scholarly journals Tecnologías de Mantenimiento industrial en la industria 4.0

2019 ◽  
pp. 20-24
Author(s):  
Manuel Meraz-Méndez ◽  
Claudia Lerma-Hernández
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Teaching Practices ◽  
Industry 4.0 ◽  
Digital Technologies ◽  
Industrial Maintenance ◽  
Technological University ◽  
Engineering Career ◽  
Smart Factories ◽  
Technical Area

Industry 4.0 is the incorporation of digital technologies in factories such as: artificial intelligence, machine learning, 3D printing, drones, robotics, IOT, big data, virtual reality, automation, among others, which aim to digitalize processes productive in the factories, these are also called smart factories. The objective of this article is to identify the technologies applicable to industrial maintenance in Industry 4.0, the final result of this research determine the teaching practices that must be carried out in the Industrial Maintenance Engineering career at the Technological University of Chihuahua, and how the students must be prepared with the competences and skills necessary to face this challenge, at the same time the new teaching practices and strategies that a teacher in the technical area of Industrial Maintenance must apply in laboratory practices with a focus on Industry 4.0.

scholarly journals La industria 4.0 en el mantenimiento industrial

2019 ◽  
pp. 17-24
Author(s):  
Manuel Meraz-Mendez ◽  
Claudia Lerma-Hernández ◽  
Guadalupe Corral-Ramírez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Teaching Practices ◽  
Industry 4.0 ◽  
Digital Technologies ◽  
Industrial Maintenance ◽  
Technological University ◽  
Engineering Career ◽  
Smart Factories ◽  
Technical Area

Industry 4.0 is the incorporation of digital technologies in factories such as: artificial intelligence, machine learning, 3D printing, drones, robotics, IOT, big data, virtual reality, automation, among others, which aim to digitalize processes productive in the factories, these are also called smart factories. The objective of this article is to identify the technologies applicable to industrial maintenance in Industry 4.0, the final result of this research determine the teaching practices that must be carried out in the Industrial Maintenance Engineering career at the Technological University of Chihuahua, and how the students must be prepared with the competences and skills necessary to face this challenge, at the same time the new teaching practices and strategies that a teacher in the technical area of Industrial Maintenance must apply in laboratory practices with a focus on Industry 4.0.

scholarly journals Economic, Social Impacts and Operation of Smart Factories in Industry 4.0 Focusing on Simulation and Artificial Intelligence of Collaborating Robots

2019 ◽  
Vol 8 (5) ◽  
pp. 143 ◽  
Author(s):  
Rabab Benotsmane ◽  
György Kovács ◽  
László Dudás
Keyword(s):  
Artificial Intelligence ◽  
Industry 4.0 ◽  
Optimal Operation ◽  
Added Value ◽  
Smart Factory ◽  
Motion Path ◽  
Robot Arm ◽  
Quantitative Research Methods ◽  
Smart Factories

Smart Factory is a complex system that integrates the main elements of the Industry 4.0 concept (e.g., autonomous robots, Internet of Things, and Big data). In Smart Factories intelligent robots, tools, and smart workpieces communicate and collaborate with each other continuously, which results in self-organizing and self-optimizing production. The significance of Smart Factories is to make production more competitive, efficient, flexible and sustainable. The purpose of the study is not only the introduction of the concept and operation of the Smart Factories, but at the same time to show the application of Simulation and Artificial Intelligence (AI) methods in practice. The significance of the study is that the economic and social operational requirements and impacts of Smart Factories are summarized and the characteristics of the traditional factory and the Smart Factory are compared. The most significant added value of the research is that a real case study is introduced for Simulation of the operation of two collaborating robots applying AI. Quantitative research methods are used, such as numerical and graphical modeling and Simulation, 3D design, furthermore executing Tabu Search in the space of trajectories, but in some aspects the work included fundamental methods, like suggesting an original whip-lashing analog for designing robot trajectories. The conclusion of the case study is that—due to using Simulation and AI methods—the motion path of the robot arm is improved, resulting in more than five percent time-savings, which leads to a significant improvement in productivity. It can be concluded that the establishment of Smart Factories will be essential in the future and the application of Simulation and AI methods for collaborating robots are needed for efficient and optimal operation of production processes.

Potentially Relevant Digital Technologies

2018 ◽  
pp. 21-41
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industrial Revolution ◽  
Learning Experience ◽  
Digital Technologies ◽  
Main Section ◽  
Fourth Industrial Revolution ◽  
Practical Reality ◽  
Self Driving Cars ◽  
Or Applications

In this chapter, Smith and Pourdehnad discuss two digital technologies, Artificial Intelligence (AI) and Crowdsourcing, that are not considered fundamental to current applications of the Fourth Industrial Revolution. These authors believe that these technologies are well worth consideration for some industries or applications as explained in this chapter. AI has become a practical reality in recent years through new tangible innovations such as the advent of self-driving cars, and some very human-like robots, and also as a result of improved machine learning experience. As familiarity with machine learning has grown, so have AI applications. AI is discussed in the first section together with Machine Learning; Crowdsourcing is discussed at length in the second main section.

Digital Technologies for Smart Agriculture

2021 ◽  
pp. 54-67
Author(s):  
Garima Singh ◽  
Gurjit Kaur
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Digital Technologies ◽  
Agricultural Crop ◽  
Seed Selection ◽  
Crop Selection ◽  
Soil Preparation ◽  
Crop Cycle ◽  
Smart Agriculture

This chapter will provide the reader with an introduction to the modern emerging technologies like cloud computing, machine learning, and artificial intelligence used in agriculture. Then a glimpse of complete crop cycle follows, including seven steps, namely crop selection, soil preparation, seed selection, seed sowing, irrigation, crop growth, fertilizing and harvesting; and how these digital technologies are helpful for the crop cycle is also explained in this chapter. The rest of the chapter will explain the merger of the modern digital technologies with the agricultural crop cycle and how the future farming will work.

scholarly journals Advantages and Barriers of Industry 4.0 Concepts Implementation in Small and Medium Industrial Enterprises

2021 ◽  
Vol 93 ◽  
pp. 01007
Author(s):  
Elena Mezentseva
Keyword(s):  
Statistical Analysis ◽  
Industrial Production ◽  
Industry 4.0 ◽  
Digital Technologies ◽  
New Markets ◽  
Bibliographic Analysis ◽  
Industrial Enterprises ◽  
The Subject ◽  
Smart Factories

In the context of Industry 4.0, processes in industrial production are radically reorganized, including digitalization. The subject of the research is the processes of digitalization of small and medium-sized industrial enterprises. The purpose of the study was to identify the current areas of digitalization and the implementation of Industry 4.0 at industrial SMEs. Based on the bibliographic analysis, it was revealed the need for special research for the implementation of Industry 4.0 technologies in SMEs, which will allow to transform them into smart factories. The advantages and limitations for the implementation of Industry 4.0 technologies in manufacturing SMEs are highlighted. On the basis of statistical analysis and analysis of indices, the readiness of Russian SMEs for digitalization was assessed; constraining factors, implemented digital technologies, and prospects for entering new markets were identified.

scholarly journals BIG DATA AS AN ECONOMIC PHENOMENON: THEORETICAL AND METHODOLOGICAL SUMMARY

2021 ◽  
pp. 132-139
Author(s):  
M. Stashevskaya
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Economic Activity ◽  
Large Scale ◽  
Digital Technologies ◽  
Digital Economy ◽  
Descriptive Model ◽  
Back Ground ◽  
Definition Of

The article contains a study of existing views on the economic content of big data. From among the views, within which the authors define big data, the descriptive-model, utility-digital and complex-technological approaches are formulated. Against the back- ground of the large-scale spread of digital technologies (machine learning, cloud computing, artificial intelligence, augmented and virtual reality, etc.), functioning thanks to big data, the study of their economic essence is becoming especially relevant. As a result, it was found that the basis of economic activity in the digital economy is big data. The definition of big data as a resource of the digital economy is proposed.

Industry 4.0

2021 ◽  
pp. 1551-1564
Author(s):  
Immo H. Wernicke
Keyword(s):  
Industry 4.0 ◽  
Swot Analysis ◽  
Digital Technologies ◽  
Industrie 4.0 ◽  
Sustainable Growth ◽  
Business Results ◽  
Challenges And Opportunities ◽  
Traditional Manufacturing ◽  
The Impact ◽  
Smart Factories

The German Government and the European Commission have launched the strategic initiative named Industrie 4.0 for a re-industrialization of Germany and Europe and for achieving more competitiveness and sustainable growth. The strategy promotes and supports R&D and the implementation of digital technologies at SMEs of the traditional manufacturing industries. Digital technologies include Cyber Physical Systems, Cloud Computing, Robotics, 3D-printer-technology, Smart Factories, Additive-Manufacturing, and Artificial Intelligence. The impact of digitization on the economy, on employment, and on business results of SMEs is not yet clear due to insufficient availability of business data. The methodological framework of a SWOT-Analysis might be most convenient to discuss the strength, weakness, challenges, and opportunities of the strategy and the threats on its implementation. The contribution is addressed to politicians, academics, media, startups, and managers of SMEs that are less familiar with the Industrie 4.0 strategy. The concept might be useful to overcome the impact of the corona virus lockdown.

Overview of IoT and Machine Learning for E-Healthcare in Pandemics and Health Crises

2021 ◽  
pp. 16-43
Author(s):  
Mohsin Raza ◽  
Muhammad Awais ◽  
Imran Haider ◽  
Muhammad Usman Hadi ◽  
Ehtasham Javed
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Health Services ◽  
Digital Technologies ◽  
Research Directions ◽  
Smart Healthcare ◽  
Healthcare Needs ◽  
Cross Platform ◽  
Improve Health

The outbreak of COVID-19 has severely affected the healthcare infrastructure. The limitations of conventional healthcare urge the use of the digital technologies to lessen the overall load on the healthcare infrastructure and assist healthcare workers/staff. This chapter focuses on digital technologies to enable smart healthcare solutions to sustain and improve health services. The chapter focuses on two main driving technologies (internet of things [IoT] and artificial intelligence [AI]), pioneering automation and digitalization of healthcare. The enabling technologies possess the potential to transform the healthcare with emergence of new and novel research directions to realize and address healthcare needs. Therefore, it is essential to focus on key driving and complementing technologies to establish multidisciplinary research solutions with cross-platform design coupled with translational learning to unlock the potentials of next generation healthcare.

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