Decoding Residual Stresses in Directed Energy Deposition

Residual stresses (RS) are a critical challenge in metal additive manufacturing (AM). This challenge is especially significant in directed energy deposition (DED), where larger part sizes and melt pools, together with heat accumulation and repeated thermal cycling, promote substantial stress accumulation. Control of RS is further complicated by the many tunable process parameters. Existing reviews either isolate individual parameters or cover RS in AM generally, and rarely address how parameters interact to shape the final stress state. This review addresses that gap with a framework that breaks RS formation into sequential steps: process parameters, intermediate variables, thermal drivers, and RS mechanisms. Using this framework, it analyses how changes in individual parameters propagate through the process to the resulting RS, focusing on experimental studies in DED. It then examines energy density as a unifying metric that captures the combined effect of interacting parameters, collecting published data to correlate calculated energy densities with reported RS levels. Normalised enthalpy is also evaluated as a complementary metric that accounts for intrinsic material properties. Beyond parameter optimisation, the review evaluates the effectiveness of in-situ and post-process mitigation strategies. This work consolidates current knowledge and provides a structured foundation for reducing RS in DED-fabricated parts.

Authors

Institutions

Publication Details

Published
2026-11-08
DOI
https://doi.org/10.31224/8751
Primary Topic
Additive Manufacturing Materials and Processes
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
preprint

Decoding Residual Stresses in Directed Energy Deposition

Bey Vrancken, Zoé Jardon, Dieter De Baere, Sukayna Fakher et al.
4 links
Additive Manufacturing Materials and Processes
preprint

Decoding Residual Stresses in Directed Energy Deposition

Bey Vrancken, Zoé Jardon, Dieter De Baere, Sukayna Fakher, Houda Houban, Antoine Devroe, Roland Loge, Michael Hinderdael
preprint en

Abstract

Residual stresses (RS) are a critical challenge in metal additive manufacturing (AM). This challenge is especially significant in directed energy deposition (DED), where larger part sizes and melt pools, together with heat accumulation and repeated thermal cycling, promote substantial stress accumulation. Control of RS is further complicated by the many tunable process parameters. Existing reviews either isolate individual parameters or cover RS in AM generally, and rarely address how parameters interact to shape the final stress state. This review addresses that gap with a framework that breaks RS formation into sequential steps: process parameters, intermediate variables, thermal drivers, and RS mechanisms. Using this framework, it analyses how changes in individual parameters propagate through the process to the resulting RS, focusing on experimental studies in DED. It then examines energy density as a unifying metric that captures the combined effect of interacting parameters, collecting published data to correlate calculated energy densities with reported RS levels. Normalised enthalpy is also evaluated as a complementary metric that accounts for intrinsic material properties. Beyond parameter optimisation, the review evaluates the effectiveness of in-situ and post-process mitigation strategies. This work consolidates current knowledge and provides a structured foundation for reducing RS in DED-fabricated parts.

Vrije Universiteit Brussel (BE), École Polytechnique Fédérale de Lausanne (CH), KU Leuven (BE)
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.

Laika access coming soon