Internet of Things and Additive Manufacturing: Toward Intelligent Production Systems in Industry 4.0

2019 ◽  
pp. 73-89
Author(s):  
A. Suresh ◽  
R. Udendhran ◽  
G. Yamini
Keyword(s):  
Additive Manufacturing ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Production Systems

Novel Additive Manufacturing Processes and Techniques in Industry 4.0

2020 ◽  
pp. 439-455
Author(s):  
Kuldeep Kumar Saxena ◽  
Ankita Awasthi
Keyword(s):  
Additive Manufacturing ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Production Systems ◽  
Manufacturing Processes ◽  
End User ◽  
Mechanical Parts ◽  
Current Trends ◽  
Scientific World ◽  
Materials Used

The chapter contains a comprehensive study to analyse the importance of additive manufacturing in relevance with latest industrial revolution, Industry 4.0. Industry 4.0 is the most promising field with the integration of intelligent production systems with advanced technologies. Additive manufacturing is one of the keys to unlock wonders in scientific world when merging with Industry 4.0. This technology not only provides freedom to customize mechanical parts as per end user requirements, but its ability to construct complex structures and rapid prototyping has given it an edge over other manufacturing processes. Additive manufacturing is a revolutionary technology, which is very impactful in changing industrial scenario where the need of the customer is very dynamic. In this chapter, current trends, methodology, materials used with their advantages and drawbacks are reported. In the end, future benchmarks are also estimated, which becomes important due to the dynamic nature of the industry and its adaptation by the technology to give the relevant solutions.

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.

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 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.

Domestic Engineering - Industry 4.0 Technology Transition Problems

2019 ◽  
pp. 11-20
Author(s):  
E. N. Lapteva ◽  
O. V. Nasarochkina
Keyword(s):  
Additive Manufacturing ◽  
Industry 4.0 ◽  
Technology Development ◽  
Engineering Industry ◽  
Problem Analysis ◽  
Industrial Internet Of Things ◽  
Technology Transition ◽  
Industrial Internet ◽  
Qualified Personnel ◽  
Virtual And Augmented Reality

The paper deals with problem analysis due to domestic engineering transition to the Industry 4.0 technology. It presents such innovative technologies as additive manufacturing (3D-printing), Industrial Internet of Things, total digitization of manufacturing (digital description of products and processes, virtual and augmented reality). Among the main highlighted problems the authors include a lack of unification and standardization at this stage of technology development; incompleteness of both domestic and international regulatory framework; shortage of qualified personnel.

Emerging Industrial Revolution: Symbiosis of Industry 4.0 and Circular Economy: The Role of Universities

2020 ◽  
Vol 25 (3) ◽  
pp. 505-525 ◽  
Author(s):  
Seeram Ramakrishna ◽  
Alfred Ngowi ◽  
Henk De Jager ◽  
Bankole O. Awuzie
Keyword(s):  
Economic Growth ◽  
Circular Economy ◽  
Industry 4.0 ◽  
Business Models ◽  
Industrial Revolution ◽  
New Technologies ◽  
Production Systems ◽  
Fourth Industrial Revolution ◽  
New Jobs

Growing consumerism and population worldwide raises concerns about society’s sustainability aspirations. This has led to calls for concerted efforts to shift from the linear economy to a circular economy (CE), which are gaining momentum globally. CE approaches lead to a zero-waste scenario of economic growth and sustainable development. These approaches are based on semi-scientific and empirical concepts with technologies enabling 3Rs (reduce, reuse, recycle) and 6Rs (reuse, recycle, redesign, remanufacture, reduce, recover). Studies estimate that the transition to a CE would save the world in excess of a trillion dollars annually while creating new jobs, business opportunities and economic growth. The emerging industrial revolution will enhance the symbiotic pursuit of new technologies and CE to transform extant production systems and business models for sustainability. This article examines the trends, availability and readiness of fourth industrial revolution (4IR or industry 4.0) technologies (for example, Internet of Things [IoT], artificial intelligence [AI] and nanotechnology) to support and promote CE transitions within the higher education institutional context. Furthermore, it elucidates the role of universities as living laboratories for experimenting the utility of industry 4.0 technologies in driving the shift towards CE futures. The article concludes that universities should play a pivotal role in engendering CE transitions.

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