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

Abstract

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.

Assessment of Additive Manufactured IN 625’s Tensile Strength Based on Nonstandard Specimens

“…These primary carbides (M6C) transform further during cooling in the solid state into M23C6-type carbides. This finding is in agreement with other data determined by thermodynamic modelling and STEM-EDS obtained by Maciol et al [76] who investigated the precipitates in L-PBF Inconel 625 subjected to high-temperature annealing.…”

Evaluation of Semi-Automatic Compositional and Microstructural Analysis of Energy Dispersive Spectroscopy (EDS) Maps via a Python-Based Image and Data Processing Framework for Fuel Cell Applications

“…Some researchers have used image analysis on STEM-EDS data to study other complicated material systems, for example, to investigate channel thicknesses for flash memory devices, precipitates in Inconel 625 super alloy, and core and shell sizes for spherical semiconductor nanocrystal systems with minimum human intervention. [53][54][55][56] However, as far as we know, no such automated or semi-automated approach combining both spectral and image processing has been adopted and documented for fuel cell-related research yet, especially not when using high-resolution STEM-EDS. The development and application of an automated approach to analyze large sets of statistically relevant imaging and spectroscopy data would significantly reduce the analysis time and open new opportunities in the systematic study of structure-property correlations.…”

Modeling, Simulation and Data Processing for Additive Manufacturing

“…They formed a theoretical concept for an inter-laboratory study design considering the process chain, including research data management. Macioł et al [13] compared automatically detected precipitates in LPBF of Inconel 625 with a combination of the complementary electron techniques such as the chemical composition performed by EDS. Image processing methods and statistical tools were applied to maximize information gain from data with a low signal-to-noise ratio, keeping human interactions on a minimal level.…”