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The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions

arXiv.org Artificial Intelligence

The increasing need for economic, safe, and sustainable smart manufacturing combined with novel technological enablers, has paved the way for Artificial Intelligence (AI) and Big Data in support of smart manufacturing. This implies a substantial integration of AI, Industrial Internet of Things (IIoT), Robotics, Big data, Blockchain, 5G communications, in support of smart manufacturing and the dynamical processes in modern industries. In this paper, we provide a comprehensive overview of different aspects of AI and Big Data in Industry 4.0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective towards deployment of Industry 5.0. In detail, we highlight and analyze how the duo of AI and Big Data is helping in different applications of Industry 4.0. We also highlight key challenges in a successful deployment of AI and Big Data methods in smart industries with a particular emphasis on data-related issues, such as availability, bias, auditing, management, interpretability, communication, and different adversarial attacks and security issues. In a nutshell, we have explored the significance of AI and Big data towards Industry 4.0 applications through panoramic reviews and discussions. We believe, this work will provide a baseline for future research in the domain.


Big Data Analytics for Manufacturing Internet of Things: Opportunities, Challenges and Enabling Technologies

arXiv.org Artificial Intelligence

The recent advances in information and communication technology (ICT) have promoted the evolution of conventional computer-aided manufacturing industry to smart data-driven manufacturing. Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytics in manufacturing data of MIoT. Then, the enabling technologies of big data analytics of manufacturing data are surveyed and discussed. Moreover, this paper also outlines the future directions in this promising area.


Graph Learning for Cognitive Digital Twins in Manufacturing Systems

arXiv.org Artificial Intelligence

Future manufacturing requires complex systems that connect simulation platforms and virtualization with physical data from industrial processes. Digital twins incorporate a physical twin, a digital twin, and the connection between the two. Benefits of using digital twins, especially in manufacturing, are abundant as they can increase efficiency across an entire manufacturing life-cycle. The digital twin concept has become increasingly sophisticated and capable over time, enabled by rises in many technologies. In this paper, we detail the cognitive digital twin as the next stage of advancement of a digital twin that will help realize the vision of Industry 4.0. Cognitive digital twins will allow enterprises to creatively, effectively, and efficiently exploit implicit knowledge drawn from the experience of existing manufacturing systems. They also enable more autonomous decisions and control, while improving the performance across the enterprise (at scale). This paper presents graph learning as one potential pathway towards enabling cognitive functionalities in manufacturing digital twins. A novel approach to realize cognitive digital twins in the product design stage of manufacturing that utilizes graph learning is presented.


Discover the Top 10 Industry 4.0 Trends & Innovations in 2021

#artificialintelligence

The concept of the fourth industrial revolution was first introduced in Hannover earlier in this decade. This followed several decades of industrial automation, albeit at lower levels of functionality and complexity. Many developments have since shaped several industry 4.0 technologies that were previously under the purview of researchers. This is possible today, mainly due to innovations in technology, software, and hardware. Already, the increasing human-machine, machine-machine, and human-human connectivity influence production systems and processes across the world. Industry 4.0 trends and technologies are fundamental in achieving connected manufacturing geared towards smart and autonomous factories. For this in-depth research on the Top Industry 4.0 Trends & Startups, we analyzed a sample of 770 global startups and scaleups.


Top 10 Industry 4.0 Trends & Innovations: 2020 & Beyond

#artificialintelligence

The concept of the fourth industrial revolution was first introduced in Hannover earlier in this decade. This followed several decades of industrial automation, albeit at lower levels of functionality and complexity. Many developments have since shaped several industry 4.0 technologies that were previously under the purview of researchers. This is possible today, mainly due to innovations in technology, software, and hardware. Already, the increasing human-machine, machine-machine, and human-human connectivity influence production systems and processes across the world. Industry 4.0 trends and technologies are fundamental in achieving connected manufacturing geared towards smart and autonomous factories.