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

Author:

Malashin Ivan1ORCID,Martysyuk Dmitry1ORCID,Tynchenko Vadim1ORCID,Nelyub Vladimir12ORCID,Borodulin Aleksei1ORCID,Gantimurov Andrei1,Nisan Anton3,Novozhilov Nikolay3,Zelentsov Viatcheslav1,Filimonov Aleksey1,Galinovsky Andrey1

Affiliation:

1. Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia

2. Scientific Department, Far Eastern Federal University, 690922 Vladivostok, Russia

3. Engineering Center “Forta”, 117036 Moscow, Russia

Abstract

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.

Publisher

MDPI AG

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