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High-throughput transient temperature field prediction and heuristic parameter optimisation toward thermal history uniformity in directed energy deposition
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OA
AI
J
Z
W
王
M
Z
L
DOI：10.1080/17452759.2026.2688024.png)
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Abstract
En 中文
The thermal history nonuniformity during metallic additive manufacturing induces heterogeneity in microstructures and mechanical properties, which remains a critical challenge to the component performance. In this work, a high-throughput parameter optimisation algorithm is developed to allocate laser power on each deposition track so as to reduce thermal history nonuniformity in directed energy deposition (DED). Firstly, a numerical surrogate model is developed to infer the multi-layer transient temperature fields efficiently. In the model, a matrix-wise computational workflow is introduced, which substantially accelerates computational speed and reduces inference time for optimisation evaluations. In addition, a heuristic optimisation algorithm is proposed to allocate laser power parameters on each deposition track, where the standard deviation of cross-track temperature integrals serves as the objective metric and is minimised to achieve a uniform thermal history. In the experiments, the optimised manufacturing strategy suppresses over 57% of the thermal history nonuniformity. The component fabricated with optimised parameters achieves an average improvement of 33% in mechanical property homogeneity and enhanced ductility without sacrificing tensile strength. It highlights the practical value of reducing thermal history nonuniformity to improve mechanical reliability and mitigate the strength-ductility trade-off in DED-fabricated components.
Keywords:
Directed energy deposition
High-throughput surrogate model
Temperature field prediction
Heuristic parameter optimisation
Thermal history uniformity
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