A Machine Learning Approach for Predicting Particle Spatial, Velocity, and Temperature Distributions in Cold Spray Additive Manufacturing
Masked cold spray additive manufacturing (CSAM) is investigated for fabricating nickel-based electrodes with pyramidal pin-fins that enlarge the active area for the hydrogen-evolution reaction (HER). To bypass the high cost of purely CFD-driven optimization, we construct a two-stage machine learning...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6418 |
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| author | Lurui Wang Mehdi Jadidi Ali Dolatabadi |
| author_facet | Lurui Wang Mehdi Jadidi Ali Dolatabadi |
| author_sort | Lurui Wang |
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| description | Masked cold spray additive manufacturing (CSAM) is investigated for fabricating nickel-based electrodes with pyramidal pin-fins that enlarge the active area for the hydrogen-evolution reaction (HER). To bypass the high cost of purely CFD-driven optimization, we construct a two-stage machine learning (ML) framework trained on 48 high-fidelity CFD simulations. Stage 1 applies sampling and a K-nearest-neighbor kernel-density-estimation algorithm that predicts the spatial distribution of impacting particles and re-allocates weights in regions of under-estimation. Stage 2 combines sampling, interpolation and symbolic regression to extract key features, then uses a weighted random forest model to forecast particle velocity and temperature upon impact. The ML predictions closely match CFD outputs while reducing computation time by orders of magnitude, demonstrating that ML-CFD integration can accelerate CSAM process design. Although developed for a masked setup, the framework generalizes readily to unmasked cold spray configurations. |
| format | Article |
| id | doaj-art-7242085d2f154d808e6593302c2e239f |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7242085d2f154d808e6593302c2e239f2025-08-20T02:24:25ZengMDPI AGApplied Sciences2076-34172025-06-011512641810.3390/app15126418A Machine Learning Approach for Predicting Particle Spatial, Velocity, and Temperature Distributions in Cold Spray Additive ManufacturingLurui Wang0Mehdi Jadidi1Ali Dolatabadi2Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Rd., Toronto, ON M5S 3G8, CanadaDepartment of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Rd., Toronto, ON M5S 3G8, CanadaDepartment of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Rd., Toronto, ON M5S 3G8, CanadaMasked cold spray additive manufacturing (CSAM) is investigated for fabricating nickel-based electrodes with pyramidal pin-fins that enlarge the active area for the hydrogen-evolution reaction (HER). To bypass the high cost of purely CFD-driven optimization, we construct a two-stage machine learning (ML) framework trained on 48 high-fidelity CFD simulations. Stage 1 applies sampling and a K-nearest-neighbor kernel-density-estimation algorithm that predicts the spatial distribution of impacting particles and re-allocates weights in regions of under-estimation. Stage 2 combines sampling, interpolation and symbolic regression to extract key features, then uses a weighted random forest model to forecast particle velocity and temperature upon impact. The ML predictions closely match CFD outputs while reducing computation time by orders of magnitude, demonstrating that ML-CFD integration can accelerate CSAM process design. Although developed for a masked setup, the framework generalizes readily to unmasked cold spray configurations.https://www.mdpi.com/2076-3417/15/12/6418cold sprayadditive manufacturingmachine learningparticlevelocitytemperature |
| spellingShingle | Lurui Wang Mehdi Jadidi Ali Dolatabadi A Machine Learning Approach for Predicting Particle Spatial, Velocity, and Temperature Distributions in Cold Spray Additive Manufacturing Applied Sciences cold spray additive manufacturing machine learning particle velocity temperature |
| title | A Machine Learning Approach for Predicting Particle Spatial, Velocity, and Temperature Distributions in Cold Spray Additive Manufacturing |
| title_full | A Machine Learning Approach for Predicting Particle Spatial, Velocity, and Temperature Distributions in Cold Spray Additive Manufacturing |
| title_fullStr | A Machine Learning Approach for Predicting Particle Spatial, Velocity, and Temperature Distributions in Cold Spray Additive Manufacturing |
| title_full_unstemmed | A Machine Learning Approach for Predicting Particle Spatial, Velocity, and Temperature Distributions in Cold Spray Additive Manufacturing |
| title_short | A Machine Learning Approach for Predicting Particle Spatial, Velocity, and Temperature Distributions in Cold Spray Additive Manufacturing |
| title_sort | machine learning approach for predicting particle spatial velocity and temperature distributions in cold spray additive manufacturing |
| topic | cold spray additive manufacturing machine learning particle velocity temperature |
| url | https://www.mdpi.com/2076-3417/15/12/6418 |
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