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Description
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The optimization of complex manufacturing processes, such as Chemical Vapor Deposition, requires integrated approaches that combine physical modeling with advanced data-driven methodologies. This review synthesizes recent advances in hybrid modeling frameworks that merge equation-based computational fluid dynamics, machine learning, and natural language processing models to enhance process understanding, prediction, optimization and control. In particular, natural language processing techniques are leveraged to generate embedding-based predictors that inform learning tasks. The proposed framework integrates data acquisition, dimensionality reduction, and feature engineering with contextual language processing embeddings, surrogate modeling, and sensitivity analysis. This results in improved forecasting accuracy and interpretability. Key applications include coating thickness prediction, process regime classification, and critical parameter identification using SHAP analysis and Sobol’ indices. Nevertheless, significant challenges remain, including limitations in sensor infrastructure, assessment of dataset sufficiency for specific industrial objectives, and restricted generalizability across reactor designs. This work highlights how hybrid frameworks, together with natural language processing models applied to industrial process datasets, can bridge the gap between data availability in industrial environments and the actionable insights required for practical implementation, while identifying necessary future directions for robust, scalable, and interpretable modeling systems in advanced manufacturing. (2026-08-01)
***This entry has been automatically imported via Infodoc(ASO) CSV by LIST harvest scripts. Please refer to https://doi.org/10.1016/j.ijrmhm.2026.107750 for the original and latest version of the dataset and data downloads*** (2026-06-04)
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Keyword
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Categorical variable embeddings, chemical vapor deposition, Computational Fluid Dynamics (CFD), Data-driven process optimization, Dimensionality reduction, Feature engineering, Hybrid modeling, Industrial process modeling, machine learning, Natural language processing (NLP), Principal Component Analysis (PCA), Process regime classification, Reduced-Order Models (ROMs), Sensitivity analysis, SHAP values, Sobol’ indices, Surrogate modeling |
