2025-10-03 バージニア工科大学 (VirginiaTech)
Web要約 の発言：
＜関連情報＞
- https://news.vt.edu/articles/2025/09/eng-ise-meltedmetal.html
- https://www.sciencedirect.com/science/article/pii/S0264127525010184
メルトプールイメージングと機械学習を用いたワイヤアーク指向性エネルギー堆積積層造形におけるプロセス不安定性の理解と検出 Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning
André Ramalho, Anis Assad, Benjamin Bevans, Fernando Deschamps, Telmo G. Santos, J.P. Oliveira, Prahalada Rao
Materials & Design Available online: 17 August 2025
DOI:https://doi.org/10.1016/j.matdes.2025.114598
Graphical abstract
Highlights
- Using in-operando high-speed optical imaging to understand the transient and stochastic meltpool dynamics of instabilities in WA-DED.
- Meltpool flow velocity are estimated and correlated to instabilities.
- Physically intuitive meltpool shape features are used for rapid and autonomous detection of process instabilities.
- Process-aware machine learning detects process instability with statistical accuracy ∼84 % compared to less-than 70 % for black-box deep learning models.
Abstract
This work concerns the wire arc directed energy deposition (WA-DED) additive manufacturing process. The objectives were two-fold: (1) observe and understand, through in-operando high-speed meltpool imaging, the causal dynamics of two common WA-DED process instabilities, namely, humping and humping-induced porosity; and (2) leverage the high-speed meltpool imaging data within machine learning algorithms for real-time detection of process instabilities. Humping and humping-induced porosity are leading stochastic causes of poor WA-DED part quality that occur despite extensive optimization of processing conditions. It is therefore essential to understand, detect and control the causal meltpool phenomena linked to these instabilities. Accordingly, we used a high-speed camera to capture the meltpool dynamics of multi-layer depositions of ER90S-G steel parts and meltpool flow behavior related to process instabilities were demarcated and quantified. Next, physically intuitive meltpool morphology signatures were extracted from the imaging data. These signatures were used in a machine learning model trained to autonomously detect process instabilities. This novel process-aware machine learning approach classified onset of instabilities with ∼85 % accuracy (F1-score), outperforming black-box deep learning models (F1-score <66 %). These results pave the way for a physically intuitive process-aware machine learning strategy for monitoring and control of the WA-DED process.
