Towards Automatic Detection of Precipitates in Inconel 625 Superalloy Additively Manufactured by the L-PBF Method

• Published in: Materials Vol. 14; no. 16; p. 4507
• Main Authors: Macioł, Piotr, Falkus, Jan, Indyka, Paulina, Dubiel, Beata
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.08.2021; MDPI
• Subjects: Additive manufacturing; Algorithms; automatic image analysis; Chemical composition; Chemical elements; Chemical precipitation; EDS microanalysis; High temperature; Image processing; Image segmentation; inconel 625; Methods; Microscopy; Nickel base alloys; Noise; Precipitates; Principal components analysis; Signal to noise ratio; Statistical methods; Superalloys; Thermodynamic models; inconel 625; EDS microanalysis; additive manufacturing; automatic image analysis
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma14164507

In our study, the comparison of the automatically detected precipitates in L-PBF Inconel 625, with experimentally detected phases and with the results of the thermodynamic modeling was used to test their compliance. The combination of the complementary electron microscopy techniques with the microanalysis of chemical composition allowed us to examine the structure and chemical composition of related features. The possibility of automatic detection and identification of precipitated phases based on the STEM-EDS data was presented and discussed. The automatic segmentation of images and identifying of distinguishing regions are based on the processing of STEM-EDS data as multispectral images. Image processing methods and statistical tools are applied to maximize an information gain from data with low signal-to-noise ratio, keeping human interactions on a minimal level. The proposed algorithm allowed for automatic detection of precipitates and identification of interesting regions in the Inconel 625, while significantly reducing the processing time with acceptable quality of results.