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 Middleware Application, Suitable to Build an Automated and Connected Smart Manufacturing Environment

2021 ◽  
Author(s):  
Muzaffar Rao ◽  
Thomas Newe
Keyword(s):  
Mobile Robots ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Manufacturing Execution System ◽  
Manufacturing Environment ◽  
Industrial Internet ◽  
A Cell ◽  
Smart Industry ◽  
Manufacturing Execution

The current manufacturing transformation is represented by using different terms like; Industry 4.0, smart manufacturing, Industrial Internet of Things (IIoTs), and the Model-Based enterprise. This transformation involves integrated and collaborative manufacturing systems. These manufacturing systems should meet the demands changing in real-time in the smart factory environment. Here, this manufacturing transformation is represented by the term ‘Smart Manufacturing’. Smart manufacturing can optimize the manufacturing process using different technologies like IoT, Analytics, Manufacturing Intelligence, Cloud, Supplier Platforms, and Manufacturing Execution System (MES). In the cell-based manufacturing environment of the smart industry, the best way to transfer the goods between cells is through automation (mobile robots). That is why automation is the core of the smart industry i.e. industry 4.0. In a smart industrial environment, mobile-robots can safely operate with repeatability; also can take decisions based on detailed production sequences defined by Manufacturing Execution System (MES). This work focuses on the development of a middleware application using LabVIEW for mobile-robots, in a cell-based manufacturing environment. This application works as middleware to connect mobile robots with the MES system.

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.

The Application of the Positive Semi-Definite Kernel Space for SVM in Quality Prediction

2020 ◽  
Vol 13 (2) ◽  
pp. 228-233
Author(s):  
Wang Meng ◽  
Dui Hongyan ◽  
Zhou Shiyuan ◽  
Dong Zhankui ◽  
Wu Zige
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Industrial Revolution ◽  
Feature Space ◽  
Mixed Data ◽  
Smart Manufacturing ◽  
Additive Constant ◽  
Kernel Space ◽  
Data Set ◽  
Low Dimensional

Background: A transformation toward 4th Generation Industrial Revolution (Industry 4.0) is being led by Germany based on Cyber-Physical System-enabled manufacturing and service innovation. Smart manufacturing is an important feature of Industry 4.0 which uses the networked manufacturing systems for smart production. Current manufacturing systems (5M1E systems) require deeper mining of the data which is generated from manufacturing process. Objective: To map low-dimensional embedding into the input space would meet the requirement of “kernel trick” to solve a problem in feature space. On the other hand, the distance can be calculated more precisely. Methods: In this research, we proposed a positive semi-definite kernel space by using a constant additive method based on a kernel view of ISOMAP. There were 6 steps in the algorithm. Results: The classification precision of KMLSVM was better than SVM in the enterprise data set, in which SVM selected the RBF kernel and optimized its parameters. Conclusion: We adopted the additive constant method in kernel space construction and the positive semi-definite kernel was built. The typical mixed data set of an enterprise was used in simulation. We compared the SVM and KMLSVM in this data set and optimized the SVM kernel function parameters. The simulation results demonstrated the KMLSVM was a better algorithm in mix type data set than SVM.

scholarly journals Algorithm for Designing Reconfigurable Equipment to Enable Industry 4.0 and Circular Economy-Driven Manufacturing Systems

2021 ◽  
Vol 11 (10) ◽  
pp. 4446
Author(s):  
Emilia Brad ◽  
Stelian Brad
Keyword(s):  
Circular Economy ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Optimal Solution ◽  
Smart Manufacturing ◽  
Manufacturing Enterprises ◽  
Design Engineers ◽  
Definition Of ◽  
High Level ◽  
Reconfigurable Machine Tool

In the paradigm of industry 4.0, manufacturing enterprises need a high level of agility to adapt fast and with low costs to small batches of diversified products. They also need to reduce the environmental impact and adopt the paradigm of the circular economy. In the configuration space defined by this duality, manufacturing systems must embed a high level of reconfigurability at the level of their equipment. Finding the most appropriate concept of each reconfigurable equipment that composes an eco-smart manufacturing system is challenging because every system is unique in the context of an enterprise’s business model and technological focus. To reduce the entropy and to minimize the loss function in the design process of reconfigurable equipment, an evolutionary algorithm is proposed in this paper. It combines the particle swarm optimization (PSO) method with the theory of inventive problem-solving (TRIZ) to systematically guide the creative potential of design engineers towards the definition of the optimal concept over equipment’s lifecycle: what and when you need, no more, no less. The algorithm reduces the number of iterations in designing the optimal solution. An example for configuration design of a reconfigurable machine tool with adjustable functionality is included to demonstrate the effectiveness of the proposed algorithm.

Digital twins-based smart manufacturing system design in Industry 4.0: A review

2021 ◽  
Vol 60 ◽  
pp. 119-137
Author(s):  
Jiewu Leng ◽  
Dewen Wang ◽  
Weiming Shen ◽  
Xinyu Li ◽  
Qiang Liu ◽  
...  
Keyword(s):  
System Design ◽  
Industry 4.0 ◽  
Manufacturing System ◽  
Smart Manufacturing ◽  
Digital Twins ◽  
Manufacturing System Design
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