The dexterity of computer systems to resemble and mimic human intelligence is artificial intelligence. Artificial intelligence has reformed the diagnostic and therapeutic precision and competence in various fields of medicine. Artificial intelligence appears to play a bright role in medical diagnosis. Computer systems using artificial intelligence help in the assessment of medical images and enormous data. This research aims to identify how artificial intelligence-based technology is reforming the art of medicine. Artificial intelligence empowers providers in improving efficiency and overall healthcare. Newer machine learning techniques lead the automatic diagnostic systems. Areas of medicine such as medical imaging, automated clinical decision-making support have made significant advances with respect to artificial intelligence technology. With improved diagnosis and prognosis, artificial intelligence possesses the capability to revolutionize various fields of medicine. Artificial intelligence has its own limitations and cannot replace a bedside clinician. In the evolving modern medical digital world, physicians need to support artificial intelligence rather than fear it replacing trained physicians for improved healthcare.

In applications spanning across aerospace, marine, automotive, energy, and space travel domains, composite materials have become ubiquitous because of their superior stiffness-to-weight ratios as well as corrosion and fatigue resistance. However, from a manufacturing perspective, these advanced materials have introduced new challenges that demand the development of new tools. Due to the complex anisotropic and nonlinear material properties, composite materials are more difficult to model than conventional materials such as metals and plastics. Furthermore, there exist ultra-high precision requirements in safety critical applications that are yet to be reliably met in production. Towards developing new tools addressing these challenges, this dissertation aims to (i) build high-fidelity numerical simulations of composite assembly processes, (ii) bridge these simulations to machine learning tools, and (iii) apply data-driven solutions to process control problems while identifying and overcoming their shortcomings. This is accomplished in case studies that model the fixturing, shape control, and fastening of composite fuselage components. Therein, simulation environments are created that interact with novel implementations of modified proximal policy optimization, based on a newly developed reinforcement learning algorithm. The resulting reinforcement learning agents are able to successfully address the underlying optimization problems that underpin the process and quality requirements. / Doctor of Philosophy / Within the manufacturing domain, there has been a concerted effort to transition towards Industry 4.0. To a large degree, this term refers Klaus Schwab's vision presented at the World Economic Forum in 2015, in which he outlined fundamental systemic changes that would incorporate ubiquitous computing, artificial intelligence (AI), big data, and the internet-of-things (IoT) into all aspects of productive activities within the economy. Schwab argues that rapid change will be driven by fusing these new technologies in existing and emerging applications. However, this process has only just begun and there still exist many challenges to realize the promise of Industry 4.0. One such challenge is to create computer models that are not only useful during early design stages of a product, but that are connected to its manufacturing processes, thereby guiding and informing decisions in real-time. This dissertation explores such scenarios in the context of composite structure assembly in aerospace manufacturing. It aims to link computer simulations that characterize the assembly of product components with their physical counterparts, and provides data-driven solutions to control problems that cannot typically be solved without tedious trial-and-error approaches or expert knowledge.

Digital twin is an emerging technology that enhances digital transformation across many industries and domains. Most digital twins are made for a work context, and end users are the domain experts who carry knowledge in the work processes and products of which digital twin is part. The research gap for the present study is found in the missing adoption of a user-centered design approach and systematic evaluation of digital twins from the perspective of end users. User-Centered Design is a well-known design philosophy that engages users in the design process. By involving users, designers can better understand users and create situations where users can introduce their knowledge, needs and concerns into the products and systems. Emerging research questions for this study are: RQ1: What insights could be obtained with user-centered design and user involvement for the design of digital twin? RQ2: What limitations could user-centered design and user involvement incorporate in the design process of digital twins? This study will use both previous studies and empirical data from a scenario-based approach, workshops, observation, and interviews. Further, it will explore a theoretical framework combining User-Centered Design and Activity theory. This study aims to investigate what knowledge we can gain with users in focus and how that might help to fill the knowledge gap of previous research about user-centered involvement.

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.

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.