From Intelligent Manufacturing to Smart Manufacturing for Industry 4.0 Driven by Next Generation Artificial Intelligence and Further On

2017 ◽  
Author(s):  
Xifan Yao ◽  
Jiajun Zhou ◽  
Jiangming Zhang ◽  
Claudio R. Boer
Keyword(s):  
Artificial Intelligence ◽  
Industry 4.0 ◽  
Intelligent Manufacturing ◽  
Smart Manufacturing ◽  
Next Generation

scholarly journals Industry 4.0 and Developing Countries: Innovation Perspective of Ethiopia, India and Bangladesh

2020 ◽  
Vol 6 (6) ◽  
pp. 62-70
Author(s):  
Amrut Rao ◽  
Ravindra Pathak ◽  
Ashraf Mahmud Rayed
Keyword(s):  
Developing Countries ◽  
Industry 4.0 ◽  
Global Economy ◽  
Information Technologies ◽  
Technological Development ◽  
Value Added ◽  
Developed Countries ◽  
Intelligent Manufacturing ◽  
Smart Manufacturing ◽  
Efficient Production

Ethiopia, India and Bangladesh are raising economic power, but have not yet integrated very much with the global economy and still have not achieved their potential in context of technology, globalization, and international competitiveness like developed countries. These countries have much strength, but at the same time , are facing many challenges in the increasingly competitive and fast changing global economy. The main key strengths of these courtiers are their large domestic market, young and growing population, a strong private sector with experience in market institutions, and a well developed legal and financial system. In today’s environment of global competition, technological development and innovation; companies, especially manufacturing, are forced to reconfigure their manufacturing and management processes. Industry 4.0 and intelligent manufacturing are part of a transformation, in which manufacturing and information technologies have been integrated to create innovative systems of manufacturing, management and ways of doing business. This system allows optimizing manufacturing, to achieve greater flexibility, efficient production processes and generate a value added proposal for their customers, as well as to provide a timely response to their market needs. The objective of this work is to explore the Industry 4.0, smart manufacturing, environment requirement and relation of innovation in perspective of developing countries.

Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0

2022 ◽  
pp. 406-428
Author(s):  
Lejla Banjanović-Mehmedović ◽  
Fahrudin Mehmedović
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Manufacturing Systems ◽  
System Modeling ◽  
Big Data Analytics ◽  
Intelligent Manufacturing ◽  
Intelligent Manufacturing Systems ◽  
Intelligent Decision Making

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

Artificial Intelligence as an Emerging Technology in Global Trade

2022 ◽  
pp. 98-117
Author(s):  
Seema Garg ◽  
Navita Mahajan ◽  
Jayanta Ghosh
Keyword(s):  
Artificial Intelligence ◽  
International Trade ◽  
Industry 4.0 ◽  
Smart Manufacturing ◽  
Global Trade ◽  
Digital Platforms ◽  
New Horizons ◽  
Demand Prediction ◽  
Immense Potential ◽  
Chain Links

With Industry 4.0 and now 5.0 technologies, the entire globe is embracing these changes. Artificial intelligence-powered systems have immense potential to eliminate international geographical barriers and prove to influence global trade worldwide. The present study highlights how AI increases productivity, economic development, and provides international trade with new horizons. The global value chains, prediction of future trends like changes in consumer demand, risk management, supply chain links are some of the key applications of AI in the sector. AI empowers international trade negotiations to analyze economic trajectories of negotiating partners, adjustments of trade barriers at different rates and scenarios. The chapter will cover the support of AI to access global trade data, its response to diverse challenges, international expansions through digital platforms, support in translations, mechanism of demand prediction, automation of administration with increased efficiency and utility, smart manufacturing, barriers, and influences.

Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0

2020 ◽  
pp. 31-52 ◽  
Author(s):  
Lejla Banjanović-Mehmedović ◽  
Fahrudin Mehmedović
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Manufacturing Systems ◽  
System Modeling ◽  
Big Data Analytics ◽  
Intelligent Manufacturing ◽  
Intelligent Manufacturing Systems ◽  
Intelligent Decision Making

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

scholarly journals Smart manufacturing: Characteristics, technologies and enabling factors

2017 ◽  
Vol 233 (5) ◽  
pp. 1342-1361 ◽  
Author(s):  
Sameer Mittal ◽  
Muztoba Ahmad Khan ◽  
David Romero ◽  
Thorsten Wuest
Keyword(s):  
Industry 4.0 ◽  
Production Systems ◽  
Intelligent Manufacturing ◽  
Smart Manufacturing ◽  
Enabling Factors ◽  
Physical Systems ◽  
Industrial Community ◽  
Body Of Knowledge ◽  
Cyber Physical Production Systems ◽  
Selection Of

The purpose of this article is to collect and structure the various characteristics, technologies and enabling factors available in the current body of knowledge that are associated with smart manufacturing. Eventually, it is expected that this selection of characteristics, technologies and enabling factors will help compare and distinguish other initiatives such as Industry 4.0, cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing, which are frequently used synonymously with smart manufacturing. The result of this article is a comprehensive list of such characteristics, technologies and enabling factors that are regularly associated with smart manufacturing. This article also considers principles of “semantic similarity” to establish the basis for a future smart manufacturing ontology, since it was found that many of the listed items show varying overlaps; therefore, certain characteristics and technologies are merged and/or clustered. This results in a set of five defining characteristics, 11 technologies and three enabling factors that are considered relevant for the smart manufacturing scope. This article then evaluates the derived structure by matching the characteristics and technology clusters of smart manufacturing with the design principles of Industry 4.0 and cyber-physical systems. The authors aim to provide a solid basis to start a broad and interdisciplinary discussion within the research and industrial community about the defining characteristics, technologies and enabling factors of smart manufacturing.

scholarly journals IIoT/IoT and Artificial Intelligence/Machine Learning as a Process Optimization driver under industry 4.0 model

2021 ◽  
Vol 21 (2) ◽  
pp. e15
Author(s):  
Federico Walas ◽  
Andrés Redchuk
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Process Optimization ◽  
Industry 4.0 ◽  
Industrial Process ◽  
Value Added ◽  
Smart Manufacturing ◽  
Industrial Plants ◽  
A Value ◽  
Learning As A Process

The advance of digitalization in industry is making possible that connected products and processes help people, industrial plants and equipment to be more productive and efficient, and the results for operative processes should impact throughout the economy and the environment.Connected products and processes generate data that is being seen as a key source of competitive advantage, and the management and processing of that data is generating new challenges in the industrial environment.The article to be presented looks into the framework of the adoption of Artificial Intelligence and Machine Learning and its integration with IIoT or IoT under industry 4.0, or smart manufacturing framework. This work is focused on the discussion around Artificial Intelligence/Machine Learning and IIoT/IoT as driver for Industrial Process optimization.The paper explore some related articles that were find relevant to start the discussion, and includes a bibliometric analysis of the key topics around Artificial Intelligence/Machine Learning as a value added solution for process optimization under Industry 4.0 or Smart Manufacturing paradigm.The main findings are related to the importance that the subject has acquired since 2013 in terms of published articles, and the complexity of the approach of the issue proposed by this work in the industrial environment.

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.

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