Data-driven modeling to predict the load vs. displacement curves of targeted composite materials for industry 4.0 and smart manufacturing

2020 ◽  
pp. 113207
Author(s):  
Monzure-Khoda Kazi ◽  
Fadwa Eljack ◽  
E. Mahdi
Keyword(s):  
Composite Materials ◽  
Industry 4.0 ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Data Driven Modeling

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.

scholarly journals Are college campuses superspreaders? A data-driven modeling study

2021 ◽  
pp. 1-11
Author(s):  
Hannah Lu ◽  
Cortney Weintz ◽  
Joseph Pace ◽  
Dhiraj Indana ◽  
Kevin Linka ◽  
...  
Keyword(s):  
College Campuses ◽  
Data Driven ◽  
Data Driven Modeling ◽  
Modeling Study
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