordable examples of good lighting design that can be applied to varying situations is a good place to start to at the least initiate a discussion.

[pt] Com a chegada da Industria 4.0, empresas aderiram a usar gêmeos digitais para melhorar seus processos de produção e as condições de trabalhos de seus empregados. Os gêmeos digitais são normalmente associados a modelos tridimensionais, permitindo a realização de planejamentos, extração de dados, simulação e treinamento a partir das condições reais. Infelizmente, gêmeos digitais incorretos ou desatualizados podem induzir a erros e a desencontro de informações o que retira todas as vantagens do processo de virtualização, arruinando quaisquer comparativos com a realidade. Em contrapartida, gêmeos digitais ricos em informação permitem que simulações e extrações de dados sejam mais fieis a realidade. Atualmente, as tecnologias capazes de enriquecer as informações de gêmeos digitais são escassos, pois é um procedimento que leva tempo devido a necessidade de análises de especialistas, custos, equipamentos e ferramentas específicas. Recursos como fotografias 360 graus, vídeos e modelos tridimensionais podem ser usados para realizar uma avaliação e atualização nos gêmeos digitais. Porém, diferenças temporais, condições do ambiente e erros humanos entre os recursos podem gerar confusão durante a transferência e conexão da informação. Este trabalho apresenta uma ferramenta que explora as vantagens de combinar fotografias 360 graus com modelos 3D para gerar gêmeos digitais as-built. Cada imagem pode ser ajustada a uma localização dentro do sistema de coordenadas do modelo, inclusive permitindo alterações nos eixos e no campo de visão. Durante a navegação, é possível navegar livremente pelo modelo e pelas posições de interesse criadas pelo usuário. Além da visualização, a ferramenta propõe uma interação mais eficaz para realizar anotações entre modelos e fotografias 360 graus com o propósito de verificar consistências ou agregar novas informações ao gêmeo digital. Estas interações são importantes para a inspeção e manutenção, como avaliação de peças, análise das condições atuais ou a criação de comparativos entre o planejado e o real. / [en] With the arrival of Industry 4.0, companies have adopted digital twins to improve their production processes and the working conditions of their employees. Digital twins are generally associated with three-dimensional models and allow planning, data extraction, simulation, and training based on current conditions. Unfortunately, incorrect or outdated digital twins can lead to errors and information mismatch, which takes away all the advantages of the virtualization and computerization process, ruining any comparisons with reality. In contrast, information-rich digital twins allow simulations and data extraction to be more faithful to reality. Currently, technologies capable of enriching the information of digital twins are scarce, as it is a procedure that takes time due to the need for expert analysis, costs, equipment, and specific tools. Resources such as 360 degrees photographs, videos, and 3D models can be used to perform an evaluation and update the digital twins. However, temporal differences, environmental conditions, and human errors between the images and the model can generate confusion during the transfer and connection of information. This work presents a tool that explores the advantages of combining 360 degrees photographs with 3D models to generate as-built digital twins. Each image can be adjusted to a location within the model s coordinate system, allowing changes to axes and field of view. During navigation, it is possible to navigate the model and the user-created positions of interest freely. In addition to visualization, the tool proposes a more effective interaction to annotate between models and 360 degrees photographs to verify consistency or add new information to the digital twin. These interactions are essential for inspection and maintenance, such as evaluating parts, analyzing current conditions, or creating comparisons between planned and actual.