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

Area of Science:

• Materials Science
• Metallurgy
• Additive Manufacturing

Background:

• Laser Powder Bed Fusion (L-PBF) is a key additive manufacturing technique for high-performance alloys like Inconel 625.
• Accurate characterization of microstructural precipitates is crucial for understanding material properties.
• Current methods for precipitate analysis can be time-consuming and labor-intensive.

Purpose of the Study:

• To develop and validate an automated algorithm for detecting and identifying precipitates in L-PBF Inconel 625.
• To compare the performance of the automated method against experimental detection and thermodynamic modeling.
• To assess the efficiency and accuracy of automated precipitate analysis using STEM-EDS data.

Main Methods:

• Utilized Scanning Transmission Electron Microscopy with Energy-Dispersive Spectroscopy (STEM-EDS) for data acquisition.
• Applied image processing techniques, including automatic segmentation, to STEM-EDS data treated as multispectral images.
• Employed statistical tools to enhance information extraction from low signal-to-noise ratio data with minimal human intervention.

Main Results:

• Successfully demonstrated the automatic detection and identification of precipitated phases in Inconel 625.
• Validated the compliance of automatically detected precipitates with experimentally determined phases and thermodynamic modeling.
• Achieved significant reduction in processing time while maintaining acceptable quality of results.

Conclusions:

• The proposed algorithm offers an efficient and reliable method for automated precipitate analysis in L-PBF Inconel 625.
• This approach minimizes human interaction, making microstructural characterization more accessible and faster.
• The automated detection of precipitates aids in optimizing material performance for additive manufacturing applications.