Characterization of γ′ Precipitation Kinetics in Additively Manufactured Ni-Based Superalloy ABD®-900AM

Abstract The γ ′ precipitation behavior in additively manufactured ABD ® -900AM is quantified in the temperature range of 700 K to 1200 K (427 °C to 927 °C). Data for γ ′ precipitate size distributions and median radii are reported as a function of annealing time and temperature. An empirical model predicting the median γ ′ precipitate size during post-build heat treatment is established, and effective diffusion coefficients are determined at the three studied temperatures. A strong correlation between median γ ′ precipitate size and bulk hardness is identified, revealing an optimum precipitate size of 10 to 20 nm for maximum hardness and enabling classification of the precipitation process into two stages: γ → γ + γ ′ transformation and subsequent coarsening. A time–temperature–transformation (TTT) diagram is constructed and further rationalized using CALPHAD and TC-PRISMA modeling. Notably, interconnected γ ′ morphologies are observed at early reaction times ( r < 6 nm), indicating the possible involvement of spinodal decomposition in the precipitation mechanism. The work provides comprehensive datasets for thermal stability, mechanical response, and phase evolution in ABD ® -900AM and offers insights for post-processing strategies and predictive modeling in AM Ni-based superalloys.

Authors

Institutions

Publication Details

Journal
Metallurgical and Materials Transactions A
Published
2026-01-09
DOI
https://doi.org/10.1007/s11661-025-08081-z
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Characterization of γ′ Precipitation Kinetics in Additively Manufactured Ni-Based Superalloy ABD®-900AM

Yuanbo T. Tang, Roger C. Reed, Yuhan Zhuge, Sergio Lozano-Perez et al.
Metallurgical and Materials Transactions A
Additive Manufacturing Materials and Processes
article

Characterization of γ′ Precipitation Kinetics in Additively Manufactured Ni-Based Superalloy ABD®-900AM

Yuanbo T. Tang, Roger C. Reed, Yuhan Zhuge, Sergio Lozano-Perez, D. G. McCartney
article en

Abstract

Abstract The γ ′ precipitation behavior in additively manufactured ABD ® -900AM is quantified in the temperature range of 700 K to 1200 K (427 °C to 927 °C). Data for γ ′ precipitate size distributions and median radii are reported as a function of annealing time and temperature. An empirical model predicting the median γ ′ precipitate size during post-build heat treatment is established, and effective diffusion coefficients are determined at the three studied temperatures. A strong correlation between median γ ′ precipitate size and bulk hardness is identified, revealing an optimum precipitate size of 10 to 20 nm for maximum hardness and enabling classification of the precipitation process into two stages: γ → γ + γ ′ transformation and subsequent coarsening. A time–temperature–transformation (TTT) diagram is constructed and further rationalized using CALPHAD and TC-PRISMA modeling. Notably, interconnected γ ′ morphologies are observed at early reaction times ( r < 6 nm), indicating the possible involvement of spinodal decomposition in the precipitation mechanism. The work provides comprehensive datasets for thermal stability, mechanical response, and phase evolution in ABD ® -900AM and offers insights for post-processing strategies and predictive modeling in AM Ni-based superalloys.

Metallurgical and Materials Transactions A
University of Oxford (GB), University of Birmingham (GB)
Openalex Percentile: Top 92%
Additive Manufacturing Materials and Processes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.