Controlled Porosity of Selective Laser Melting-Produced Thermal Pipes: Experimental Analysis and Machine Learning Approach for Pore Recognition on Pipes Surfaces

Malashin Ivan; Martysyuk Dmitry; Tynchenko Vadim; Nelyub Vladimir; Borodulin Aleksei; Gantimurov Andrei; Nisan Anton; Novozhilov Nikolay; Zelentsov Viatcheslav; Filimonov Aleksey; Galinovsky Andrey

Дополнительные ссылки: eLibrary

База данных: Каталог библиотеки СФУ

Библиографическое описание: Controlled Porosity of Selective Laser Melting-Produced Thermal Pipes: Experimental Analysis and Machine Learning Approach for Pore Recognition on Pipes Surfaces : научное издание / I. I. Malashin [et al.]. - Текст : непосредственный // Sensors. - 2024. - Т. 24, № 15. - P. 4959. - ISSN 1424-8220. - ISSN 1424-3210, DOI 10.3390/s24154959.

Аннотация: This study investigates the methods for controlling porosity in thermal pipes manufactured using selective laser melting (SLM) technology. Experiments conducted include water permeability tests and surface roughness measurements, which are complemented by SEM image ML-based analysis for pore recognition. The results elucidate the impact of SLM printing parameters on water permeability. Specifically, an increase in hatch and point distances leads to a linear rise in permeability, while higher laser power diminishes permeability. Using machine learning (ML) techniques, precise pore identification on SEM images depicting surface microstructures of the samples is achieved. The average percentage of the surface area containing detected pores for microstructure samples printed with laser parameters (laser power (W) _ hatch distance (µm) _ point distance (µm)) 175_ 80_80 was found to be 5.2%, while for 225_120_120, it was 4.2%, and for 275_160_160, it was 3.8%. Pore recognition was conducted using the Haar feature-based method, and the optimal patch size was determined to be 36 pixels on monochrome images of microstructures with a magnification of 33×, which were acquired using a Leica S9 D microscope.

Год издания: 2024

Авторы: Malashin Ivan , Martysyuk Dmitry , Tynchenko Vadim , Nelyub Vladimir , Borodulin Aleksei , Gantimurov Andrei , Nisan Anton , Novozhilov Nikolay , Zelentsov Viatcheslav , Filimonov Aleksey , Galinovsky Andrey

Издательство: Molecular Diversity Preservation International

Источник: Sensors

Выпуск: Т. 24, № 15

Номера страниц: 4959

Держатель оригинала документа: Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia, Engineering Center “Forta”, 117036 Moscow, Russia, Scientific Department, Far Eastern Federal University, 690922 Vladivostok, Russia

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