scholarly journals Emerging Technologies in Manufacturing

2020 ◽  
Vol 1 (2) ◽  
Keyword(s):  
Artificial Intelligence ◽  
Economic Development ◽  
Internet Of Things ◽  
Manufacturing Industry ◽  
Emerging Technologies ◽  
Smart Manufacturing ◽  
Industrial Internet Of Things ◽  
Profound Impact ◽  
The Core ◽  
Industrial Internet

Manufacturing is the way of transforming resources into products or goods which are required to cater to the needs of the society. It constitutes the foundation of any nation’s economic development. This paper reviews emerging technologies in manufacturing. These technologies include artificial intelligence, smart manufacturing, robotics, automation, 3D printing, nanotechnology, industrial Internet of things, and augmented reality. The use of these technologies will have a profound impact on the manufacturing industry. They have the potential to transform manufacturing as we know it. They should be at the core of any manufacturing upgrading effort.

Industrial internet of things for smart manufacturing applications using hierarchical trustful resource assignment

Work ◽  
2021 ◽  
pp. 1-11
Author(s):  
Duan Pingli ◽  
Bala Anand Muthu ◽  
Seifedine Nimer Kadry
Keyword(s):  
Internet Of Things ◽  
Manufacturing Industry ◽  
Production Systems ◽  
Intelligent Manufacturing ◽  
Smart Manufacturing ◽  
The Internet ◽  
Resource Assignment ◽  
Industrial Internet Of Things ◽  
Assignment Method ◽  
Industrial Internet

BACKGROUND: The manufacturing industry undergoes a new age, with significant changes taking place on several fronts. Companies devoted to digital transformation take their future plants inspired by the Internet of Things (IoT). The IoT is a worldwide network of interrelated physical devices, which is an essential component of the internet, including sensors, actuators, smart apps, computers, mechanical machines, and people. The effective allocation of the computing resources and the carrier is critical in the industrial internet of Things (IIoT) for smart production systems. Indeed, the existing assignment method in the smart production system cannot guarantee that resources meet the inherently complex and volatile requirements of the user are timely. Many research results on resource allocations in auction formats which have been implemented to consider the demand and real-time supply for smart development resources, but safety privacy and trust estimation issues related to these outcomes are not actively discussed. OBJECTIVES: The paper proposes a Hierarchical Trustful Resource Assignment (HTRA) and Trust Computing Algorithm (TCA) based on Vickrey Clarke-Groves (VGCs) in the computer carriers necessary resources to communicate wirelessly among IIoT devices and gateways, and the allocation of CPU resources for processing information at the CPC. RESULTS: Finally, experimental findings demonstrate that when the IIoT equipment and gateways are valid, the utilities of each participant are improved. CONCLUSION: This is an easy and powerful method to guarantee that intelligent manufacturing components genuinely work for their purposes, which want to integrate each element into a system without interactions with each other.

scholarly journals Discussion on the Flexible Manufacturing and Operation Strategy Model for a Machine Tool Component Company in Taiwan

2021 ◽  
Vol 14 (10) ◽  
pp. 1
Author(s):  
Jui-Lung Chen ◽  
Shih-Hsuan Yang
Keyword(s):  
Internet Of Things ◽  
Flexible Manufacturing ◽  
Manufacturing Industry ◽  
Production Management ◽  
Production Model ◽  
Smart Manufacturing ◽  
Labor Costs ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
The Impact

Recently, many manufacturing industries have been facing challenges such as rising material costs, small-volume and large-variety products, shortened production cycles, increased labor costs and longer after-sales service times, which is a very tough challenge for most small and medium-sized component manufacturing suppliers. In addition to the current hot topics in the manufacturing industry - Smart Manufacturing (Industry 4.0) and lean production management, if small and medium-sized enterprises are not able to adjust the pace of manufacturing timely and find a suitable production model, they will soon be overwhelmed by the torrent of the era of speed and accuracy. In the face of the dramatic changes in the industry structure, the company can deploy the global expansion of overseas customers in advance, and adjust to apply and implement a flexible manufacturing model system through the introduction of the Industrial Internet of Things and flexible manufacturing production management. In order to meet the market needs, the manufacturing industry is gradually oriented towards customized production and the rapid development of new products. To meet such stringent requirements, flexible manufacturing becomes one of the necessary ways for enterprises to consider their development models. Therefore, the efficiency and reliability of work can be improved through the Industrial Internet of Things that facilitates machine-to-machine communication, cloud-based big data and learning and imitations of smart robots. This study is an in-depth study of a company that is currently in the process of digital transformation, collecting relevant information and reviewing the analysis to find a suitable smart manufacturing solution for the company and to explore the impact of the COVID-19 pandemic on the strategic development of the company. The findings can provide a significant reference for homotypic companies in the development of their business strategies.

scholarly journals Agile Service Engineering in the Industrial Internet of Things

2018 ◽  
Vol 10 (10) ◽  
pp. 100 ◽  
Author(s):  
Thomas Usländer ◽  
Thomas Batz
Keyword(s):  
Internet Of Things ◽  
Business Models ◽  
Smart Manufacturing ◽  
Interaction Patterns ◽  
Functional Requirements ◽  
Industrial Internet Of Things ◽  
Service Engineering ◽  
Analysis And Design ◽  
Information Models ◽  
Industrial Internet

The emerging Industrial Internet of Things (IIoT) will not only leverage new and potentially disruptive business models but will also change the way software applications will be analyzed and designed. Agility is a need in a systematic service engineering as well as a co-design of requirements and architectural artefacts. Functional and non-functional requirements of IT users (in smart manufacturing mostly from the disciplines of mechanical engineering and electrical engineering) need to be mapped to the capabilities and interaction patterns of emerging IIoT service platforms, not to forget the corresponding information models. The capabilities of such platforms are usually described, structured, and formalized by software architects and software engineers. However, their technical descriptions are far away from the thinking and the thematic terms of end-users. This complicates the transition from requirements analysis to system design, and hence the re-use of existing and the design of future platform capabilities. Current software engineering methodologies do not systematically cover these interlinked and two-sided aspects. The article describes in a comprehensive manner how to close this gap with the help of a service-oriented analysis and design methodology entitled SERVUS (also mentioned in ISO 19119 Annex D) and a corresponding Web-based Platform Engineering Information System (PEIS).

scholarly journals Artificial intelligence in cyber physical systems

2020 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Max Van Kleek ◽  
Omar Santos ◽  
Uchenna Ani
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Literature Review ◽  
Conceptual Framework ◽  
Cyber Physical Systems ◽  
Industrial Internet Of Things ◽  
Physical Systems ◽  
Industrial Internet ◽  
Selection Decisions ◽  
Future Challenges

Abstract This article conducts a literature review of current and future challenges in the use of artificial intelligence (AI) in cyber physical systems. The literature review is focused on identifying a conceptual framework for increasing resilience with AI through automation supporting both, a technical and human level. The methodology applied resembled a literature review and taxonomic analysis of complex internet of things (IoT) interconnected and coupled cyber physical systems. There is an increased attention on propositions on models, infrastructures and frameworks of IoT in both academic and technical papers. These reports and publications frequently represent a juxtaposition of other related systems and technologies (e.g. Industrial Internet of Things, Cyber Physical Systems, Industry 4.0 etc). We review academic and industry papers published between 2010 and 2020. The results determine a new hierarchical cascading conceptual framework for analysing the evolution of AI decision-making in cyber physical systems. We argue that such evolution is inevitable and autonomous because of the increased integration of connected devices (IoT) in cyber physical systems. To support this argument, taxonomic methodology is adapted and applied for transparency and justifications of concepts selection decisions through building summary maps that are applied for designing the hierarchical cascading conceptual framework.

A Survey on Industrial Information Integration 2016–2019

2020 ◽  
Vol 05 (01) ◽  
pp. 33-163 ◽  
Author(s):  
Yong Chen
Keyword(s):  
Internet Of Things ◽  
Information Integration ◽  
Smart Grids ◽  
Web Of Science ◽  
Smart Manufacturing ◽  
Future Research ◽  
Industrial Internet Of Things ◽  
Physical Systems ◽  
Industrial Internet ◽  
Industrial Information Integration

Industrial information integration engineering (IIIE) is a set of foundational concepts and techniques that facilitate the industrial information integration process. In recent years, many applications of the integration between Internet of Things (IoT) and IIIE have become available, including industrial Internet of Things (IIoT), cyber-physical systems, smart grids, and smart manufacturing. In order to investigate the latest achievements of studies on IIIE, this paper reviews literatures from 2016 to 2019 in IEEEXplore and Web of Science. Altogether, 970 papers related to IIIE are grouped into 27 research categories and reviewed. The results present up-to-date development of IIIE and provide directions for future research on IIIE.

scholarly journals Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6241
Author(s):  
Israel Campero-Jurado ◽  
Sergio Márquez-Sánchez ◽  
Juan Quintanar-Gómez ◽  
Sara Rodríguez ◽  
Juan M. Corchado
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Internet Of Things ◽  
Information And Communication Technologies ◽  
Health And Safety ◽  
Working Environment ◽  
Training Dataset ◽  
Support Vector ◽  
Industrial Internet Of Things ◽  
Industrial Internet

Information and communication technologies (ICTs) have contributed to advances in Occupational Health and Safety, improving the security of workers. The use of Personal Protective Equipment (PPE) based on ICTs reduces the risk of accidents in the workplace, thanks to the capacity of the equipment to make decisions on the basis of environmental factors. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) make it possible to generate PPE models feasibly and create devices with more advanced characteristics such as monitoring, sensing the environment and risk detection between others. The working environment is monitored continuously by these models and they notify the employees and their supervisors of any anomalies and threats. This paper presents a smart helmet prototype that monitors the conditions in the workers’ environment and performs a near real-time evaluation of risks. The data collected by sensors is sent to an AI-driven platform for analysis. The training dataset consisted of 11,755 samples and 12 different scenarios. As part of this research, a comparative study of the state-of-the-art models of supervised learning is carried out. Moreover, the use of a Deep Convolutional Neural Network (ConvNet/CNN) is proposed for the detection of possible occupational risks. The data are processed to make them suitable for the CNN and the results are compared against a Static Neural Network (NN), Naive Bayes Classifier (NB) and Support Vector Machine (SVM), where the CNN had an accuracy of 92.05% in cross-validation.

Design of industrial internet of things system based on machine learning and artificial intelligence technology

2020 ◽  
pp. 1-11
Author(s):  
Xu Kun ◽  
Zhiliang Wang ◽  
Ziang Zhou ◽  
Wang Qi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Industrial Equipment ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Test Platform ◽  
Artificial Intelligence Technology

For industrial production, the traditional manual on-site monitoring method is far from meeting production needs, so it is imperative to establish a remote monitoring system for equipment. Based on machine learning algorithms, this paper combines artificial intelligence technology and Internet of Things technology to build an efficient, fast, and accurate industrial equipment monitoring system. Moreover, in view of the characteristics of the diverse types of equipment, scattered layout, and many parameters in the manufacturing equipment as well as the complexity of the high temperature, high pressure, and chemical environment in which the equipment is located, this study designs and implements a remote monitoring and data analysis system for industrial equipment based on the Internet of Things. In addition, based on the application scenarios of the actual aeronautical weather floating platform test platform, this study combines the platform prototype system to design and implement a set of strong real-time communication test platform based on the Windows operating system. The test results show that the industrial Internet of Things system based on machine learning and artificial intelligence technology constructed in this paper has certain practicality.

scholarly journals Industrial Internet of Things (IIoT) of Forensic and Vulnerabilities

2020 ◽  
Vol 8 (5) ◽  
pp. 257-260
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
New Technologies ◽  
Smart Devices ◽  
Digital Information ◽  
Forensic Investigation ◽  
Industrial Internet Of Things ◽  
Future Technology ◽  
Industrial Internet ◽  
Investigation Process

The Industrial Internet-of-Things (IIoT) have changed the present world and future technology-based Industry 4.0, however the understanding of Industrial Internet of things (IIoT) has turned out to be big challenge as far as security concern. The main purpose of adopting and going with new technologies will bring new challenges with cybersecurity and will have more expose uncertain vulnerabilities in terms of AI and BI applications and usage with forensic investigation and accuracy of information sharing between smart devices. This paper composed on the utilization of Artificial Intelligence in securing required evidence for forensic investigation process. The legal methods are different as per region and industry, but the back-frame work and case-based thinking are similar. This framework is made from Intelligence systems such as AI and BI too dependent on the digital information from cloud server. The information from Business Intelligence (BI) and Artificial Intelligence (AI) intersects with data on cloud-based server requires more secure network process and firewall to prevent cyber intruders. This paper has discovered a few gaps on security issues and vulnerabilities where as it will cater proper IIoT based procedure for the Digital Forensic Investigation process.

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