Artificial Intelligence Applications in Material Diagnostics and Technology for Sustainability

Dear Colleagues,

Modern science shows that the complexity of real-world phenomena with many connections between objects is constantly increasing, and there is an effort to cope with it through adequate procedures. One of them is the application of artificial intelligence methods. Artificial intelligence is another breakthrough technology that is changing the face of industry and contributing to sustainable development. This is often associated with the question of what and how to use these methods to solve materials diagnostics and technology support, especially in technical practice.

The development of artificial intelligence methods and specific industrial applications is related to the fact that there is a large amount of available data and knowledge for the training of algorithmic models. The application of artificial intelligence in practice is conditioned by the prerequisites that must be met for the effective development, deployment, and use of such solutions in the context of sustainable development. In addition to the mentioned availability of suitable data and sufficient computing power, it is also a process that supports the implementation of innovative projects.

This Special Issue aims to discuss new applications of artificial intelligence methods in various industrial and research solutions contributing to the sustainable development of processes, focusing on applications in materials diagnostics and other industrial technologies and presenting strengths and weaknesses, but also opportunities and limitations from their application with regard to sustainable development. This Special Edition will provide an opportunity for academia and industry to present innovative solutions for the application of artificial intelligence in the diagnostics of materials and technologies for sustainability.

Dr. Jiří David
Guest Editor

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• artificial neural networks
• expert systems
• knowledge systems
• evolutionary algorithms
• machine vision
• digital image processing
• technical diagnostics
• analytical diagnostics
• materials
• process management
• industrial technologies with sustainable functions
• quality management
• digital twins