Physics-Guided CFD–ML Framework for Sustainable Classical Wire Coating with Power-Law Fluids

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dc.contributor.authorNabudda, Kriengkrai
dc.contributor.authorPoungthong, Pongthep
dc.contributor.authorRitthong, Wirote
dc.contributor.authorElumalai, P. V.
dc.date.accessioned2026-08-06T10:55:57Z
dc.date.available2026-08-06T10:55:57Z
dc.date.issued2026-07-01
dc.description.abstractThis study presents an integrated Computational Fluid Dynamics (CFD) and machine learning framework for analyzing and optimizing classical wire coating processes involving non-Newtonian power-law fluids. A two-dimensional axisymmetric CFD model was developed in ANSYS Fluent 2024R1 to investigate the effects of the power-law index (n = 0.3–1.0) on flow, pressure, temperature, and density fields under non-isothermal conditions. A Latin Hypercube Sampling-based Design of Experiments was coupled with surrogate modelling and Sobol sensitivity analysis to evaluate process performance and identify optimal operating conditions. The results showed that velocity distributions were highly dependent on fluid rheology, with shear-thinning fluids producing broader plug-like flow regions and more uniform velocity profiles. In contrast, pressure, temperature, and density fields exhibited limited sensitivity to variations in the power-law index. Optimization indicated that low power-law indices, moderate pressure gradients, and low-to-moderate wire speeds maximize coating thickness while minimizing material loss. Ridge Polynomial Regression achieved excellent predictive accuracy for all response variables (R<sup>2</sup> > 0.995). Sensitivity analysis revealed that the initial die gap is the dominant factor governing coating thickness, whereas material loss is influenced by combined effects of die geometry, fluid rheology, and wire speed. The proposed framework provides an efficient tool for process optimization and material conservation in industrial wire coating applications.
dc.identifier.citationEng, 7(7), 2026
dc.identifier.doi10.3390/eng7070352
dc.identifier.issn26734117
dc.identifier.other2-s2.0-105045828297
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/18220
dc.sourceEng
dc.subjectComputational Fluid Dynamics
dc.subjectmachine learning
dc.subjectnon-Newtonian fluid
dc.subjectprocess optimization
dc.subjectshear-thinning flow
dc.subjectsurrogate modelling
dc.subjectwire coating
dc.titlePhysics-Guided CFD–ML Framework for Sustainable Classical Wire Coating with Power-Law Fluids
dc.typeArticle

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