Digital Twin for Smart Manufacturing: The Practitioner’s Perspective

2020 ◽  
Author(s):  
Maja Bärring ◽  
Björn Johansson ◽  
Goudong Shao
Keyword(s):  
Conceptual Development ◽  
Production Systems ◽  
Manufacturing Sector ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Adoption And Implementation ◽  
New Information ◽  
Digital Twins ◽  
Discrete Manufacturing ◽  
Design Production

Abstract The manufacturing sector is experiencing a technological paradigm shift, where new information technology (IT) concepts can help digitize product design, production systems, and manufacturing processes. One of such concepts is Digital Twin and researchers have made some advancement on both its conceptual development and technological implementations. However, in practice, there are many different definitions of the digital-twin concept. These different definitions have created a lot of confusion for practitioners, especially small- and medium-sized enterprises (SMEs). Therefore, the adoption and implementation of the digital-twin concept in manufacturing have been difficult and slow. In this paper, we report our findings from a survey of companies (both large and small) regarding their understanding and acceptance of the digital-twin concept. Five supply-chain companies from discrete manufacturing and one trade organization representing suppliers in the automotive business were interviewed. Their operations have been studied to understand their current digital maturity levels and articulate their needs for digital solutions to stay competitive. This paper presents the results of the research including the viewpoints of these companies in terms of opportunities and challenges for implementing digital twins.

Design Framework for Preparing Workforce Towards Industry 4.0 Implementation in Small and Medium-Sized Enterprises

2020 ◽  
Author(s):  
Sagil James ◽  
Anupam Shetty
Keyword(s):  
Industry 4.0 ◽  
Industrial Revolution ◽  
Manufacturing Sector ◽  
Implementation Process ◽  
Secondary Data ◽  
Smart Manufacturing ◽  
New Paradigm ◽  
Skill Gaps ◽  
Adoption And Implementation ◽  
The Internet Of Things

Abstract The fourth industrial revolution, also known as Industry 4.0 is a new paradigm that is significantly influencing several manufacturing industries across the globe. Industry 4.0 synchronizes concepts such as Smart Manufacturing, Smart Factory, and the Internet of Things with existing factory automation technologies in order to improve value in manufacturing by monitoring key performance indicators and creates value in all manufacturing related aspects. Currently, several large companies industries have started early initiatives for implementing these technologies. However, small and medium-sized enterprises (SMEs) face massive challenges in adopting Industry 4.0 technologies. As the SMEs are evaluating their readiness for implementing the Industry 4.0 concepts, several challenges need to be addressed, including high initial investment, lack of standardization, data security, and lack of skilled labor. A strategic roadmap towards implementing the Industry 4.0 paradigms is still unclear in the SME sector as well as in academia. This research focuses on designing a framework for training/retraining the strong workforce for SMEs to enable Industry 4.0 adoption and implementation. The framework is created using qualitative research methods followed by the secondary data collection approach. The study suggests the use of a three-step implementation process consisting of 1) creating new jobs, 2) recruiting, and 3) retraining and retaining the talent. The results of this study are expected to create a platform to train the workforce for Industry 4.0, reduce skill gaps, and retain incumbent workers in the manufacturing sector.

Digital Twins Concepts, Challenges, and Future Trends

2021 ◽  
pp. 48-60
Author(s):  
Amged Sayed A. Mahmoud ◽  
Ezz El-Din Hemdan
Keyword(s):  
Life Cycle ◽  
Product Life Cycle ◽  
Smart Cities ◽  
Sensor Technology ◽  
Smart Manufacturing ◽  
Future Trends ◽  
Functional Description ◽  
Digital Twin ◽  
Research Areas ◽  
Digital Twins

Digital twin has gained a great interest during the last few years from academia and industry because of the development in IT technology, communication field, and sensor technology. The general vision of the DT is to provide a detailed physical and functional description of a component, product, and systems. Nevertheless, the digital twin is a highly dynamic concept growing in complexity during the product life cycle, which leads to an enormous amount of data and information. Motivated by these, this chapter investigates the concepts and architecture of DT to cover its challenges and explore its applications in various fields such as smart cities, smart manufacturing and factories, and healthcare sectors. In the end, the challenges and research areas will be presented.

scholarly journals An architecture of an Intelligent Digital Twin in a Cyber-Physical Production System

2019 ◽  
Vol 67 (9) ◽  
pp. 762-782 ◽  
Author(s):  
Behrang Ashtari Talkhestani ◽  
Tobias Jung ◽  
Benjamin Lindemann ◽  
Nada Sahlab ◽  
Nasser Jazdi ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Production Systems ◽  
Heterogeneous Data ◽  
Use Cases ◽  
Added Value ◽  
Automation System ◽  
The Real ◽  
Digital Twin ◽  
Digital Twins ◽  
Cyber Physical Production Systems

Abstract The role of a Digital Twin is increasingly discussed within the context of Cyber-Physical Production Systems. Accordingly, various architectures for the realization of Digital Twin use cases are conceptualized. There lacks, however, a clear, encompassing architecture covering necessary components of a Digital Twin to realize various use cases in an intelligent automation system. In this contribution, the added value of a Digital Twin in an intelligent automation system is highlighted and various existing definitions and architectures of the Digital Twin are discussed. Flowingly, an architecture for a Digital Twin and an architecture for an Intelligent Digital Twin and their required components are proposed, with which use cases such as plug and produce, self-x and predictive maintenance are enabled. In the opinion of the authors, a Digital Twin requires three main characteristics: synchronization with the real asset, active data acquisition from the real environment and the ability of simulation. In addition to all the characteristics of a Digital Twin, an Intelligent Digital Twin must also include the characteristics of Artificial Intelligence. The Intelligent Digital Twin can be used for the realization of the autonomous Cyber-Physical Production Systems. In order to realize the proposed architecture for a Digital Twin, several methods, namely the Anchor-Point-Method, a method for heterogeneous data acquisition and data integration as well as an agent-based method for the development of a co-simulation between Digital Twins were implemented and evaluated.

scholarly journals An Enabling Open-Source Technology for Development and Prototyping of Production Systems by Applying Digital Twinning

Processes ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Robert Kazała ◽  
Sławomir Luściński ◽  
Paweł Strączyński ◽  
Albena Taneva
Keyword(s):  
Open Source ◽  
Production Systems ◽  
Communication Technologies ◽  
Digital Twin ◽  
Twin System ◽  
Digital Twins ◽  
First Choice ◽  
Automation And Control ◽  
And Control

This article presents the most valuable and applicable open-source tools and communication technologies that may be employed to create models of production processes by applying the concept of Digital Twins. In recent years, many open-source technologies, including tools and protocols, have been developed to create virtual models of production systems. The authors present the evolution and role of the Digital Twin concept as one of the key technologies for implementing the Industry 4.0 paradigm in automation and control. Based on the presented structured review of valuable open-source software dedicated to various phases and tasks that should be realised while creating the whole Digital Twin system, it was demonstrated that the available solutions cover all aspects. However, the dispersion, specialisation, and lack of integration cause this software to usually not be the first choice to implement DT. Therefore, to successfully create full-fledged models of Digital Twins by proceeding with proposed open-source solutions, it is necessary to make additional efforts due to integration requirements.

scholarly journals Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 115
Author(s):  
Elia Henrichs ◽  
Tanja Noack ◽  
Ana María Pinzon Pinzon Piedrahita ◽  
María Alejandra Salem ◽  
Johnathan Stolz ◽  
...  
Keyword(s):  
Food Processing ◽  
Food Industry ◽  
Production Systems ◽  
Food Supply Chain ◽  
Real World Data ◽  
Processing Stage ◽  
Digital Twin ◽  
Digital Twins ◽  
Production Agriculture ◽  
Growing Population

The food industry faces many challenges, including the need to feed a growing population, food loss and waste, and inefficient production systems. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real-world data seem to be a promising approach. This paper aims to provide an overview of digital twin applications in the food industry and analyze their challenges and potentials. Therefore, a literature review is executed to examine digital twin applications in the food supply chain. The applications found are classified according to a taxonomy and key elements to implement digital twins are identified. Further, the challenges and potentials of digital twin applications in the food industry are discussed. The survey revealed that the application of digital twins mainly targets the production (agriculture) or the food processing stage. Nearly all applications are used for monitoring and many for prediction. However, only a small amount focuses on the integration in systems for autonomous control or providing recommendations to humans. The main challenges of implementing digital twins are combining multidisciplinary knowledge and providing enough data. Nevertheless, digital twins provide huge potentials, e.g., in determining food quality, traceability, or designing personalized foods.

scholarly journals Digital Technologies in the Smart Production Management System

2021 ◽  
Vol 93 ◽  
pp. 01024
Author(s):  
Evgeniy Starikov ◽  
Marina Evseeva ◽  
Irina Tkachenko
Keyword(s):  
Management System ◽  
Production Management ◽  
Manufacturing Sector ◽  
Digital Technologies ◽  
Digital Twin ◽  
Twin Model ◽  
Digital Twins ◽  
Industrial Internet ◽  
Production Management System ◽  
Twin Models

The article analyzes the possibility of using such digital technologies as the Industrial Internet of Things (IIoT), Big Data and the creation of models of digital twins in the formation of intelligent management systems for "smart" production within the framework of the concept of digital transformation of the manufacturing sector Industry 4.0. The essence and features of these technologies, problematic aspects of their implementation in real production enterprises are considered. The concept of the functional structure of the digital production management system of a "smart" enterprise based on the digital twin model is proposed. The conclusion is made about the integrating role of technologies for the development and application of digital twin models in the construction of intelligent control systems for "smart" production.

scholarly journals Sustainable Industry 4.0 Wireless Networks, Machine Learning Algorithms, and Internet of Things-based Real-Time Production Logistics in Digital Twin-driven Smart Manufacturing

2021 ◽  
Vol 129 ◽  
pp. 04003
Author(s):  
Elvira Nica ◽  
Gheorghe H. Popescu ◽  
George Lăzăroiu
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Industry 4.0 ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Cyber Physical Production Systems

Research background: The aim of this paper is to synthesize and analyze existing evidence on artificial intelligence-based decision-making algorithms, industrial big data, and Internet of Things sensing networks in digital twin-driven smart manufacturing. Purpose of the article: Using and replicating data from Altair, Catapult, Deloitte, DHL, GAVS, PwC, and ZDNet we performed analyses and made estimates regarding cyber-physical system-based real-time monitoring, product decision-making information systems, and artificial intelligence data-driven Internet of Things systems in digital twin-based cyber-physical production systems. Methods: From the completed surveys, we calculated descriptive statistics of compiled data when appropriate. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. An Internet-based survey software program was utilized for the delivery and collection of responses. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau’s American Community Survey to reflect reliably and accurately the demographic composition of the United States. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Findings & Value added: The way Internet of Things-based decision support systems, artificial intelligence-driven big data analytics, and robotic wireless sensor networks configure digital twin-driven smart manufacturing and cyber-physical production systems in sustainable Industry 4.0.

scholarly journals Digital Twin of Experimental Smart Manufacturing Assembly System for Industry 4.0 Concept

2020 ◽  
Vol 12 (9) ◽  
pp. 3658 ◽  
Author(s):  
Kamil Židek ◽  
Ján Piteľ ◽  
Milan Adámek ◽  
Peter Lazorík ◽  
Alexander Hošovský
Keyword(s):  
Vision System ◽  
Smart Manufacturing ◽  
Assembly System ◽  
Inspection System ◽  
Main Task ◽  
Digital Twin ◽  
Open Platform ◽  
Twin Model ◽  
Digital Twins ◽  
Manufacturing Assembly

This article deals with the creation of a digital twin for an experimental assembly system based on a belt conveyor system and an automatized line for quality production check. The point of interest is a Bowden holder assembly from a 3D printer, which consists of a stepper motor, plastic components, and some fastener parts. The assembly was positioned in a fixture with ultra high frequency (UHF) tags and internet of things (IoT) devices for identification of status and position. The main task was parts identification and inspection, with the synchronization of all data to a digital twin model. The inspection system consisted of an industrial vision system for dimension, part presence, and errors check before and after assembly operation. A digital twin is realized as a 3D model, created in CAD design software (CDS) and imported to a Tecnomatix platform to simulate all processes. Data from the assembly system were collected by a programmable logic controller (PLC) system and were synchronized by an open platform communications (OPC) server to a digital twin model and a cloud platform (CP). Digital twins can visualize the real status of a manufacturing system as 3D simulation with real time actualization. Cloud platforms are used for data mining and knowledge representation in timeline graphs, with some alarms and automatized protocol generation. Virtual digital twins can be used for online optimization of an assembly process without the necessity to stop that is involved in a production line.

scholarly journals Potential for combining semantics and data analysis in the context of digital twins

2021 ◽  
Vol 379 (2207) ◽  
pp. 20200368
Author(s):  
Birgit Vogel-Heuser ◽  
Felix Ocker ◽  
Iris Weiß ◽  
Robert Mieth ◽  
Frederik Mann
Keyword(s):  
Data Analysis ◽  
Production Systems ◽  
Expert Knowledge ◽  
Autonomous Systems ◽  
Feasibility Studies ◽  
Smart Manufacturing ◽  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Digital Twins ◽  
Combining Data

Modern production systems can benefit greatly from integrated and up-to-date digital representations. Their applications range from consistency checks during the design phase to smart manufacturing to maintenance support. Such digital twins not only require data, information and knowledge as inputs but can also be considered integrated models themselves. This paper provides an overview of data, information and knowledge typically available throughout the lifecycle of production systems and the variety of applications driven by data analysis, expert knowledge and knowledge-based systems. On this basis, we describe the potential for combining data analysis and knowledge-based systems in the context of production systems and describe two feasibility studies that demonstrate how knowledge-based systems can be created using data analysis. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

Edge Computing Enhanced Digital Twins for Smart Manufacturing

2021 ◽  
Author(s):  
Huiyue Huang ◽  
Xun Xu
Keyword(s):  
Real Time ◽  
Information And Communication Technologies ◽  
Research Work ◽  
Edge Computing ◽  
Smart Manufacturing ◽  
Data Mapping ◽  
Digital Twin ◽  
Mapping Strategy ◽  
Digital Twins ◽  
Automation Systems

Abstract Digital Twin is one of the key enabling technologies for smart manufacturing in the context of Industry 4.0. The combination with advanced data analytics and information and communication technologies allows Digital Twins to perform real-time simulation, optimization and prediction to their physical counterparts. Efficient bi-directional data exchange is the foundation for Digital Twin implementation. However, the widely mentioned cloud-based architecture has disadvantages, such as high pressure on bandwidth and long latency time, which limit Digital Twins to provide real-time operating responses in dynamic manufacturing processes. Edge computing has the characteristics of low connectivity, the capability of immediate analysis and access to temporal data for real-time analytics, which makes it a fit-for-purpose technology for Digital Twin development. In this paper, the benefits of edge computing to Digital Twin are first explained through the reviews of the two technologies. The Digital Twin functions to be performed at the edge are then elaborated. After that, how the data model will be used in the edge for data mapping to realize the Digital Twin is illustrated and the data mapping strategy based on the EXPRESS schemas is discussed. Finally, a case study is carried out to verify the data mapping strategy based on EXPRESS schema. This research work refers to ISO/DIS 23247 Automation systems and integration — Digital Twin framework for manufacturing.

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