Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning
Year: 2025
Type: article
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.
Source: Materials & Design
Authors: André Ramalho, Anis Assad, Benjamin Bevans, Fernando Deschamps, Telmo G. Santos, J.P. Oliveira, Prahalada Rao
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,650
Funders: National Science Foundation, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Ministério da Ciência, Tecnologia e Ensino Superior, UNIDEMI, Horizon 2020 Framework Programme, European Institute of Innovation and Technology, Fundação para a Ciência e a Tecnologia, Office of Naval Research, Naval Air Systems Command, Naval Surface Warfare Center
Awards: G1379225946, AI Institute: Planning: AI-Enabled Secure and Responsive Smart Manufacturing, Instituto de Nanoestruturas, Nanomodelação e Nanofabricação, CAREER: Smart Additive Manufacturing - Fundamental Research in Sensing, Data Science,and Modeling Toward Zero Part Defects., Desenvolvimento do processo de WAAM e de sistema de monitorização e inspeção com END: aplicação a ligas com memória de forma de base Ferro., MRI: Acquisition of an X-Ray Computed Tomography System at the University of Nebraska-Lincoln for Advancing Multidisciplinary Research and Education in the Great Plains Region, RII Track-4: Understanding the Fundamental Thermal Physics in Metal Additive Manufacturing and its Influence on Part Microstructure and Distortion., CPS: Medium: Collaborative Research: Cyber-Enabled Online Quality Assurance for Scalable Additive Bio-Manufacturing, Instituto de Nanoestruturas, Nanomodelação e Nanofabricação, G4519807699, G4662785699, G4691621526, G5668077596, ECLIPSE: Collaborative Research: Understanding Material Removal in High-Resolution, High-throughput Magnetically Enhanced Laser Induced Plasma Micromachining, PFI-TT: Ultrafast Thermal Simulation of Metal Additive Manufacturing, Instituto de Nanoestruturas, Nanomodelação e Nanofabricação, FMRG: Eco: Manufacturing USA: Multi-Nozzle Metal Jet Additive Manufacturing with Recycled Feedstock Materials
OpenAlex ID: W4413296089