Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning
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-i...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-10-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525010184 |
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| author | André Ramalho Anis Assad Benjamin Bevans Fernando Deschamps Telmo G. Santos J.P. Oliveira Prahalada Rao |
| author_facet | André Ramalho Anis Assad Benjamin Bevans Fernando Deschamps Telmo G. Santos J.P. Oliveira Prahalada Rao |
| author_sort | André Ramalho |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-31873e2e7ec149fe87c63feebdd239c7 |
| institution | Kabale University |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-31873e2e7ec149fe87c63feebdd239c72025-08-24T05:11:21ZengElsevierMaterials & Design0264-12752025-10-0125811459810.1016/j.matdes.2025.114598Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learningAndré Ramalho0Anis Assad1Benjamin Bevans2Fernando Deschamps3Telmo G. Santos4J.P. Oliveira5Prahalada Rao6UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica 2829-516, Portugal; Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA; Corresponding author at: UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica 2829-516, Portugal.Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA; University of Southern Denmark, Department of Technology and Innovation, Sønderborg, DenmarkGrado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA; Sooner Advanced Manufacturing Laboratory, University of Oklahoma, Norman, OK, USAPontifícia Universidade Católica do Paraná, Imaculada Conceição 1155, 80215-901 Curitiba, BrazilUNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica 2829-516, Portugal; Laboratório Associado de Sistemas Inteligentes, LASI, 4800-058 Guimarães, PortugalCENIMAT/I3N, Department of Materials Science, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica 2829-516, PortugalGrado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USAThis 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.http://www.sciencedirect.com/science/article/pii/S0264127525010184Wire arc directed energy depositionWire arc additive manufacturing (WAAM)PorosityHumpingMeltpool imagingProcess-aware machine learning |
| spellingShingle | André Ramalho Anis Assad Benjamin Bevans Fernando Deschamps Telmo G. Santos J.P. Oliveira Prahalada Rao Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning Materials & Design Wire arc directed energy deposition Wire arc additive manufacturing (WAAM) Porosity Humping Meltpool imaging Process-aware machine learning |
| title | Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning |
| title_full | Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning |
| title_fullStr | Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning |
| title_full_unstemmed | Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning |
| title_short | Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning |
| title_sort | understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning |
| topic | Wire arc directed energy deposition Wire arc additive manufacturing (WAAM) Porosity Humping Meltpool imaging Process-aware machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525010184 |
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