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Possible applications of artificial intelligence algorithms in F-16 aircraft

Krawczyk Tomasz, Papis Mateusz, Bielawski Radosław, Rządkowski Witold

Zeszyty Naukowe. Transport / Politechnika Śląska

2024

z. 123

101--131

The F-16 aircraft, widely used by the Polish Army Air Force, requires modifications based on Artificial Intelligence (AI) algorithms to enhance its combat capabilities and performance. This study aims to develop comprehensive guidelines for this purpose by first describing F-16 systems and categorizing AI algorithms. Machine learning, deep learning, fuzzy logic, evolutionary algorithms, and swarm intelligence are reviewed for their potential applications in modern aircraft. Subsequently, specific algorithms applicable to F-16 systems are identified, with conclusions drawn on their suitability based on system features. The resultant analysis informs potential F-16 modifications and anticipates future AI applications in military aircraft, facilitating the guidance of new algorithmic developments and offering benefits to similar aircraft types. Moreover, directions for future research and development work are delineated.

Optimisation of femtosecond laser micromachining of copper with AI algorithms

Garasz Katarzyna, Kocik M., Barbucha R., Tański M., Petrov T. S., Mohamed-Seghir M.

Journal of Achievements in Materials and Manufacturing Engineering

2023

Vol. 121, nr 2

267--274

To investigate the application of advanced artificial intelligence (AI) algorithms in optimising femtosecond laser micromachining processes for copper, addressing the critical need for precision and efficiency in micro-scale manufacturing. Design/methodology/approach The objectives are achieved by employing AI algorithms, including machine learning and neural networks, to analyse and optimise laser parameters for copper micromachining systematically. The approach combines theoretical modelling and experimental validation. Findings The research has shown that it is possible to apply artificial intelligence (AI) techniques for modelling and optimising quality characteristics in laser micromachining materials in a femtosecond regime. The results were promising with a challenging material and moderate-sized training set. The best validation performance was approx. 0.094. So far, this study provides basic guidelines for applying AI in laser micromachining of materials and adds to the newest research in this area. The results of the research also provide a deeper knowledge of the ablation mechanisms and will contribute to a coherent theory of ultrashort pulse laser–matter interactions. Research limitations/implications While this research represents a significant advancement in the field, it acknowledges certain limitations, primarily related to the complexity of femtosecond laser-material interactions and secondly related to small amounts of data, not easily scalable to feed the AI algorithms. Practical implications The outcomes of this study provide practical information for industries reliant on micro-scale manufacturing, such as electronics, micro-mechanics and precision instrumentation. Manufacturers can leverage the AI-driven optimization techniques presented here to improve product quality, reduce production costs, and expedite time-to-market. The optimised parameters result in reduced material waste, improved machining precision, and increased productivity. Originality/value The paper contributes to the field by demonstrating the original value of AI algorithms in optimizing femtosecond laser micromachining processes for copper. It introduces a novel approach marries cutting-edge AI techniques with intricate manufacturing processes, offering a unique perspective on the future of precision micro-manufacturing. The value of the research extends to materials scientists, manufacturing engineers, and AI practitioners seeking innovative solutions in materials processing and AI-driven optimisation.

Przegląd metod szybkiego prototypowania algorytmów uczenia maszynowego w FPGA

Smyk Robert, Kowalski Paweł

Poznan University of Technology Academic Journals. Electrical Engineering

2021

No. 106

31--43

W artykule opisano możliwe do wykorzystania otwarte narzędzia wspomagające szybkie prototypowanie algorytmów uczenia maszynowego (ML) i sztucznej inteligencji (AI) przy użyciu współczesnych platform FPGA. Przedstawiono przykład szybkiej ścieżki przy realizacji toru wideo wraz z implementacją przykładowego algorytmu przetwarzania w trybie na żywo.

The paper discusses open tools that can be used to support rapid prototyping of machine learning (ML) and artificial intelligence (AI) algorithms using contemporary FPGA platforms. An example of a fast path in the implementation of a video processing system was presented along with the implementation of an exemplary processing algorithm in live mode.