scholarly journals A Development Methodology Framework of Smart Manufacturing Systems (Industry 4.0)

2020 ◽  
Vol 10 (5) ◽  
pp. 1927
Author(s):  
Moamin A Mahmoud ◽  
Ramona Ramli ◽  
Feninferina Azman ◽  
Jennifer Grace
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Development Methodology ◽  
Smart Manufacturing Systems

Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

2021 ◽  
pp. 1737-1766
Author(s):  
Chetna Chauhan ◽  
Amol Singh
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Relative Importance ◽  
Fuzzy Analytical Hierarchy Process ◽  
Delphi Approach ◽  
Fuzzy Delphi ◽  
Privacy Risks ◽  
Smart Manufacturing Systems ◽  
Hierarchy Process

The pace of Industry 4.0 adoption in manufacturing industries has been slow as it is accompanied by several barriers, specifically in the emerging economies. The current study intends to identify and understand the landscape of these challenges. Further, this paper prioritizes the challenges on the basis of their relative importance. To achieve this objective, the authors combine the fuzzy delphi approach along with the fuzzy analytical hierarchy process. Additionally, a sensitivity analysis is done to enhance robustness of the findings. The global rankings of the challenges reveal that the most significant factors that hamper the full realization of smart manufacturing include cybersecurity, privacy risks, and enormously high number of technology choices available in the market. The analysis offers insights into the reasons for the slow diffusion of smart manufacturing systems and the results would assist managers, policymakers, and technology providers in the advent of manufacturing digitalization.

scholarly journals The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review

2019 ◽  
Vol 9 (18) ◽  
pp. 3865 ◽  
Author(s):  
Mehrshad Mehrpouya ◽  
Amir Dehghanghadikolaei ◽  
Behzad Fotovvati ◽  
Alireza Vosooghnia ◽  
Sattar S. Emamian ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Industry 4.0 ◽  
Information Technologies ◽  
Manufacturing Systems ◽  
High Efficiency ◽  
Industrial Applications ◽  
Future Trend ◽  
Smart Manufacturing ◽  
The Future ◽  
Smart Manufacturing Systems

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations.

scholarly journals BDCPS — A Framework for Smart Manufacturing Systems using Blockchain Technology

2019 ◽  
Vol 9 (1S3) ◽  
pp. 366-378
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Real Life ◽  
Cloud Services ◽  
Smart Manufacturing ◽  
Small Scale ◽  
Automotive Manufacturing ◽  
Blockchain Technology ◽  
Decentralized Decision Making ◽  
Smart Manufacturing Systems

Blockchain is going to be the most fundamental technology, and will change the world — going forward. In fact, the revolution has already begun. The birth of Industry 4.0 aka the Fourth Industrial Relution (I4.0), has created a need for autonomous and integrated, secure manufacturing systems. The current smart systems lack the decentralized decision making and real-time communication infrastructure, which is a condition for adaptive, smart manufacturing systems. In this paper, an autonomous, secure and collaborative platform based on Blockchain technology, is presented to adapt to such results. In support with Internet of Things (IoT) and cloud services, a Blockchain Driven Cyber Physical Production System (BDCPS) architecture is designed to communicate with machines, users, devices, suppliers and other peers. Using the Smart Contracts feature and trust-less peer-to-peer decentralized ledger feature, BDCPS will validate the claim with a small-scale real-life Blockchain with IoT system. This implementation case study will be running a private Blockchain on a single board computer, and bridged to a microcontroller containing IoT sensors. The applications of this system in automotive manufacturing industry are presented, to proceed towards Industry 4.0.

Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives

2018 ◽  
Vol 13 (2) ◽  
pp. 137-150 ◽  
Author(s):  
Pai Zheng ◽  
Honghui wang ◽  
Zhiqian Sang ◽  
Ray Y. Zhong ◽  
Yongkui Liu ◽  
...  
Keyword(s):  
Conceptual Framework ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Future Perspectives ◽  
Smart Manufacturing Systems

Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

2020 ◽  
Vol 11 (2) ◽  
pp. 66-92
Author(s):  
Chetna Chauhan ◽  
Amol Singh
Keyword(s):  
Emerging Economies ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Delphi Approach ◽  
Fuzzy Delphi ◽  
Privacy Risks ◽  
Smart Manufacturing Systems ◽  
Hierarchy Process ◽  
Significant Factors

The pace of Industry 4.0 adoption in manufacturing industries has been slow as it is accompanied by several barriers, specifically in the emerging economies. The current study intends to identify and understand the landscape of these challenges. Further, this paper prioritizes the challenges on the basis of their relative importance. To achieve this objective, the authors combine the fuzzy delphi approach along with the fuzzy analytical hierarchy process. Additionally, a sensitivity analysis is done to enhance robustness of the findings. The global rankings of the challenges reveal that the most significant factors that hamper the full realization of smart manufacturing include cybersecurity, privacy risks, and enormously high number of technology choices available in the market. The analysis offers insights into the reasons for the slow diffusion of smart manufacturing systems and the results would assist managers, policymakers, and technology providers in the advent of manufacturing digitalization.

scholarly journals Systematic analysis of needs and requirements for the design of smart manufacturing systems in SMEs☆

2020 ◽  
Vol 7 (2) ◽  
pp. 129-144 ◽  
Author(s):  
Erwin Rauch ◽  
Andrew R Vickery
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Industrial Revolution ◽  
Smart Manufacturing ◽  
Functional Requirements ◽  
Systematic Analysis ◽  
Fourth Industrial Revolution ◽  
Increasing Trend ◽  
Scientific Approach ◽  
Smart Manufacturing Systems

Abstract With the increasing trend of the Fourth Industrial Revolution, also known as Industry 4.0 or smart manufacturing, many companies are now facing the challenge of implementing Industry 4.0 methods and technologies. This is a challenge especially for small and medium-sized enterprises, as they have neither sufficient human nor financial resources to deal with the topic sufficiently. However, since small and medium-sized enterprises form the backbone of the economy, it is particularly important to support these companies in the introduction of Industry 4.0 and to develop appropriate tools. This work is intended to fill this gap and to enhance research on Industry 4.0 for small and medium-sized enterprises by presenting an exploratory study that has been used to systematically analyze and evaluate the needs and translate them into a final list of (functional) requirements and constraints using axiomatic design as scientific approach.

scholarly journals Framework for the Development of Affective and Smart Manufacturing Systems Using Sensorised Surrogate Models

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2274
Author(s):  
María Jesús Ávila-Gutiérrez ◽  
Francisco Aguayo-González ◽  
Juan Ramón Lama-Ruiz
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Human Factor ◽  
Smart Manufacturing ◽  
Physical Systems ◽  
Support Skills ◽  
Key Enabling Technologies ◽  
Smart Manufacturing Systems ◽  
New Framework

Human Factor strategy and management have been affected by the incorporation of Key Enabling Technologies (KETs) of industry 4.0, whereby operator 4.0 has been configured to address the wide variety of cooperative activities and to support skills that operate in VUCA (volatile, uncertain, complex, and ambiguous) environments under the interaction with ubiquitous interfaces on real and virtual hybrid environments of cyber-physical systems. Current human Competences-Capacities that are supported by the technological enablers could result in a radically disempowered human factor. This means that in the processes of optimization and improvement of manufacturing systems from industry 4.0 to industry 5.0, it would be necessary to establish strategies for the empowerment of the human factor, which constitute symbiotic and co-evolutionary socio-technical systems through talent, sustainability, and innovation. This paper establishes a new framework for the design and development of occupational environments 5.0 for the inclusion of singularized operators 4.0, such as individuals with special capacities and talents. A case study for workers and their inclusion in employment is proposed. This model integrates intelligent and inclusive digital solutions in the current workspaces of organizations under digital transformation.

scholarly journals Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0

2021 ◽  
Vol 11 (7) ◽  
pp. 3186
Author(s):  
Radhya Sahal ◽  
Saeed H. Alsamhi ◽  
John G. Breslin ◽  
Kenneth N. Brown ◽  
Muhammad Intizar Ali
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Interaction Mechanism ◽  
Semantic Knowledge ◽  
Smart Manufacturing ◽  
Data Detection ◽  
Workflow Model ◽  
Digital Twins ◽  
Potential Failure ◽  
Operational Data

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.

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