scholarly journals Moving from Industry 2.0 to Industry 4.0: A case study from India on leapfrogging in smart manufacturing

2018 ◽  
Vol 21 ◽  
pp. 663-670 ◽  
Author(s):  
Anandi Iyer
Keyword(s):  
Industry 4.0 ◽  
Smart Manufacturing

scholarly journals A Framework for Industry 4.0 Readiness and Maturity of Smart Manufacturing Enterprises: A Case Study

2021 ◽  
Vol 13 (12) ◽  
pp. 6659
Author(s):  
Zeki Murat Çınar ◽  
Qasim Zeeshan ◽  
Orhan Korhan
Keyword(s):  
Industry 4.0 ◽  
Smart Manufacturing ◽  
Maturity Models ◽  
Four Dimensions ◽  
Manufacturing Enterprises ◽  
Maturity Score ◽  
Parts Manufacturing ◽  
Adoption Level ◽  
Insight Into

Recently, researchers have proposed various maturity models (MMs) for assessing Industry 4.0 (I4.0) adoption; however, few have proposed a readiness framework (F/W) integrated with technology forecasting (TF) to evaluate the growth of I4.0 adoption and consequently provide a roadmap for the implementation of I4.0 for smart manufacturing enterprises. The aims of this study were (1) to review the research related to existing I4.0 MMs and F/Ws; (2) to propose a modular MM with four dimensions, five levels, 60 second-level dimensions, and 246 sub-dimensions, and a generic F/W with four layers and seven hierarchy levels; and (3) to conduct a survey-based case study of an automobile parts manufacturing enterprise by applying the MM and F/W to assess the I4.0 adoption level and TF model to anticipate the growth of I4.0. MM and F/W integrated with TF provides insight into the current situation and growth of the enterprise regarding I4.0 adoption, by identifying the gap areas, and provide a foundation for I4.0 integration. Case study findings show that the enterprise’s overall maturity score is 2.73 out of 5.00, and the forecasted year of full integration of I4.0 is between 2031 and 2034 depending upon the policy decisions.

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 The Role of Industry 4.0 and BPMN in the Arise of Condition-Based and Predictive Maintenance: A Case Study in the Automotive Industry

2021 ◽  
Vol 11 (8) ◽  
pp. 3438
Author(s):  
Jorge Fernandes ◽  
João Reis ◽  
Nuno Melão ◽  
Leonor Teixeira ◽  
Marlene Amorim
Keyword(s):  
Business Process ◽  
Automotive Industry ◽  
Industry 4.0 ◽  
Process Model ◽  
Business Process Management ◽  
Process Management ◽  
Predictive Maintenance ◽  
The Evolution Of Industry

This article addresses the evolution of Industry 4.0 (I4.0) in the automotive industry, exploring its contribution to a shift in the maintenance paradigm. To this end, we firstly present the concepts of predictive maintenance (PdM), condition-based maintenance (CBM), and their applications to increase awareness of why and how these concepts are revolutionizing the automotive industry. Then, we introduce the business process management (BPM) and business process model and notation (BPMN) methodologies, as well as their relationship with maintenance. Finally, we present the case study of the Renault Cacia, which is developing and implementing the concepts mentioned above.

scholarly journals Connectivity as a Design Feature for Industry 4.0 Production Equipment: Application for the Development of an In-Line Metrology System

2021 ◽  
Vol 11 (3) ◽  
pp. 1312
Author(s):  
Ana Pamela Castro-Martin ◽  
Horacio Ahuett-Garza ◽  
Darío Guamán-Lozada ◽  
Maria F. Márquez-Alderete ◽  
Pedro D. Urbina Coronado ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Design Feature ◽  
Production Equipment ◽  
Smart Manufacturing ◽  
Process Data ◽  
Industrial Sectors ◽  
Advanced Manufacturing Technologies ◽  
Manufacturing Technologies ◽  
Equipment Application ◽  
The Impact

Industry 4.0 (I4.0) is built upon the capabilities of Internet of Things technologies that facilitate the recollection and processing of data. Originally conceived to improve the performance of manufacturing facilities, the field of application for I4.0 has expanded to reach most industrial sectors. To make the best use of the capabilities of I4.0, machine architectures and design paradigms have had to evolve. This is particularly important as the development of certain advanced manufacturing technologies has been passed from large companies to their subsidiaries and suppliers from around the world. This work discusses how design methodologies, such as those based on functional analysis, can incorporate new functions to enhance the architecture of machines. In particular, the article discusses how connectivity facilitates the development of smart manufacturing capabilities through the incorporation of I4.0 principles and resources that in turn improve the computing capacity available to machine controls and edge devices. These concepts are applied to the development of an in-line metrology station for automotive components. The impact on the design of the machine, particularly on the conception of the control, is analyzed. The resulting machine architecture allows for measurement of critical features of all parts as they are processed at the manufacturing floor, a critical operation in smart factories. Finally, this article discusses how the I4.0 infrastructure can be used to collect and process data to obtain useful information about the process.

Industry 4.0 Impact on Evolution of Product Development: The Bicycle Saddle Case Study

2020 ◽  
Author(s):  
Mohsen Memaran ◽  
Cristiana Delprete ◽  
Eugenio Brusa ◽  
Abbas Razavykia ◽  
Paolo Baldissera
Keyword(s):  
Product Development ◽  
Industry 4.0

scholarly journals Design Framework Based on TEC21 Educational Model and Education 4.0 Implemented in a Capstone Project: A Case Study of an Electric Vehicle Suspension System

2021 ◽  
Vol 13 (11) ◽  
pp. 5768
Author(s):  
Hugo A López ◽  
Pedro Ponce ◽  
Arturo Molina ◽  
María Soledad Ramírez-Montoya ◽  
Edgar Lopez-Caudana
Keyword(s):  
Electric Vehicle ◽  
Industry 4.0 ◽  
Engineering Students ◽  
Traditional Education ◽  
Educational Model ◽  
Capstone Project ◽  
Future Challenges ◽  
Vehicle Suspension System ◽  
Competencies Development

Nowadays, engineering students have to improve specific competencies to tackle the challenges of 21st-century-industry, referred to as Industry 4.0. Hence, this article describes the integration and implementation of Education 4.0 strategies with the new educational model of our university to respond to the needs of Industry 4.0 and society. The TEC21 Educational Model implemented at Tecnologico de Monterrey in Mexico aims to develop disciplinary and transversal competencies for creative and strategic problem-solving of present and future challenges. Education 4.0, as opposed to traditional education, seeks to provide solutions to these challenges through innovative pedagogies supported by emerging technologies. This article presents a case study of a Capstone project developed with undergraduate engineering students. The proposed structure integrates the TEC21 model and Education 4.0 through new strategies and laboratories, all linked to industry. The results of a multidisciplinary project focused on an electric vehicle racing team are presented, composed of Education 4.0 elements and competencies development in leadership, innovation, and entrepreneurship. The project was a collaboration between academia and the productive sector. The results verified the students’ success in acquiring the necessary competencies and skills to become technological leaders in today’s modern industry. One of the main contributions shown is a suitable education framework for bringing together the characteristics established by Education 4.0 and achieved by our educational experience based on Education 4.0.

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.

Sign in / Sign up

Export Citation Format

Share Document