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.

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.

<p>The modern world of manufacturing is in the middle of an industrial revolution with the digital and physical worlds integrating through cyber-physical systems.  Through a virtual model that is able to communicate with its physical system known as the Digital Twin, catered decisions can be made based on the current state of the system.  The digital twin presents immense opportunities and challenges as there is a greater need to understand how these new technologies work together. </p> <p><br></p> <p>This thesis is an experimental investigation of the characteristics of the essential components of the Digital Twin.  A Digital Twin Framework is developed to explore the impacts of model accuracy and update frequency on the system’s performance measure. A simple inventory management system and a more complex manufacturing plant is modeled through the framework providing a method to study the interactions of the physical and digital systems with empirical data.</p> <p><br></p> <p><br></p> <p>As the decision policies are affected by the state changes in the system, designing the Digital Twin must account for the direct and indirect impact of its components. </p> <p>Furthermore, we show the importance of communication and information exchange between the Digital Twin and its physical system.  A key characteristic for developing and applying a digital twin is to monitor the update frequency and its impact on performance.  Through the study there are implications of optimal combinations of the digital twin components and how the physical system responds.  There are also limits to how effective the Digital Twin can be in certain instances and is an area of research that needs further investigation.  </p> <p><br></p> <p>The goal of this work is to help practitioners and researchers implement and use the Digital Twin more effectively.  Better understanding the interactions of the model components will help guide designing Digital Twins to be more effective as they become an integral part of the future of manufacturing.</p>

This paper will give an overview about the technological change in Industrial Hydraulics and the impact of the Digital Twin on the related new engineering processes and methods in order to overcome the challenges coming out of that technology change. Simulation models will more and more become a decisive factor for the engineering process. The Digital Twin will be a window of opportunity for innovations and a technology push for the engineering process and the products in the Industrial Hydraulics.