Titlebook: DigiTwin: An Approach for Production Process Optimization in a Built Environment; Josip Stjepandi?,Markus Sommer,Berend Denkena Book 2022

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Book 2022m and its digital representation of complex engineering products and systems. It describes a methodology to create and use Digital Twin in a built environment for the improvement and optimization of factory processes such as factory planning, investment planning, bottleneck analysis, and in-house ma

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Book 2022es in process planning, and concludes by looking at future areas of progression. By providinga holistic, trans-disciplinary perspective, this book will showcase Digital Twin technology as state-of-the-art both in research and practice..

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Introduction to the Book,ed enterprises. For this market segment, appropriate concepts and solutions need to be elaborated and developed, including modern IT techniques such as Machine Learning. In this introductory chapter, the approach used to pave the way for this book is presented. The idea of Digital Twin, the origins,

Dorsal-Kyphosis

Requirements for the Optimization of Processes Using a Digital Twin of Production Systems,create a framework that is not only valid for a single use case, but allows for an arbitrary application which is as comprehensive as possible. This leads to a Digital Twin, which, in turn, can handle several use cases and is not focused on one use case like the simulation. The following chapter dea

Autobiography

Digital Twin: A Conceptual View,win which refer to the different phases of the product lifecycle: design, production and operation. Therefore, this chapter formulates a consistent and detailed definition of Digital Twins and gives insight in dedicated research direction. Finally, based on the Digital Twin characteristics, an appro

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Scan Methods and Tools for Reconstruction of Built Environments as Basis for Digital Twins, step, the color information is assigned to the measured points via the evaluation of photos. For certain applications, models resulting in highest accuracy and detail are not necessary; in these cases, speed and practicability of the acquisition is the primary concern. Due to recent advancements ph

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Machine Learning in Manufacturing in the Era of Industry 4.0,e state of the art with reinforcement learning methods gaining more attention in recent years. Subsequently, the gains of ML such as derivation of model configuration based on the data, generation of behavioral models through training, easy validation of the model and optimization of the real system