Abstract: Additive manufacturing demonstrates tremendous progress and is expected to play an important role in the creation of construction materials and final products. Contactless (remote) mechanical testing of the materials and 3D printed parts is a critical limitation since the amount of collected data and corresponding structure/strength correlations need to be acquired. In this work, an efficient approach for coupling mechanical tests with thermographic analysis is described. Experiments were performed to find relationships between mechanical and thermographic data. Mechanical tests of 3D-printed samples were carried out on a universal testing machine, and the fixation of thermal changes during testing was performed with a thermal imaging camera. As a proof of concept for the use of machine learning as a method for data analysis, a neural network for fracture prediction was constructed. Analysis of the measured data led to the development of thermographic markers to enhance the thermal properties of the materials. A combination of artificial intelligence with contactless nondestructive thermal analysis opens new opportunities for the remote supervision of materials and constructions.

Cite: Boiko D.A. , Korabelnikova V.A. , Gordeev E.G. , Ananikov V.P.
Integration of thermal imaging and neural networks for mechanical strength analysis and fracture prediction in 3D-printed plastic parts
Scientific Reports. 2022. V.12. N1. 8944 . DOI: 10.1038/s41598-022-12503-y WOS Scopus OpenAlex

Identifiers:

Web of science:	WOS:000800769400005
Scopus:	2-s2.0-85130694095
OpenAlex:	W4281878925

Citing:

DB	Citing
OpenAlex	10
Scopus	9

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