Article alert: Application of Machine Learning to Cold Spray. Congratulations to Prof. ABDERRACHID Hamrani for leading this effort with Aditya Medarametla, Denny John and Arvind Agarwal. #ColdSpray #machinelearning #additivemanufacturing
🎉 Excited to announce the publication of our latest research in Coatings titled "Machine-Learning-Driven Optimization of Cold Spray Process Parameters: Robust Inverse Analysis for Higher Deposition Efficiency"! In this study, we tackled a persistent challenge in cold spray technology: optimizing deposition efficiency (DE). Using a novel two-stage machine learning framework combined with Bayesian optimization, we accurately predicted DE and identified optimal process parameters for efficient material deposition. 🔑 Key Findings: - AI-Driven Efficiency: Our approach highlighted the influence of key factors like gas type, temperature, and pressure on deposition outcomes. - Hydrogen's Potential: We demonstrated that using hydrogen as a carrier gas can lower operational costs while achieving high DE. - Explainable AI (XAI): SHAP analysis provided interpretability, showing gas temperature and type as dominant contributors to DE. - Real-World Validation: The model achieved high accuracy, replicating experimental conditions and offering valuable insights for cost-effective, high-performance cold spray processes. 👉 Read more here: https://lnkd.in/e6ffEYbs Special thanks to: Aditya Medarametla Denny John Arvind Agarwal, head of the Cold Spray and Rapid Deposition (ColRAD) Lab at Florida International University, provided invaluable guidance and expertise for this research. Let’s continue pushing the boundaries of additive manufacturing and material science! 🚀 #CoatingsJournal #MDPI #OpenAccess #ScientificPublication #MachineLearning #AdditiveManufacturing #ColdSpray #MaterialsScience #Innovation #FIU #COLRADLab