scholarly journals Industry 4.0: smart test bench for shipbuilding industry

2020 ◽  
Vol 14 (4) ◽  
pp. 1525-1533
Author(s):  
Antonio Giallanza ◽  
Giuseppe Aiello ◽  
Giuseppe Marannano ◽  
Vincenzo Nigrelli
Keyword(s):  
Internet Of Things ◽  
Industry 4.0 ◽  
High Speed ◽  
Test Bench ◽  
Production Systems ◽  
Interactive Approach ◽  
Propulsion Systems ◽  
Shipbuilding Industry ◽  
Support Tool

AbstractIndustry 4.0 promises to increase the efficiency of production plants and the quality of the final product. Consequently, companies that implement advanced solutions in production systems will have a competitive advantage in the future. The principles of Industry 4.0 can also be applied to shipyards to transform them into “smart shipyards” (Shipyard 4.0). The aim of this research is to implement an interactive approach by Internet of Things on a closed power-loop test bench equipped with sophisticated sensors that is specifically designed to test high-power thrusters before they are installed on high-speed crafts, which are used in passenger transport. The preliminary results of the proposed Internet of Things-platform demonstrates the efficacy of the decision-making support tool in improving the design of propulsion systems and increasing their efficiency compared to traditional systems.

scholarly journals Middleware for providing activity-driven assistance in cyber-physical production systems☆

2020 ◽  
Author(s):  
Hitesh Dhiman ◽  
Carsten Röcker
Keyword(s):  
Virtual Reality ◽  
Industry 4.0 ◽  
Production Systems ◽  
Actor Model ◽  
Single Task ◽  
Work Support ◽  
Interactive Computing ◽  
Cyber Physical Production Systems ◽  
Theoretical Foundations

Abstract Assistance is becoming increasingly relevant in carrying out industrial work in the context of cyber-physical production systems (CPPSs) and Industry 4.0. While assistance in a single task via a single interaction modality has been explored previously, crossdevice interaction could improve the quality of assistance, especially given the concurrent and distributed nature of work in CPPSs. In this paper, we present the theoretical foundations and implementation of MiWSICx (Middleware for Work Support in Industrial Contexts), a middleware that showcases how multiple interactive computing devices such as tablets, smartphones, augmented/virtual reality glasses, and wearables could be combined to provide crossdevice industrial assistance. Based on activity theory, MiWSICx models human work as activities combining multiple users, artifacts, and cyber-physical objects. MiWSICx is developed using the actor model for deployment on a variety of hardware alongside a CPPS to provide multiuser, crossdevice, multiactivity assistance.

Development of IoT—enabled data analytics enhance decision support system for lean manufacturing process improvement

2021 ◽  
pp. 1063293X2098791
Author(s):  
Mohd Soufhwee Bin Abd Rahman ◽  
Effendi Mohamad ◽  
Azrul Azwan Bin Abdul Rahman
Keyword(s):  
Decision Support ◽  
Internet Of Things ◽  
Decision Support System ◽  
Process Improvement ◽  
Support System ◽  
Lean Manufacturing ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Production Systems ◽  
Pertinent Data

For over three decades, production firms have extensively espoused lean manufacturing (LM) approach for constantly enhancing their operations. Of late, due to the fusion of physical and digital systems within the Industry 4.0 evolution, production systems can upgrade by applying both notions and lift operational excellence to a new high. This is primarily the reason why digital business transformation has gained significance. Moreover, Industry 4.0 that is led by data assures huge strides in output. The sheer volume of pertinent data from the production systems employing servers, sensors, and cloud computing have made the data exchange procedure more gigantic and intricate. However, conventional systems do not extensively support LM in the context of Industry 4.0. Moreover, the previous studies by researchers in the same field, shown that there was no standard platform to manage the new technologies in LM. This study presents a discussion on the interrelated framework about the way Industry 4.0 has transformed production into an industry focusing on connective mechanisms and platforms which utilize data analytics from the real world. The theoretical framework proposed in this paper integrates LM, data analytics, and Internet of Things (IoT) to enhance decision support systems in process improvement. Data analytics in simulation is employed through Internet of Things to improve bottleneck problems by maintaining the principle of LM. The main information flow route within LM decision support system is demonstrated in detail to show how the decision-making process is done. The decision support mechanism has undergone up-gradation and the suggested framework has shown that the assimilated components could function together to augment the output.

scholarly journals Sustainable Industry 4.0 Wireless Networks, Machine Learning Algorithms, and Internet of Things-based Real-Time Production Logistics in Digital Twin-driven Smart Manufacturing

2021 ◽  
Vol 129 ◽  
pp. 04003
Author(s):  
Elvira Nica ◽  
Gheorghe H. Popescu ◽  
George Lăzăroiu
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Industry 4.0 ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Cyber Physical Production Systems

Research background: The aim of this paper is to synthesize and analyze existing evidence on artificial intelligence-based decision-making algorithms, industrial big data, and Internet of Things sensing networks in digital twin-driven smart manufacturing. Purpose of the article: Using and replicating data from Altair, Catapult, Deloitte, DHL, GAVS, PwC, and ZDNet we performed analyses and made estimates regarding cyber-physical system-based real-time monitoring, product decision-making information systems, and artificial intelligence data-driven Internet of Things systems in digital twin-based cyber-physical production systems. Methods: From the completed surveys, we calculated descriptive statistics of compiled data when appropriate. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. An Internet-based survey software program was utilized for the delivery and collection of responses. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau’s American Community Survey to reflect reliably and accurately the demographic composition of the United States. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Findings & Value added: The way Internet of Things-based decision support systems, artificial intelligence-driven big data analytics, and robotic wireless sensor networks configure digital twin-driven smart manufacturing and cyber-physical production systems in sustainable Industry 4.0.

scholarly journals Data-Driven Manufacturing Simulation: Towards a CPS-Based Approach

2020 ◽  
Author(s):  
Yongkuk Jeong ◽  
Amita Singh ◽  
Masoud Zafarzadeh ◽  
Magnus Wiktorsson ◽  
Jannicke Baalsrud Hauge
Keyword(s):  
Industry 4.0 ◽  
Swot Analysis ◽  
Production Systems ◽  
Traditional Approach ◽  
Data Gathering ◽  
Decision Support Tool ◽  
Future Application ◽  
Support Tool ◽  
Manufacturing Simulation ◽  
Monitoring And Forecasting

Manufacturing simulation has been used as a decision support tool to solve various problems in production systems. However, with the advent of Industry 4.0 and CPS, manufacturing simulation becomes not only a tool for supporting decision-making but also essential for operation, monitoring, and forecasting the production system. In this paper, a traditional approach and a CPS-based approach in manufacturing simulation are compared. In the CPS-based approach, the key processes are divided into 1) data gathering, 2) modeling and simulation, and 3) simulation results analytics and feedback. In addition, a SWOT analysis is conducted to discuss the future application of the manufacturing simulation.

scholarly journals High-Speed Digital Detector for the Internet of Things Assisted by Signal’s Intensity Quantification

2021 ◽  
Vol 9 ◽  
Author(s):  
Yidi Zhang ◽  
Haibo Wang
Keyword(s):  
Internet Of Things ◽  
High Speed ◽  
Digital Signal ◽  
The Internet ◽  
Noisy Environment ◽  
Digital Detector ◽  
Uniform Quantization ◽  
Amplitude Quantization ◽  
The Internet Of Things

This paper proposes a high-speed digital detector for the Internet of Things (IoT) assisted by signal’s intensity quantification. The detector quantifies the amplitude of each pixel of the detected image and converts it into a digital signal, which can be directly applied to the IoT with wireless communication system. Two types of amplitude quantization algorithms, uniform quantization and non-uniform quantization, are applied to the detector, which further improves the quality of the detected image and the robustness of the image signal in a noisy environment. Related simulations have been established to verify the accuracy of the models and algorithms.

scholarly journals An exploratory study on emerging technologies applied to logistics 4.0

2020 ◽  
Vol 27 (3) ◽  
Author(s):  
Jobel Santos Corrêa ◽  
Mauro Sampaio ◽  
Rodrigo de Castro Barros
Keyword(s):  
Cloud Computing ◽  
3D Printing ◽  
Internet Of Things ◽  
Data Quality ◽  
Exploratory Study ◽  
Industry 4.0 ◽  
Online Survey ◽  
Scientific Literature ◽  
Emerging Technologies

Abstract The concept of Logistics 4.0 works closely to that of Industry 4.0. While Industry 4.0 proposes a disruptive change in manufacturing, Logistics 4.0 advocates a transformation in the way organizations buy, manufacture, sell, and deliver products. The objective of this paper is to identify, in Brazilian companies, the degree of interest in the investment in six emerging technologies applicable to logistics, according to scientific literature, as well as to identify the current perception of data quality of these companies. To achieve these objectives, an online survey was conducted. The research showed that the technologies that most interest Brazilian companies are Internet of Things (IoT) and cloud computing, both with 82% of investment intention. The two technologies that least interested companies are crowdsourcing and 3D printing, both with 68% investment disinterest among respondents.

scholarly journals Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 224
Author(s):  
Parkash Tambare ◽  
Chandrashekhar Meshram ◽  
Cheng-Chi Lee ◽  
Rakesh Jagdish Ramteke ◽  
Agbotiname Lucky Imoize
Keyword(s):  
Quality Management ◽  
Internet Of Things ◽  
Performance Measurement ◽  
Industry 4.0 ◽  
Manufacturing Industries ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Manufacturing Companies ◽  
Industrial Standards

The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.

scholarly journals Sustainable Cyber-Physical Production Systems in Big Data-Driven Smart Urban Economy: A Systematic Literature Review

2021 ◽  
Vol 13 (2) ◽  
pp. 751
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Mariana Iatagan ◽  
Iulian Hurloiu ◽  
Irina Dijmărescu
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Literature Review ◽  
Operations Management ◽  
Industry 4.0 ◽  
Production Systems ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Industrial Big Data ◽  
Cyber Physical Production Systems

In this article, we cumulate previous research findings indicating that cyber-physical production systems bring about operations shaping social sustainability performance technologically. We contribute to the literature on sustainable cyber-physical production systems by showing that the technological and operations management features of cyber-physical systems constitute the components of data-driven sustainable smart manufacturing. Throughout September 2020, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “sustainable industrial value creation”, “cyber-physical production systems”, “sustainable smart manufacturing”, “smart economy”, “industrial big data analytics”, “sustainable Internet of Things”, and “sustainable Industry 4.0”. As we inspected research published only in 2019 and 2020, only 323 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 119, generally empirical, sources. Future research should investigate whether Industry 4.0-based manufacturing technologies can ensure the sustainability of big data-driven production systems by use of Internet of Things sensing networks and deep learning-assisted smart process planning.

A Survey on Internet of Things : Applications and Layered Wise Security Issues

2019 ◽  
pp. 171-180
Author(s):  
Uppuluri Sirisha ◽  
G. Lakshme Eswari
Keyword(s):  
Quality Of Life ◽  
Internet Of Things ◽  
Security And Privacy ◽  
Global Network ◽  
Business Growth ◽  
Paper Briefly ◽  
Smart Meters ◽  
Security Issues ◽  
Data Points

This paper briefly introduces Internet of Things(IOT) as a intellectual connectivity among the physical objects or devices which are gaining massive increase in the fields like efficiency, quality of life and business growth. IOT is a global network which is interconnecting around 46 million smart meters in U.S. alone with 1.1 billion data points per day[1]. The total installation base of IOT connecting devices would increase to 75.44 billion globally by 2025 with a increase in growth in business, productivity, government efficiency, lifestyle, etc., This paper familiarizes the serious concern such as effective security and privacy to ensure exact and accurate confidentiality, integrity, authentication access control among the devices.

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