2026-05-14 オークリッジ国立研究所（ORNL）
Using the PM-HIP process, the canister was filled with metal powder, vacuum-sealed and subjected to high heat and pressure to form a dense metal component. Credit: Fred List III/ORNL, U.S. Dept. of Energy
＜関連情報＞
- https://www.ornl.gov/news/advanced-manufacturing-enables-pm-hip-large-critical-parts
- https://www.sciencedirect.com/science/article/abs/pii/S0032591026004298
粉末冶金ホットアイソスタティックプレスにおける密度と形状予測のための高速かつ堅牢な計算モデリング手法 A fast and robust computational modeling approach for density and shape predictions in powder metallurgy hot isostatic pressing
Subrato Sarkar, Jason R. Mayeur, K.P.K. Ajjarapu, Fred A. List III, Soumya Nag, Ryan R. Dehoff
Powder Technology Available online: 13 April 2026
DOI:https://doi.org/10.1016/j.powtec.2026.122540
Highlights
- Faster and more stable PM-HIP models presented.
- Thermo-mechanical approximation schemes retained accuracy.
- Adding inertial damping improved numerical stability.
- Slight dip in prediction accuracy observed in approximation schemes.
- Significant improvements achieved in computational performance.
Abstract
Powder metallurgy hot isostatic pressing (PM-HIP) is an advanced manufacturing process that produces near-net-shape parts with high material utilization and uniform microstructures. PM-HIP is frequently used for producing small-scale parts with complicated geometries and is potentially economical for producing large-scale parts. However, excessive post-HIP shape distortions can reduce its effectiveness and economic advantage, especially for larger parts. A PM-HIP computational model can predict and help mitigate these distortions. However, due to complex deformation mechanisms and thermo-mechanical coupling present in PM-HIP processes, these non-linear computational models sometimes become numerically unstable. The numerical instabilities in these models can lead to very slow convergence or no convergence at all, which often translates to slow and unreliable models. These limitations are more pronounced in large models with complicated geometries. Hence, in this work, an alternative modeling approach is presented that improves numerical stability and computational performance. The presented approach achieves these improvements through approximating the fully coupled thermo-mechanical PM-HIP model as a decoupled model and adding inertial damping to the model’s mechanical part. A comparison with the fully coupled model indicated a slight dip in prediction accuracy (<5% error) but significant improvements in numerical stability ( >20 times larger time step size) and computational performance (5-10 times speed-up with less computational resource usage) when using the presented approach.
