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2022 | Book

Digital Twins in Manufacturing

Virtual and Physical Twins for Advanced Manufacturing

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About this book

This book presents a guide to digital twin technologies and their applications within manufacturing. It examines key technological advances in the area of Industry 4.0, including numerical and experimental models and the Internet of Things (IoT), and explores their potential technical benefits through real-world application examples. This book presents digital models of advanced manufacturing processes dynamics that enable to control the cutting processes including experimental and simulation studies for brittle-ductile transition of ultra-precision machining materials assuring product quality. Innovative electrical power harvesting solutions from tool vibrations and wireless data transmission from confined and heavily cooled environment are also included. It explains the benefits of virtual and physical twins adapted to real systems, including the ability to shorten the product's path to the market, and enabling the transition to higher value-added manufacturing processes. Including numerous illustrations and clear solved problems, this book will be of interest to researchers and industry professionals in the fields of mechatronics, manufacturing engineering, computational mechanics.

Table of Contents

Frontmatter
Chapter 1. The State-of-the-Art in the Theoretical and Practical Applications of the Digital Twins Components
Abstract
This chapter introduces the state-of-the-art in theoretical and practical applications of the digital twins components. Advances in manufacturing process modeling and prediction, smart technologies, energy harvesting, machine learning, the Internet of Things, edge and cloud computing have contributed significantly to the improvements of digital twins in their real-time monitoring and forecasting properties. This monograph focuses on the development of these tools beyond the state-of-the-art.
Vytautas Ostaševičius
Chapter 2. Digital Twins for Smart Manufacturing
Abstract
Smart manufacturing is a vital part of the broader concept of Industry 4.0. Its foundation is the bridge between virtual and physical environments, developed on the Internet of Things (IoT) and other contemporary technologies, such as cloud systems, data analytics, and machine learning. A cutting process controlled by digital twins can be a modern solution for manufacturing. To ensure the correct behavior of a complex manufacturing system, modern engineering uses model-based simulation and data analysis to predict the outcome, optimize, adjust, and evaluate at all stages, not only in the initial design, but also in the development, production, and monitoring phases. Such continuous data collection using virtual twin simulation and physical twin experimentation is related to modified vibratory turning and drilling tool structures, macro- and micro-drilling processes, improving the quality of grinding operations, and the application of Artificial Intelligence (AI) prediction methods for robotic sheet forming. As applications of the latter process gain momentum, solutions associated with local heating of the polymer sheet become more acceptable than expensive 3D printing processes, while the replacement of eco-unfriendly lubrication by ultrasonic metal sheet excitation allows the problems of the green economy to be addressed more quickly.
Vytautas Ostaševičius
Chapter 3. Integration of Digital and Physical Data to Process Difficult-to-Cut Materials
Abstract
Manufacturing high-quality products at the lowest possible cost requires an understanding of the often complex relationships between many factors. The current plethora of different materials and manufacturing processes has made it both a challenge and an opportunity to select the right combination to produce a high quality product while minimising costs. Ultra-precision diamond turning has been used to machine workpieces with a surface roughness Ra < 5 nm and a form accuracy < 250 nm. This process mainly uses materials such as aluminum, copper, electroless nickel and some plastics. Conventional ultra precision diamond turning cannot be used to machine ferrous metals such as hardened steel. A chemical reaction takes place between the carbon in the diamond and the iron in the steel, which greatly increases the wear on the tool. The method using ultrasonic vibrations close to 100 kHz in the diamond turning process has been developed to reduce the contact time between the tool and the workpiece. Vibration assisted milling could be useful for surface finish of difficult-to-cut metallic alloys. Vibration-assisted drilling is efficient when high-frequency vibrations excite the workpiece and enables the processing of fragile materials such as glass or ceramics. In order to induce the brittle-ductile transition of hard material, a method and patented equipment have been developed that enable the actuator of the workpiece to be excited at higher vibration modes, resulting in a good machining quality.
Vytautas Ostaševičius
Chapter 4. Wireless Connectivity Options for Tool Condition Monitoring IoT Applications
Abstract
A piezoelectric transmission mechanism is a common way of converting vibration energy into electrical energy. Piezoelectric energy harvesters attached to a vibrating structure generate an alternating output voltage due to a dynamic strain field. Traditionally, the frequency of the first vibration mode of the flexible link of a piezoelectric harvester is combined with the excitation frequency in order to increase the amplitude of the resonant oscillations and thus the amount of harvested energy. Significantly, higher amounts of energy can be obtained by exciting flexible links on the higher eigenmodes and locating segments of the piezoelectric elements, where the dynamic strain field of structure changes the sign. Similar to the dynamic properties of optimally configured tools presented in Chap. 2, optimally shaped piezoelectric harvesters are also associated with the potential for increased electric power harvesting.
Vytautas Ostaševičius
Chapter 5. Digital Twin-Driven Technological Process Monitoring for Edge Computing and Cloud Manufacturing Applications
Abstract
The use of the cloud-trained digital twin at the edge opens up new possibilities for autonomous systems, such as novel real-time artificial intelligence applications. The edge-deployed digital twin can continually improve through self-learning, while ensuring that its cloud counterpart is synchronized and up-to-date. When running a digital twin on the edge, applications, for example, device protection features, including shutoff, can be used instantly if digital twin analysis detects or predicts a hazard. Analytics created by the digital twin can help local control and vice versa. This leads to proactive control applications that ensure autonomous operation. Edge computing offers clear advantages if there are problems with low latency, connectivity, security, privacy, and data transfer. The trade-offs are relatively high initial costs and maintenance costs. On the other side of the scale is cloud manufacturing, where the initial cost and maintenance overhead are average, but latency, connectivity, and the amount of data transferred can be a problem with the associated costs. With IoT, manufacturing companies will increasingly manage data, and cloud and edge computing will provide a high level of “smartness” to their operating environment. Using techniques such as online machine learning on data streaming and real-time learning, the digital twin can continuously self-learn and evolve.
Vytautas Ostaševičius
Metadata
Title
Digital Twins in Manufacturing
Author
Prof. Vytautas Ostaševičius
Copyright Year
2022
Electronic ISBN
978-3-030-98275-1
Print ISBN
978-3-030-98274-4
DOI
https://doi.org/10.1007/978-3-030-98275-1

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