An Innovative Agile Model of Smart Lean–Green Approach for Sustainability Enhancement in Industry 4.0

2021 ◽  
Vol 7 (4) ◽  
pp. 215
Author(s):  
Varun Tripathi ◽  
Somnath Chattopadhyaya ◽  
Alok K. Mukhopadhyay ◽  
Shubham Sharma ◽  
Jujhar Singh ◽  
...  
Keyword(s):  
Case Studies ◽  
Present Article ◽  
Process Optimization ◽  
Industry 4.0 ◽  
Operational Performance ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Optimization Approach ◽  
Green Approach ◽  
Agile Model

Industry 4.0 emphasizes developing an innovative approach to eliminating the problems caused by environmental and shop floor waste, which is accomplished by a suitable process optimization approach. The process optimization approach is used to maximize productivity within limited constraints by observing end-to-end management systems. The present research work developed an innovative agile model using the lean, smart, and green approach to improve operational performance within limited constraints in Industry 4.0. The proposed model was developed by thoroughly reviewing research articles conducted over the past decades on process optimization approaches that include lean manufacturing, smart manufacturing, kaizen, and lean six sigma. The model was validated through two real production case studies in the mining machinery and automobile industries. The present article concluded that overall operational performance was enhanced in both case studies by improvement in different factors, including working environment, worker efficiency, environmental evolution, logistics management, and resources utilization. The authors of the present article strongly believe that the proposed innovative agile model would help people in industry make aesthetic and smart sustainable production systems in Industry 4.0 within limited constraints.

scholarly journals IIoT/IoT and Artificial Intelligence/Machine Learning as a Process Optimization driver under industry 4.0 model

2021 ◽  
Vol 21 (2) ◽  
pp. e15
Author(s):  
Federico Walas ◽  
Andrés Redchuk
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Process Optimization ◽  
Industry 4.0 ◽  
Industrial Process ◽  
Value Added ◽  
Smart Manufacturing ◽  
Industrial Plants ◽  
A Value ◽  
Learning As A Process

The advance of digitalization in industry is making possible that connected products and processes help people, industrial plants and equipment to be more productive and efficient, and the results for operative processes should impact throughout the economy and the environment.Connected products and processes generate data that is being seen as a key source of competitive advantage, and the management and processing of that data is generating new challenges in the industrial environment.The article to be presented looks into the framework of the adoption of Artificial Intelligence and Machine Learning and its integration with IIoT or IoT under industry 4.0, or smart manufacturing framework. This work is focused on the discussion around Artificial Intelligence/Machine Learning and IIoT/IoT as driver for Industrial Process optimization.The paper explore some related articles that were find relevant to start the discussion, and includes a bibliometric analysis of the key topics around Artificial Intelligence/Machine Learning as a value added solution for process optimization under Industry 4.0 or Smart Manufacturing paradigm.The main findings are related to the importance that the subject has acquired since 2013 in terms of published articles, and the complexity of the approach of the issue proposed by this work in the industrial environment.

scholarly journals Technical Interoperability for Machine Connectivity on the Shop Floor

2018 ◽  
Author(s):  
Magnus Åkerman ◽  
Johan Stahre ◽  
Ulrika Engström ◽  
Ola Angelsmark ◽  
Daniel McGillivray ◽  
...  
Keyword(s):  
Information And Communication Technologies ◽  
Industry 4.0 ◽  
Data Transfer ◽  
Open Systems ◽  
Communication Technologies ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Information And Communication ◽  
Grinding Machine ◽  
Technical Solutions

This paper presents technical solutions to increase Industry 4.0 maturity. Within the “5G-Enabled Manufacturing” project, a 5G network has been deployed at the shop floor to enable fast and scalable connectivity. This network was used to connect a grinding machine to a private cloud to enable visibility and transparency of the production data, which is the basis for Industry 4.0 and smart manufacturing. Results indicate a present lack of commercially available product independent solutions for interconnection and data transfer despite the availability of open standards and well-documented demonstrator projects. The solution is described and discussed regarding technical interoperability, focusing on the system layout, communication standards, and open systems. From the discussion, it is derived that manufacturing end-users are in need to expand and further internalize knowledge on future information and communication technologies to reduce their dependency on equipment and technology providers.

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.

scholarly journals Transitioning of Steel Producers to the Steelworks 4.0 –Literature Review with Case Studies

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4109
Author(s):  
Bożena Gajdzik ◽  
Radosław Wolniak
Keyword(s):  
Literature Review ◽  
Case Studies ◽  
Steel Industry ◽  
Industry 4.0 ◽  
Strong Dependence ◽  
Industrial Robots ◽  
Basic Material ◽  
Sources Of Information ◽  
Important Place ◽  
Steel Mills

The publication presents a picture of modern steelworks that is evolving from steelworks 3.0 to steelworks 4.0. The paper was created on the basis of secondary sources of information (desk research). The entire publication concerns the emerging opportunities for the development of the steel producers to Industry 4.0 and the changes already implemented in the steel plants. The collected information shows the support environment for changes in the steel sector (EU programs), the levels of evolution of steel mills, along with the areas of change in the steel industry and implemented investment projects. The work consists of a theoretical part based on a literature review and a practical part based on case studies. The work ends with a discussion in which the staged and segmented nature of the changes introduced in the analyzed sector is emphasized. Based on the three case studies described in the paper, a comparative analysis was conducted between them. When we tried to compare methods used in the three analyzed steel producers (capital groups): ArcelorMittal, Thyssenkrupp, and Tata Steel Group, it can be seen that in all organizations, the main problem connected with steelworks 4.0 transition is the digitalization of all processes within an organization and in the entire supply chain. This is realized using various tools and methods but they are concentrated on using technologies and methods such as artificial intelligence, drones, virtual reality, full automatization, and industrial robots. The effects are connected to better relations with customers, which leads to an increase in customer satisfaction and the organizations’ profit. The steel industry will undergo further strong changes, bringing it closer to Industry 4.0 because it occupies an important place in the economies of many countries due to the strong dependence of steel producers on the markets of the recipients (steel consumers). Steel is the basic material needed to make many products, and its properties have been valued for centuries. In addition, steel mills with positive economic, social, and environmental aspects are part of the concept of sustainability for industries and economies.

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.

scholarly journals A MODEL OF CREATIVE HERITAGE FOR INDUSTRY: DESIGNING NEW RULES WHILE PRESERVING THE PRESENT SYSTEM OF RULES

2021 ◽  
Vol 1 ◽  
pp. 141-150
Author(s):  
Honorine Harlé ◽  
Pascal Le Masson ◽  
Benoit Weil
Keyword(s):  
Case Studies ◽  
Design Process ◽  
Industry 4.0 ◽  
Innovation Management ◽  
Present System ◽  
Innovative Capability ◽  
Current Change ◽  
A Site ◽  
Do So

AbstractIn industry, there is at once a strong need for innovation and a need to preserve the existing system of production. Thus, although the literature insists on the necessity of the current change toward Industry 4.0, how to implement it remains problematic because the preservation of the factory is at stake. Moreover, the question of the evolution of the system depends on its innovative capability, but it is difficult to understand how a new rule can be designed and implemented in a factory. This tension between preservation and innovation is often explained in the literature as a process of creative destruction. Looking at the problem from another perspective, this article models the factory as a site of creative heritage, enabling creation within tradition, i.e., creating new rules while preserving the system of rules. Two case studies are presented to illustrate the model. The paper shows that design in the factory relies on the ability to validate solutions. To do so, the design process can explore and give new meaning to the existing rules. The role of innovation management is to choose the degree of revision of the rules and to make it possible.

scholarly journals TO SUPPORT IOT COLLABORATIVE EXPRESSIVENESS ON THE SHOP FLOOR

2021 ◽  
Vol 1 ◽  
pp. 3149-3158
Author(s):  
Álvaro Aranda Muñoz ◽  
Yvonne Eriksson ◽  
Yuji Yamamoto ◽  
Ulrika Florin ◽  
Kristian Sandström
Keyword(s):  
Industry 4.0 ◽  
Shop Floor ◽  
Manufacturing Companies ◽  
Technical Aspects ◽  
Industrial Landscape ◽  
The Future ◽  
New Research ◽  
Empirical Material

AbstractThe availability of new research for IoT support and the human-centric perspective of industry 4.0 opens a gap to support operators in unleashing their creativity so they can provide improvements opportunities with IoT technology. This paper presents a case-study carried out in four Swedish manufacturing companies, where four different workshops were facilitated to support operators in the conceptualization of manufacturing improvements with IoT technologies. The empirical material gathered during these workshops has been analyzed in five different reflective sessions and discussed in light of previous research from industry 4.0, operators, and IoT support. Results indicate that operators can collaboratively create conceptual IoT solutions and that expressiveness in communicating their ideas and needs using IoT technology is more relevant than technical aspects and details of their proposed IoT solutions. This technological expressiveness is identified as a necessary skill to be cultivated on the shop floor and can potentially contribute to making a more effective and socially sustainable industrial landscape in the future.

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