scholarly journals CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin

2021 ◽  
Vol 4 ◽  
Author(s):  
Evgeny Zotov ◽  
Visakan Kadirkamanathan
Keyword(s):  
Knowledge Transfer ◽  
Industry 4.0 ◽  
Transfer Problem ◽  
The Novel ◽  
Process Optimisation ◽  
Target Domain ◽  
Adaptation Algorithm ◽  
Generative Adversarial Network ◽  
Physical Systems ◽  
Digital Twin

Digitalisation of manufacturing is a crucial component of the Industry 4.0 transformation. The digital twin is an important tool for enabling real-time digital access to precise information about physical systems and for supporting process optimisation via the translation of the associated big data into actionable insights. Although a variety of frameworks and conceptual models addressing the requirements and advantages of digital twins has been suggested in the academic literature, their implementation has received less attention. The work presented in this paper aims to make a proposition that considers the novel challenges introduced for data analysis in the presence of heterogeneous and dynamic cyber-physical systems in Industry 4.0. The proposed approach defines a digital twin simulation tool that captures the dynamics of a machining vibration signal from a source model and adapts them to a given target environment. This constitutes a flexible approach to knowledge extraction from the existing manufacturing simulation models, as information from both physics-based and data-driven solutions can be elicited this way. Therefore, an opportunity to reuse the costly established systems is made available to the manufacturing businesses, and the paper presents a process optimisation framework for such use case. The proposed approach is implemented as a domain adaptation algorithm based on the generative adversarial network model. The novel CycleStyleGAN architecture extends the CycleGAN model with a style-based signal encoding. The implemented model is validated in an experimental scenario that aims to replicate a real-world manufacturing knowledge transfer problem. The experiment shows that the transferred information enables the reduction of the required target domain data by one order of magnitude.

scholarly journals Industry 4.0: A Proposal of Paradigm Organization Schemes from a Systematic Literature Review

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 66
Author(s):  
Cristian Rocha-Jácome ◽  
Ramón González Carvajal ◽  
Fernando Muñoz Chavero ◽  
Esteban Guevara-Cabezas ◽  
Eduardo Hidalgo Fort
Keyword(s):  
Human Resources ◽  
Industry 4.0 ◽  
Scientific Information ◽  
Cyber Physical Systems ◽  
Digital Transformation ◽  
The Novel ◽  
Alternative Analysis ◽  
Physical Systems ◽  
Simple Alternative ◽  
Successful Execution

Currently, the concept of Industry 4.0 is well known; however, it is extremely complex, as it is constantly evolving and innovating. It includes the participation of many disciplines and areas of knowledge as well as the integration of many technologies, both mature and emerging, but working in collaboration and relying on their study and implementation under the novel criteria of Cyber–Physical Systems. This study starts with an exhaustive search for updated scientific information of which a bibliometric analysis is carried out with results presented in different tables and graphs. Subsequently, based on the qualitative analysis of the references, we present two proposals for the schematic analysis of Industry 4.0 that will help academia and companies to support digital transformation studies. The results will allow us to perform a simple alternative analysis of Industry 4.0 to understand the functions and scope of the integrating technologies to achieve a better collaboration of each area of knowledge and each professional, considering the potential and limitations of each one, supporting the planning of an appropriate strategy, especially in the management of human resources, for the successful execution of the digital transformation of the industry.

The Knowledge Transfer Problem in Systems Engineering

ASCEND 2020 ◽  
2020 ◽  
Author(s):  
Amanda Banks ◽  
Christopher J. White ◽  
Casey Eaton ◽  
Bryan Mesmer
Keyword(s):  
Knowledge Transfer ◽  
Systems Engineering ◽  
Transfer Problem

scholarly journals The challenges, approaches, and used techniques of CPS for manufacturing in Industry 4.0: a literature review

2021 ◽  
Vol 113 (7-8) ◽  
pp. 2395-2412
Author(s):  
Baudouin Dafflon ◽  
Nejib Moalla ◽  
Yacine Ouzrout
Keyword(s):  
Internet Of Things ◽  
Industry 4.0 ◽  
Cyber Physical Systems ◽  
Review Literature ◽  
Comprehensive Understanding ◽  
Physical Systems ◽  
Fine Grained ◽  
Key Technologies ◽  
Granularity Level ◽  
Human Component

AbstractThis work aims to review literature related to the latest cyber-physical systems (CPS) for manufacturing in the revolutionary Industry 4.0 for a comprehensive understanding of the challenges, approaches, and used techniques in this domain. Different published studies on CPS for manufacturing in Industry 4.0 paradigms through 2010 to 2019 were searched and summarized. We, then, analyzed the studies at a different granularity level inspecting the title, abstract, and full text to include in the prospective study list. Out of 626 primarily extracted relevant articles, we scrutinized 78 articles as the prospective studies on CPS for manufacturing in Industry 4.0. First, we analyzed the articles’ context to identify the major components along with their associated fine-grained constituents of Industry 4.0. Then, we reviewed different studies through a number of synthesized matrices to narrate the challenges, approaches, and used techniques as the key-enablers of the CPS for manufacturing in Industry 4.0. Although the key technologies of Industry 4.0 are the CPS, Internet of Things (IoT), and Internet of Services (IoS), the human component (HC), cyber component (CC), physical component (PC), and their HC-CC, CC-PC, and HC-PC interfaces need to be standardized to achieve the success of Industry 4.0.

scholarly journals Using Digital Twin Technology in Engineering Education – Course Concept to Explore Benefits and Barriers

2020 ◽  
Vol 10 (1) ◽  
pp. 377-385 ◽  
Author(s):  
Antti Liljaniemi ◽  
Heikki Paavilainen
Keyword(s):  
Engineering Education ◽  
Industry 4.0 ◽  
Digital Technologies ◽  
Digital Twin ◽  
New Knowledge

AbstractDigital Twin (DT) technology is an essential technology related to the Industry 4.0. In engineering education, it is important that the curricula are kept up-to-date. By adopting new digital technologies, such as DT, we can provide new knowledge for students, teachers, and companies. The main aim of this research was to create a course concept to research benefits and barriers of DT technology in engineering education. The research confirmed earlier findings concerning digitalization in engineering education. DT technology can increase motivation for studying and improve learning when applied correctly.

scholarly journals Robot Digital Twin towards Industry 4.0

2020 ◽  
Vol 53 (2) ◽  
pp. 10867-10872
Author(s):  
Luige Vlădăreanu ◽  
Alexandru I. Gal ◽  
Octavian D. Melinte ◽  
Victor Vlădăreanu ◽  
Mihaiela Iliescu ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Digital Twin

scholarly journals Artificial Intelligence and the Internet of Things in Industry 4.0

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Razvan Nicolescu ◽  
Michael Huth ◽  
Omar Santos
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Leading Industry ◽  
Key Technologies ◽  
The Internet Of Things ◽  
State Of Art ◽  
Social Level

AbstractThis paper presents a new design for artificial intelligence in cyber-physical systems. We present a survey of principles, policies, design actions and key technologies for CPS, and discusses the state of art of the technology in a qualitative perspective. First, literature published between 2010 and 2021 is reviewed, and compared with the results of a qualitative empirical study that correlates world leading Industry 4.0 frameworks. Second, the study establishes the present and future techniques for increased automation in cyber-physical systems. We present the cybersecurity requirements as they are changing with the integration of artificial intelligence and internet of things in cyber-physical systems. The grounded theory methodology is applied for analysis and modelling the connections and interdependencies between edge components and automation in cyber-physical systems. In addition, the hierarchical cascading methodology is used in combination with the taxonomic classifications, to design a new integrated framework for future cyber-physical systems. The study looks at increased automation in cyber-physical systems from a technical and social level.

scholarly journals Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 487 ◽  
Author(s):  
Mahmoud Elsisi ◽  
Karar Mahmoud ◽  
Matti Lehtonen ◽  
Mohamed M. F. Darwish
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Industry 4.0 ◽  
Learning Algorithm ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Smart Meters ◽  
Industrial Environment ◽  
Physical Systems ◽  
Smart Machines

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.

scholarly journals Introducing Gamification in the AR-Enhanced Order Picking Process: A Proposed Approach

Logistics ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Athina G. Bright ◽  
Stavros T. Ponis
Keyword(s):  
Industry 4.0 ◽  
Business Processes ◽  
Cyber Physical Systems ◽  
Order Picking ◽  
Physical Systems ◽  
The Core ◽  
Complex Process ◽  
Core Elements ◽  
Game Elements ◽  
Innovative Industry

In the last decade, the Industry 4.0 concept has introduced automation and cyber-physical systems as the core elements of future logistics, supported by an array of technologies, such as augmented reality (AR) providing the necessary support for the digital transformation of manufacturing and logistics and the smartification and digital refinement of traditional pre-Industry 4.0 processes. This paper studies the influence and the potential of gamification techniques in supporting innovative Industry 4.0-enhanced processes in the contemporary warehouse work ecosystem. Gamification in the workplace aims to motivate the employees and increase their involvement in an activity, while at the same time creating a sense of an everyday different experience rather than a set of repetitive and monotonous tasks. Since the design of such a system is a complex process, the most widespread design frameworks are studied, and the emphasis is on the principal game elements and their connection to mobilization mechanisms. Finally, an initial proposal of a gamification framework to support the AR-enhanced order picking process in contemporary logistics centers is provided with an emphasis on the mechanics of a fair and functional reward system. The proposed approach aims to showcase the potential alignment of business processes to human motivation, respecting the differences between tasks and the workers’ cognitive workload.

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