Application of Neural Networks in Processing of Metallic Materials
A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Metal Casting, Forming and Heat Treatment".
Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 16334
Special Issue Editor
Interests: artificial intelligence; artificial neural networks; blockchain; structural mechanics; earthquake engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The processing of metallic materials is one of the most demanding and expensive processes, as it requires several intermediate stages (i.e., molten metal or raw material; casting and solidification; hot, warm and/or cold working; shaping of products; heat and surface treatment), and involves high energy consumption. For this reason, it is desirable to optimise the production process (process chain) in terms of energy, mechanical properties, specific product properties, and technological yield. This can be achieved with a better and deeper understanding of the influence of process parameters, the chemical composition, as well as the production equipment used on the final properties of the products. Thus, the properties of the products are related to the technological path of the material in the production process, the chemical composition of the materials used, the production equipment used, etc. Physical phenomena that take place in materials during their production have a very complex and highly nonlinear dependence on the technological parameters that influence the evolution of the microstructure and determine its final properties. The main reason for the complexity is the multiscale nature of metal processing, which makes it difficult to develop reliable and sufficiently accurate physical models for metal material processing simulations that are also computationally undemanding to be potentially used for the on-line control of production. On the other hand, phenomenological models have many other deficiencies. However, recent advances in AI—especially in the field of modern artificial neural networks—allow the development of efficient models that can optimize production (efficient use of energy, lower production costs, improvement of desired mechanical properties, etc.).
To get closer to a greener and sustainable future of metal processing, an efficient approach is necessary. Therefore, the purpose of this Special Issue is to present works dealing with the development of novel approaches—primarily the development and application of artificial neural networks in various metal processing operations (i.e., molten metal processing, raw material processing, preparing of initial material before the deformation process, casting and solidification shaping of product and semi-product, hot and/or cold working, heat and surface treatment, etc.). However, the sharing of research results in metal processing by applying other novel approaches related to artificial intelligence and/or disruptive technologies such as IoT (Internet of Things) and/or blockchain technology is also welcome. The latter can help in controlling work processes and ensuring the immutability of process parameters.
Dr. Iztok Peruš
Guest Editor
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Keywords
- processing of metallic materials
- metallurgy
- microstructure
- neural networks
- artificial intelligence
- process chains
- quality of products
- energy consumption
- sustainable metal production
- blockchain technology
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