Artificial Intelligence as Driving Force for Industry 4.0, Sensors, and Digital Twin

Dear Colleagues,

The main aim of this Special Issue is to present a forum for researchers comprising the entire range of artificial intelligence and machine learning-based applications in Industry 4.0.

The Fourth Industrial Revolution, or Industry 4.0, is focused on the continuous modifications and improvements in the production processes and methods used to create goods, as well as the efficiency of these processes, which is rising quickly with the development of digital twin and machine learning methods. A digital twin is a digital representation of a real-world object, such as a jet engine, wind farm, or even larger objects such as a building or even an entire city. Future industry development techniques and trends are embodied by Industry 4.0, which aims to create more sophisticated and intelligent manufacturing processes. In Industry 4.0, machine learning combines various technologies to allow computer programs and other devices to perceive, understand, respond to, and learn from human actions. IoT allows multiple systems and sensors to communicate, enabling real-time collaboration with other human systems and operators. Using advanced technology, the industrial production system can be made more effective. The manufacturing industry is constantly expanding because of Industry 4.0's technological advancements.

In this Special Issue, we would like to encourage people to contribute their latest developments, ideas and review articles on machine learning applications in Industry 4.0. This Special Issue will focus on the essential ML-based applications in smart manufacturing to optimize digital twins. However, this focus is not limited to the following:

• Sensors of digital twin;
• Digital twin for the renewable energy sector;
• Smart factory;
• Digital twin in healthcare;
• Electric vehicles and machines;
• Quality control;
• Predictive maintenance;
• Machine learning for prediction;
• Blockchain in Industry 4.0;
• Internet of Things for digital twin.

Prof. Dr. Yungcheol Byun
Prof. Dr. Óscar García
Guest Editors

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