Special Issue "Applications of AI in Mechanical Surface Treatments and Welding Processes: Modelling, Prediction and Optimization"

Dear Colleagues,

The application of artificial intelligence (AI)-based systems has attracted lots of attention for modelling, prediction, and optimization of complex physical phenomena in the last decade. In the AI-based methods, which were basically inspired from natural interconnected systems such those in the human brain, computational nodes have been used to solve the problems by generating functional relations in a parallel way. These methods can be widely employed for modelling of the nonlinear physical phenomena such as mechanical surface treatments or welding. In both mentioned processes, different input process parameters can highly affect the corresponding outputs. Although determination of an efficient function to describe the sole effect of each input parameter might be not so difficult via experiments or analytical approaches, considering whole inputs simultaneously seems to be quite challenging. Herein, application of AI-based alternative approaches can play a key role in unlocking the potentials of comprehensive modelling and other related analyses.

This Special Issue on “Applications of AI in mechanical surface treatments and welding processes: Modelling, prediction and optimization” aims to curate novel advances in the development and applications of computational modelling to address longstanding challenges in surface mechanical treatments and welding processes. Topics include, but are not limited to:

• Development of AI-based methods such as deep learning, artificial neural networks, fuzzy systems, neuro-fuzzy systems, genetic algorithms, ant colony systems, etc., for modelling, prediction, parametric analysis and optimization of surface mechanical treatments and welding processes.
• AI-based modelling and analysis of all mechanical surface treatments based on surface severe plastic deformation of metallic materials such as shot peening, laser shock peening, cavitation peening, constrained groove pressing and ultrasonic nanocrystal surface modifications, etc., considering microstructural characterization, surface and mechanical properties, and fatigue behavior.
• AI-based modelling and analysis of all conventional and modern welding processes used for metallic materials such as arc welding, friction stir welding, spot welding, hot isostatic pressure welding, ultrasonic welding, electron beam welding and laser beam welding, etc., with respect to weld zone microstructural characterization, tensile properties and strengths as well as fatigue behavior.

Dr. Erfan Maleki
Dr. Okan Unal
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• artificial intelligence
• modelling, prediction
• optimization
• mechanical surface treatment
• peening
• welding