DOI: 10.18503/1995-2732-2024-22-3-188-196
Abstract
In an effort to reduce production costs, modern industry has increasingly turned to additive methods for producing blanks. Although benefits of additive metal production are obvious, the characteristics of these techniques have not been sufficiently studied. The paper describes the methods of 3D printing with metals, such as selective laser melting (SLM) and wire arc additive manufacturing (WAAM), showing several disadvantages. These include the possibility of forming increased porosity in blanks, as well as high residual stresses in the material and structural heterogeneity in the deposited metal. The research was aimed at assessing porosity and structural defects in the deposited metal, and developing a method to determine the degree of risk of material defects using neural network modeling. During the research, samples were produced from austenitic steels 12Kh18N10T using SLM and 08Kh18N10 using WAAM. Metallographic studies were conducted with an Altami MET 1C optical microscope with magnifications of ×50, 100 and 200. In particular, the level of contamination of the deposited metal with non-metallic inclusions was evaluated in accordance with GOST 1778. To study the nature of the porosity formation, the authors conducted a fractographic analysis of fracture in tensile samples. Tensile tests were performed to study the effect of defects in deposited metal on strength characteristics. Using specialized software developed in Matlab, a quantitative and fractal analysis of microstructure was performed to assess the risk entailed by various defects. A neural network model was also trained using Matlab. The study showed that the formation of porosity in the samples produced by SLM was greatly influenced by the quality of the feedstock material. The samples produced by WAAM have lower porosity and contamination with non-metallic inclusions compared with the samples produced by laser melting. The paper contains a linear relationship established between the fractal dimension of the microstructure and the area of pores and zones of faulty fusion in the analyzed digital image of the microstructure proved by the tensile tests.
Keywords
additive manufacturing, SLM, WAAM, porosity, defects of deposited metal, neural network modeling, fractal analysis
For citation
Kabaldin Yu.G., Anosov M.S., Chernigin M.A., Mordovina Yu.S. Neural Network Modeling Used to Assess the Effect of Metal Defects of Blanks Produced by Additive Methods on Strength Properties. Vestnik Magnitogorskogo Gosudarstvennogo Tekhnicheskogo Universiteta im. G.I. Nosova [Vestnik of Nosov Magnitogorsk State Technical University]. 2024, vol. 22, no. 3, pp. 188-196. https://doi.org/10.18503/1995-2732-2024-22-3-188-196
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