A unified digital twin framework for shop floor design in industry 4.0 manufacturing systems

2021 ◽  
Vol 27 ◽  
pp. 87-91
Author(s):  
Jay Lee ◽  
Moslem Azamfar ◽  
Behrad Bagheri
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Shop Floor ◽  
Digital Twin

A Digital Twin Concept for Manufacturing Systems

2018 ◽  
Author(s):  
Wesley Ellgass ◽  
Nathan Holt ◽  
Hector Saldana-Lemus ◽  
Julian Richmond ◽  
Ali Vatankhah Barenji ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Virtual Space ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Digital Twin ◽  
Physical Resources ◽  
Virtual Resources

With the developments and applications of the advanced information technologies such as cloud computing, internet of thing, artificial intelligence and virtual reality, industry 4.0 and smart manufacturing era are coming. In this respect, one of the specific challenges is to achieve a connection of physical resources on the shop floor with virtual resources, for real-time response, real time process optimization, and simulation, which is merged by big data problem. In this respect, Digital Twins (DT) concept is introduced as a key technology, which includes physical resources, virtual resources, service system, and digital twin data. DT considers current condition of physical resource and prediction of future events to make a responsive decision. However, due to the complexity of building a digital equivalent in virtual space to its physical counterpart, very little applications have been developed with this purpose, especially in the industrial manufacturing area. Therefore, the types of data and technology required to build the DT for a manufacturing system are presented in this work, trying to develop a framework of DT based manufacturing system, which is supported by the virtual reality for virtualization of physical resources.

Self-Organizing Manufacturing Systems in Industry 4.0

2021 ◽  
pp. 346-363
Author(s):  
Blaž Rodič
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Simulation Modelling ◽  
Physical World ◽  
Self Organization ◽  
Digital Twin ◽  
Twin Model ◽  
Decision Making System ◽  
Novel Concept ◽  
Self Organizing

This chapter presents the evolution of simulation modelling methodology in the context of the Industry 4.0 paradigm and the development of autonomous, self-organizing manufacturing systems. Such a system is managed by a decision-making system that uses a detailed model of the factory, known as the “digital twin” to monitor and control the manufacturing process and test possible process reorganization scenarios. To allow self-organization within the physical world, the “digital twin” model must itself be self-organizing. That means that the structure of the simulation model can be constructed from process data, which is a novel concept, called data-driven modelling. As self-organization leads to the reorganization of existing elements and their relationships within a system, we can treat such manufacturing systems as autopoietic. The chapter introduces the Industry 4.0 paradigm and its background and presents the main self-organizing manufacturing concepts, and the state of technology supporting these concepts.

scholarly journals Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

2019 ◽  
Vol 9 (16) ◽  
pp. 3325 ◽  
Author(s):  
Tran ◽  
Park ◽  
Nguyen ◽  
Hoang
Keyword(s):  
Radio Frequency Identification ◽  
Mass Production ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Shop Floor ◽  
Self Healing ◽  
Manufacturing Environment ◽  
Information And Communication ◽  
Ict Infrastructure

The complexity and dynamic of the manufacturing environment are growing due to the changes of manufacturing demand from mass production to mass customization that require variable product types, small lot sizes, and a short lead-time to market. Currently, the automatic manufacturing systems are suitable for mass production. To cope with the changes of the manufacturing environment, the paper proposes the model and technologies for developing a smart cyber-physical manufacturing system (Smart-CPMS). The transformation of the actual manufacturing systems to the Smart-CPMS is considered as the next generation of manufacturing development in Industry 4.0. The Smart-CPMS has advanced characteristics inspired from biology such as self-organization, self-diagnosis, and self-healing. These characteristics ensure that the Smart-CPMS is able to adapt with continuously changing manufacturing requirements. The model of Smart-CPMS is inherited from the organization of living systems in biology and nature. Consequently, in the Smart-CPMS, each resource on the shop floor such as machines, robots, transporters, and so on, is an autonomous entity, namely a cyber-physical system (CPS) which is equipped with cognitive capabilities such as perception, reasoning, learning, and cooperation. The Smart-CPMS adapts to the changes of manufacturing environment by the interaction among CPSs without external intervention. The CPS implementation uses the cognitive agent technology. Internet of things (IoT) with wireless networks, radio frequency identification (RFID), and sensor networks are used as information and communication technology (ICT) infrastructure for carrying out the Smart-CPMS.

scholarly journals An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5480 ◽  
Author(s):  
Panagiotis Trakadas ◽  
Pieter Simoens ◽  
Panagiotis Gkonis ◽  
Lambros Sarakis ◽  
Angelos Angelopoulos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Business Intelligence ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Shop Floor ◽  
Efficient Production ◽  
Human In The Loop ◽  
Industrial Iot ◽  
Potential Applications

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.

scholarly journals Automation Pyramid as Constructor for a Complete Digital Twin, Case Study: A Didactic Manufacturing System

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4656
Author(s):  
Edwin Mauricio Martinez ◽  
Pedro Ponce ◽  
Israel Macias ◽  
Arturo Molina
Keyword(s):  
Industry 4.0 ◽  
Manufacturing System ◽  
Industrial Process ◽  
Operating Conditions ◽  
Shop Floor ◽  
Virtual Model ◽  
Digital Twin ◽  
Disruptive Technologies ◽  
Pyramid Model

Nowadays, the concept of Industry 4.0 aims to improve factories’ competitiveness. Usually, manufacturing production is guided by standards to segment and distribute its processes and implementations. However, industry 4.0 requires innovative proposals for disruptive technologies that engage the entire production process in factories, not just a partial improvement. One of these disruptive technologies is the Digital Twin (DT). This advanced virtual model runs in real-time and can predict, detect, and classify normal and abnormal operating conditions in factory processes. The Automation Pyramid (AP) is a conceptual element that enables the efficient distribution and connection of different actuators in enterprises, from the shop floor to the decision-making levels. When a DT is deployed into a manufacturing system, generally, the DT focuses on the low-level that is named field level, which includes the physical devices such as controllers, sensors, and so on. Thus, the partial automation based on the DT is accomplished, and the information between all manufacturing stages could be decremented. Hence, to achieve a complete improvement of the manufacturing system, all the automation pyramid levels must be included in the DT concept. An artificial intelligent management system could create an interconnection between them that can manage the information. As a result, this paper proposed a complete DT structure covering all automation pyramid stages using Artificial Intelligence (AI) to model each stage of the AP based on the Digital Twin concept. This work proposes a virtual model for each level of the traditional AP and the interactions among them to flow and control information efficiently. Therefore, the proposed model is a valuable tool in improving all levels of an industrial process. In addition, It is presented a case study where the DT concept for modular workstations underpins the development of technologies within the framework of the Automation Pyramid model is implemented into a didactic manufacturing system.

scholarly journals The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES)

2016 ◽  
Vol 3 (4) ◽  
pp. 16-21 ◽  
Author(s):  
Francisco Almada-Lobo
Keyword(s):  
Cloud Computing ◽  
Paradigm Shift ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Production Control ◽  
Shop Floor ◽  
Manufacturing Execution Systems ◽  
The Future ◽  
Smart Factories ◽  
Manufacturing Execution

Industry 4.0 dictates the end of traditional centralized applications for production control. Its vision of ecosystems of smart factories with intelligent and autonomous shop-floor entities is inherently decentralized. Responding to customer demands for tailored products, these plants fueled by technology enablers such as 3D printing, Internet of Things, Cloud computing, Mobile Devices and Big Data, among others create a totally new environment. The manufacturing systems of the future, including manufacturing execution systems (MES) will have to be built to support this paradigm shift.

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
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