Introduction The paradigm shift in manufacturing that Industry 4.0 brings forth with new advanced technologies and the rapid growth of sensing and controlling technologies enable further visualization and optimization that can contribute to achievingimproved decision-making in manufacturing. A significant new capability is the ability to construct a Digital Twinthat connects the physical and virtual space. However, there are still confusion and obscurity regarding what Digital Twinis and how it can becreated and then used to create value for the company. Therefor the purpose of the thesis is to examine how manufacturing companies can utilize the implementation of Digital Twinand assess Digital Twinin a shop-floor. ➢RQ1: How can DT be beneficial to increase business value in a manufacturing company? ➢RQ2: What changes need to be done in the shop-floor to implement Digital Twin? Methodology A literature review was conducted to provide previous researchand contextwithin the area of Digital Twin. A multiple-case studywas performed at three case companies to gain meaningful insight from a real-world perspective, semi-structured interviews, dialogs, and observations were conductedat the case companies. The analysis was then performed by examining similarities, and dissimilarities between theoretical and empirical data, as well as opportunities in theoretical findings that correspond with challenges in empirical findings. Frame of Reference The literature review increased the authors’ understanding of the research topic and gave context to the concept of Digital Twin. The review is mainly focused on the Digital Twintechnologyand how it is constructed, as well as the applicationsareas. Empirical Findings The empirical findings provide an overview of boththe current and future state of the case companies in relation to organizational, operational, and technological factors. Additionally, it provides a deeper understanding of how shop-floor management is designed at one of the case companies. Analysis The combination of the Frame of Reference and Empirical Findings contributewith important insight on the potential benefits that can be created through the utilizationof Digital Twin, as well as what is requiredin the shop-floor to enable implementation ofDigital Twin. Conclusions The value that can be created utilizing Digital Twinis outlinedand a clearer definition is proposed to avoid misunderstandings and confusion. Requirements that need to be achieved for a successful implementation arecovered as well. A future recommendation is measuring resources and effort in relation to the created value of a Digital Twin.

Digital Twin is a new technology that is taking over manufacturing and production processes while lowering their costs. This technology has proven to be a key enabler for efficient verification and validation processes, stressing out the importance of its own validation and accreditation phase. This study will emphasize the importance of validation and verification for these DTs, as well as Models and cyber-Physical Systems. Current V&V techniques will be listed and described in this paper, addressing what Model requirements are necessary to validate and translate them to DT.

<p>Human body models (HBMs) have been present in the automotive industry for simulating automotive related injury since the turn of the century and have in recent years found a place in assessment of soldier and sports related injury prediction and assessment. This issue is the lack of models that lie outside of the 50th percentile. By a simple application of physics, it is evident that acceleration or force will affect people of varying weights differently. To this end, having the ability to scale a 50th percentile HBM to targets for weight and stature would allow for better characterization on how an impact or acceleration event will affect people of differing size, especially when ~90% of males can fall outside the 50th percentile for weight and stature and HBMs models from vendors exist in only a few variations outside the 50th percentile [1]. Using Corvid Technologies’ 50th percentile model CAVEMAN (capable of being repositioned) as a base, scaled model from the 5th to 95th percentiles of stature and weight were generated based on ANSURII metrics, using a combination of 1D and 3D scaling transformations. These models met their stature and weight metrics when standing and weight metrics when positioned. </p> <p>After creation of a framework to scale the CAVEMAN HMB, creation of a digital twin to the HIRRT Lab helmet testing model commenced. With the HIRRT Lab’s history of experimental testing of football helmets, a natural turn of events was to bring helmet performance testing into the computational space. This digital twin was a natural evolution and addition to the HIRRT Lab’s helmet testing as it would enable manipulation of helmets that would be infeasible experimentally. After calibration of the barehead using experimental data, helmeted simulation began. Angle of impact, while it was found to effect peak translational acceleration, was found to profoundly effect peak rotational acceleration. With this in mind, various angles of impact were simulated to produce curves similar to experimental results. Helmeted simulations were qualitatively dissimilar to experimental data, prompting a modification of the padding material used by the models. Following various modifications of the padding material model, these inconsistencies between simulated helmets and experimentally tested helmets persisted. These inconsistencies highlight a need for better characterization of material, such as foam, and more thorough validation of simulated helmet models. The results of the helmeted simulations are difficult to quantify, as the evaluation criteria used for the BioCore model did not include rotational acceleration, indicating a need for further research and simulation is necessary. </p>

Glycosphingolipids such as α- and β-glucosylceramides (GlcCers) and α- and β- galactosylceramides (GalCers) are stereoisomers differentially synthesized by gut bacteria and their mammalian hosts in response to environmental insult. Thus, lipidomic assessment of α- and β-GlcCers and α- and β-GalCers is crucial for inferring biological functions and biomarker discovery. However, simultaneous quantification of these stereoisomeric lipids is difficult due to their virtually identical structures. Differential mobility mass spectrometry (DMS), as an orthogonal separation to high performance liquid chromatography used in electrospray ionization, tandem mass spectrometry (LC-ESI-MS/MS), can be used to separate stereoisomeric lipids. Generating LC-ESI-DMS-MS/MS methods for lipidomic analyses is exceedingly difficult demanding intensive manual optimization of DMS parameters that depend on the availability of synthetic lipid standards. Where synthetic standards do not exist, method development is not possible. To address this challenge, I developed a supervised in silico machine learning approach to accelerate method development for ion mobility-based quantification of lipid stereoisomers. I hypothesized that supervised neural network models could be used to learn the relationships between lipid structural characteristics and optimal DMS machine parameter values thereby reducing the total number of empirical experiments required to develop a DMS method and enabling users to “predict” DMS parameters for analytes that lack synthetic standards. Specifically, this thesis describes a supervised learning approach that learns the relationship between two DMS machine parameter values (separation voltage and compensation voltage) and two lipid structural features (N-Acyl chain length and degree of unsaturation). I describe here, iDMS, an algorithm that was trained on 17 lipid species, and can further simulate results of DMS manual method development and suggest optimal parameter values for 47 lipid species. This approach promises to greatly accelerate the development of assays for the detection of lipid stereoisomers in biological samples.

Maintenance management is a relevant issue in modern technical systems due to its financial, safety, and environmental implications. The need to rely on physical assets makes maintenance a necessary evil, which, on the other hand, allows achieving a high quality of end products, or services, and a safety level that is adequate for the regulatory requirements. The advent of the fourth industrial revolution offers meaningful opportunities to improve maintenance management; technologies such as Cyber-Physical Systems, the Internet of Things, and cloud computing enable realizing modern infrastructure to support decisions with advanced analytics. In this thesis, the optimization of maintenance policies is tackled in this renewed technological context. The research methods employed in this thesis include interviewing of subject experts, literature research, and numerical experiments. Mathematical modelling is used to model network effects in complex technical systems, and simulations are used to validate the proposed models and methodologies. The problem of maintenance policies comparison is addressed in one of the publications; using the proposed bi-objective analysis, an effective maintenance policy was identified. Maintenance of complex systems organized in a networked fashion is studied in another project, where maintenance costs and system performances are considered. The proposed model allowed to identify a set of non-dominated (in the Pareto sense) maintenance policies, and an efficient resolution procedure was developed. The possibility to use a digital twin to replicate a Cyber-Physical System for maintenance policies optimization is addressed in another publication. The main hurdles in realizing such a complex infrastructure are analyzed, and managerial implications are presented. Finally, following a qualitative research approach, the opportunities offered by additive manufacturing are identified and presented in a book chapter. The opportunities for both maintenance efficiency gains and new business models are identified and discussed.

Artificial intelligence (AI) has progressed significantly during the last several decades, along with the rapid advancements in computational power. This advanced technology is currently being employed in various engineering fields, not just in computer science. In aerospace engineering, AI and machine learning (ML), a major branch of AI, are now playing an important role in various applications, such as automated systems, unmanned aerial vehicles, aerospace optimum design structure, etc. This dissertation mainly focuses on structural engineering to employ AI to develop lighter and safer aircraft structures as well as challenges involving structural optimization and analysis. Therefore, various ML applications are studied in this research to provide novel frameworks for structural optimization, analysis, and design. First, the application of a deep-learning-based (DL) convolutional neural network (CNN) was studied to develop a surrogate model for providing optimum structural topology. Typically, conventional structural topology optimization requires a large number of computations due to the iterative finite element analyses (FEAs) needed to obtain optimal structural layouts under given load and boundary conditions. A proposed surrogate model in this study predicts the material density layout inputting the static analysis results using the initial geometry but without performing iterative FEAs. The developed surrogate models were validated with various example cases. Using the proposed method, the total calculation time was reduced by 98 % as compared to conventional topology optimization once the CNN had been trained. The predicted results have equal structural performance levels compared to the optimum structures derived by conventional topology optimization considered ``ground truths". Secondly, reinforcement learning (RL) is studied to create a stand-alone AI system that can design the structure from trial-and-error experiences. RL application is one of the major ML branches that mimic human behavior, specifically how human beings solve problems based on their experience. The main RL algorithm assumes that the human problem-solving process can be improved by earning positive and negative rewards from good and bad experiences, respectively. Therefore, this algorithm can be applied to solve structural design problems whereby engineers can improve the structural design by finding the weaknesses and enhancing them using a trial and error approach. To prove this concept, an AI system with the RL algorithm was implemented to drive the optimum truss structure using continuous and discrete cross-section choices under a set of given constraints. This study also proposed a unique reward function system to examine the constraints in structural design problems. As a result, the independent AI system can be developed from the experience-based training process, and this system can design the structure by itself without significant human intervention. Finally, this dissertation proposes an ML-based classification tool to categorize the vibrational mode shapes of tires. In general, tire vibration significantly affects driving quality, such as stability, ride comfort, noise performance, etc. Therefore, a comprehensive study for identifying the vibrational features is necessary to design the high-performance tire by considering the geometry, material, and operation conditions. Typically, the vibrational characteristics can be obtained from the modal test or numerical analysis. These identified modal characteristics can be used to categorize the tire mode shapes to determine the specific mode cause poorer driving performances. This study suggests a method to develop an ML-based classification tool that can efficiently categorize the mode shape using advanced feature recognition and classification algorithms. The best-performed classification tool can accurately predict the tire category without manual effort. Therefore, the proposed classification tool can be used to categorize the tire mode shapes for subsequent tire performance and improve the design process by reducing the time and resources for expensive calculations or experiments. / Doctor of Philosophy / Artificial intelligence (AI) has significantly progressed during the last several decades with the rapid advancement of computational capabilities. This advanced technology is currently employed to problems in various engineering fields, not just problems in computer science. Machine learning (ML), a major branch of AI, is actively applied to mechanical/structural problems since an ML model can replace a physical system with a surrogate model, which can be used to predict, control, and optimize its behavior. This dissertation provides a new framework to design and analyze structures using ML-based techniques. In particular, the latest ML technologies, such as convolutional neural networks, widely used for image processing and feature recognition, are applied to replace numerical calculations in structural optimization and analysis with the ML-based system. Also, this dissertation suggests how to develop a smart system that can design the structure by itself using reinforcement learning, which is utilized for autonomous driving systems and robot walking algorithms. Finally, this dissertation suggests an ML-based classification approach to categorize complex vibration modes of a structure.

Doctor of Philosophy / The long-term goal of this dissertation is to develop quality assurance methodologies for parts made using metal additive manufacturing (AM). Additive manufacturing is becoming a prominent manufacturing process due to its ability to generate complex structures that would otherwise be impossible to produce using traditional machining. This freedom of complexity enables engineers to make more efficient components and reduce part counts in assemblies. However, the AM process tends to generate random flaws that require manufacturers to perform extensive testing on all manufactured samples to ensure part quality. Due to this extensive testing, manufacturers have been slow to adopt the AM process. Thus, the goal of this dissertation is to understand, monitor, and predict the quality of metal AM parts as they are being printed to remove the need for post-manufacturing testing – hence the phrase Born Qualified. To enable Born Qualified manufacturing with AM, the objective of this dissertation was to use sensors installed on AM machines to monitor part quality during the process. With this objective, this dissertation focused on: (1) using acoustic signal monitoring to determine the onset of process instabilities that would generate flaws; (2) monitoring the process with multiple sensors to determine the specific type of flaws formed; (3) developing novel methods to monitor the sub-surface effects; and (4) combining multiple streams of sensor data with thermal simulations to detect flaw formation along with mechanical and material properties of the manufactured parts.

Com as evoluções tecnológicas nas áreas de hardware, microeletrônica, sistemas de informação e computação, o conceito de sistemas ciberfísicos (do inglês Cyber-Physical Systems) vem ganhando importância. Este sistemas se referem à junção entre sistemas computacionais distribuídos e processos físicos da natureza e, são base fundamental para a nova revolução industrial que esta sendo introduzida. Esta revolução industrial é marcada pela completa descentralização do controle dos processos produtivos e uma proliferação de dispositivos inteligentes interconectados, ao longo de toda a cadeia de produção e logística. Sistemas de automação, e particularmente os sistemas de automação industrial, nos quais elementos computacionais controlam e automatizam a execução de processos físicos em plantas industriais, são um exemplo de sistemas ciber-físicos. Com isso, percebe-se que é necessário relacionar objetos físicos a informações associadas a este objeto no mundo cibernético. Para isso, destaca-se o conceito e o uso do Digital Twin, que é uma representação virtual de objetos físicos. O Digital Twin possibilita a virtualização e centralização do controle no produto. Este estudo irá explorar uma metodologia de modelagem genérica e flexível para o Digital Twin usando a ferramenta AutomationML e propor uma arquitetura de comunicação para a troca de dados sob a ótica de Cyber Physical Systems. Com a implementação dessa metodologia, pretende-se validar o conceito proposto e oferecer um método de modelagem e configuração para obter dados, extrair conhecimento e proporcionar sistemas de visualização para os usuários. / With technological advances in the fields of hardware, microelectronics and computer systems, Cyber Physical Systems is a new concept that is gaining importance. This systems are integrations of computation, networking, and physical processes. Cyber Physical Systems are one of the pillars for the new industrial revolution, and it is marked by the complete decentralization of the control of production processes and, marked by a proliferation of interconnected intelligent devices throughout the production and logistics chain. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. A industrial automation system, is an example of cyber physical systems where computational elements control and automate the execution of physical processes in industrial plants. Thus, it is clear the need to relate physical objects to information associated with this object in the cyber world. For this, this work pretends to use the concept of Digital Twin, that is a virtual representation of physical objects. Digital Twin enables the virtualization of physical components and descentralization of control. This study will explore a generic and flexible modeling methodology for Digital Twin using the AutomationML tool. Also this work proposes a communication architecture for the exchange of data from the perspective of Cyber Physical Systems. With the implementation of this methodology, we intend to validate the proposed concept and offer a modeling and configuration method to obtain data, extract knowledge and provide visualization systems for users.

Com as evoluções tecnológicas nas áreas de hardware, microeletrônica, sistemas de informação e computação, o conceito de sistemas ciberfísicos (do inglês Cyber-Physical Systems) vem ganhando importância. Este sistemas se referem à junção entre sistemas computacionais distribuídos e processos físicos da natureza e, são base fundamental para a nova revolução industrial que esta sendo introduzida. Esta revolução industrial é marcada pela completa descentralização do controle dos processos produtivos e uma proliferação de dispositivos inteligentes interconectados, ao longo de toda a cadeia de produção e logística. Sistemas de automação, e particularmente os sistemas de automação industrial, nos quais elementos computacionais controlam e automatizam a execução de processos físicos em plantas industriais, são um exemplo de sistemas ciber-físicos. Com isso, percebe-se que é necessário relacionar objetos físicos a informações associadas a este objeto no mundo cibernético. Para isso, destaca-se o conceito e o uso do Digital Twin, que é uma representação virtual de objetos físicos. O Digital Twin possibilita a virtualização e centralização do controle no produto. Este estudo irá explorar uma metodologia de modelagem genérica e flexível para o Digital Twin usando a ferramenta AutomationML e propor uma arquitetura de comunicação para a troca de dados sob a ótica de Cyber Physical Systems. Com a implementação dessa metodologia, pretende-se validar o conceito proposto e oferecer um método de modelagem e configuração para obter dados, extrair conhecimento e proporcionar sistemas de visualização para os usuários. / With technological advances in the fields of hardware, microelectronics and computer systems, Cyber Physical Systems is a new concept that is gaining importance. This systems are integrations of computation, networking, and physical processes. Cyber Physical Systems are one of the pillars for the new industrial revolution, and it is marked by the complete decentralization of the control of production processes and, marked by a proliferation of interconnected intelligent devices throughout the production and logistics chain. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. A industrial automation system, is an example of cyber physical systems where computational elements control and automate the execution of physical processes in industrial plants. Thus, it is clear the need to relate physical objects to information associated with this object in the cyber world. For this, this work pretends to use the concept of Digital Twin, that is a virtual representation of physical objects. Digital Twin enables the virtualization of physical components and descentralization of control. This study will explore a generic and flexible modeling methodology for Digital Twin using the AutomationML tool. Also this work proposes a communication architecture for the exchange of data from the perspective of Cyber Physical Systems. With the implementation of this methodology, we intend to validate the proposed concept and offer a modeling and configuration method to obtain data, extract knowledge and provide visualization systems for users.

Com as evoluções tecnológicas nas áreas de hardware, microeletrônica, sistemas de informação e computação, o conceito de sistemas ciberfísicos (do inglês Cyber-Physical Systems) vem ganhando importância. Este sistemas se referem à junção entre sistemas computacionais distribuídos e processos físicos da natureza e, são base fundamental para a nova revolução industrial que esta sendo introduzida. Esta revolução industrial é marcada pela completa descentralização do controle dos processos produtivos e uma proliferação de dispositivos inteligentes interconectados, ao longo de toda a cadeia de produção e logística. Sistemas de automação, e particularmente os sistemas de automação industrial, nos quais elementos computacionais controlam e automatizam a execução de processos físicos em plantas industriais, são um exemplo de sistemas ciber-físicos. Com isso, percebe-se que é necessário relacionar objetos físicos a informações associadas a este objeto no mundo cibernético. Para isso, destaca-se o conceito e o uso do Digital Twin, que é uma representação virtual de objetos físicos. O Digital Twin possibilita a virtualização e centralização do controle no produto. Este estudo irá explorar uma metodologia de modelagem genérica e flexível para o Digital Twin usando a ferramenta AutomationML e propor uma arquitetura de comunicação para a troca de dados sob a ótica de Cyber Physical Systems. Com a implementação dessa metodologia, pretende-se validar o conceito proposto e oferecer um método de modelagem e configuração para obter dados, extrair conhecimento e proporcionar sistemas de visualização para os usuários. / With technological advances in the fields of hardware, microelectronics and computer systems, Cyber Physical Systems is a new concept that is gaining importance. This systems are integrations of computation, networking, and physical processes. Cyber Physical Systems are one of the pillars for the new industrial revolution, and it is marked by the complete decentralization of the control of production processes and, marked by a proliferation of interconnected intelligent devices throughout the production and logistics chain. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. A industrial automation system, is an example of cyber physical systems where computational elements control and automate the execution of physical processes in industrial plants. Thus, it is clear the need to relate physical objects to information associated with this object in the cyber world. For this, this work pretends to use the concept of Digital Twin, that is a virtual representation of physical objects. Digital Twin enables the virtualization of physical components and descentralization of control. This study will explore a generic and flexible modeling methodology for Digital Twin using the AutomationML tool. Also this work proposes a communication architecture for the exchange of data from the perspective of Cyber Physical Systems. With the implementation of this methodology, we intend to validate the proposed concept and offer a modeling and configuration method to obtain data, extract knowledge and provide visualization systems for users.