Numerical Prediction of Deformation‐Induced Martensitic Transformation in Cold‐Sprayed Austenitic Stainless Steel

The present work assesses the feasibility of depositing stainless steel powders using the cold spray (CS) deposition process. Specifically, the deposition of austenitic stainless steel powders, such as SS304 grade onto the same substrates, is evaluated for suitability in marine applications, particularly in hydraulic systems. X‐ray diffraction analysis confirmed martensitic transformation in CS SS304 coatings, with the transformed martensite volume fraction of 36.0%, depending on deposition processing parameters. Analytical 1D isentropic gas dynamic modeling, numerical simulations of particle impact, and experimental CS deposition are employed to explore martensitic transformation during deposition. The analytical work revealed that spraying 20 μm 304 stainless steel particles using helium at 473 K and 2.0 MPa with a commercially available nozzle can reduce particle temperature to 217 K, below the martensitic start ( M s ) temperature, facilitating martensitic transformation during deposition. Extending the nozzle's diverging section further enhanced the cooling effect, enabling nearly complete martensitic transformation in the deposited coating. Numerical simulations predicted a ≈40% localized martensite volume fraction at impact velocities of 1,000 m s −1 , with transformation favoring regions of low to moderate deformation (strain: 0.3–1.0). This study provides insights into tailoring microstructures of CS coatings for enhanced mechanical properties through controlled martensitic transformation.

Authors

Institutions

Publication Details

Journal
steel research international
Published
2025-10-26
DOI
https://doi.org/10.1002/srin.202500816
Primary Topic
Metal Alloys Wear and Properties
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Numerical Prediction of Deformation‐Induced Martensitic Transformation in Cold‐Sprayed Austenitic Stainless Steel

J. Lee, Sougata Roy, Cal Vin Wong, Victor K. Champagne et al.
steel research international
Metal Alloys Wear and Properties
article

Numerical Prediction of Deformation‐Induced Martensitic Transformation in Cold‐Sprayed Austenitic Stainless Steel

J. Lee, Sougata Roy, Cal Vin Wong, Victor K. Champagne, K. Ramkumar
article en

Abstract

The present work assesses the feasibility of depositing stainless steel powders using the cold spray (CS) deposition process. Specifically, the deposition of austenitic stainless steel powders, such as SS304 grade onto the same substrates, is evaluated for suitability in marine applications, particularly in hydraulic systems. X‐ray diffraction analysis confirmed martensitic transformation in CS SS304 coatings, with the transformed martensite volume fraction of 36.0%, depending on deposition processing parameters. Analytical 1D isentropic gas dynamic modeling, numerical simulations of particle impact, and experimental CS deposition are employed to explore martensitic transformation during deposition. The analytical work revealed that spraying 20 μm 304 stainless steel particles using helium at 473 K and 2.0 MPa with a commercially available nozzle can reduce particle temperature to 217 K, below the martensitic start ( M s ) temperature, facilitating martensitic transformation during deposition. Extending the nozzle's diverging section further enhanced the cooling effect, enabling nearly complete martensitic transformation in the deposited coating. Numerical simulations predicted a ≈40% localized martensite volume fraction at impact velocities of 1,000 m s −1 , with transformation favoring regions of low to moderate deformation (strain: 0.3–1.0). This study provides insights into tailoring microstructures of CS coatings for enhanced mechanical properties through controlled martensitic transformation.

steel research international
DEVCOM Army Research Laboratory (US), Iowa State University (US), Ames National Laboratory (US)
Iowa State University, South Dakota School of Mines and Technology, Army Research Laboratory
Openalex Percentile: Top 76%
Metal Alloys Wear and Properties
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.