Sustainable development in cold gas dynamic spray coating process for biomedical applications: challenges and future perspective review

Sandhu, Harpinder Singh; Goyal, Deepam; Sharma, Ankit; Goyal, Tarun; Jarial, Sapna; Sharda, Anuranjan

doi:10.1007/s12008-023-01474-7

Int J Interact Des Manuf

2023

DOI: 10.1007/s12008-023-01474-7

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Sustainable development in cold gas dynamic spray coating process for biomedical applications: challenges and future perspective review

Harpinder Singh Sandhu,

Deepam Goyal,

Ankit Sharma

et al.

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2024

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Machine-Learning-Driven Optimization of Cold Spray Process Parameters: Robust Inverse Analysis for Higher Deposition Efficiency

Hamrani,

Medarametla,

John

et al. 2024

Coatings

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Cold spray technology has become essential for industries requiring efficient material deposition, yet achieving optimal deposition efficiency (DE) presents challenges due to complex interactions among process parameters. This study developed a two-stage machine learning (ML) framework incorporating Bayesian optimization to address these challenges. In the first stage, a classification model predicted the occurrence of deposition, while the second stage used a regression model to forecast DE values given deposition presence. The approach was validated on Aluminum 6061 data, demonstrating its capability to accurately predict DE and identify optimal process parameters for target efficiencies. Model interpretability was enhanced with SHAP analysis, which identified gas temperature and gas type as primary factors affecting DE. Scenario-based inverse analysis further validated the framework by comparing model-predicted parameters to literature data, revealing high accuracy in replicating real-world conditions. Notably, substituting hydrogen as the gas carrier reduced the required gas temperature and pressure for high DE values, suggesting economic and operational benefits over helium and nitrogen. This study demonstrates the effectiveness of AI-driven solutions in optimizing cold spray processes, contributing to more efficient and practical approaches in material deposition.

show abstract

Machine-Learning-Driven Optimization of Cold Spray Process Parameters: Robust Inverse Analysis for Higher Deposition Efficiency

Hamrani,

Medarametla,

John

et al. 2024

Coatings

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Sustainable development in cold gas dynamic spray coating process for biomedical applications: challenges and future perspective review

Cited by 1 publication

References 116 publications

Machine-Learning-Driven Optimization of Cold Spray Process Parameters: Robust Inverse Analysis for Higher Deposition Efficiency

Machine-Learning-Driven Optimization of Cold Spray Process Parameters: Robust Inverse Analysis for Higher Deposition Efficiency

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