AI RESEARCH
Cross-Model Consistency of Feature Importance in Electrospinning: Separating Robust from Model-Dependent Features
arXiv CS.LG
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ArXi:2605.04905v1 Announce Type: new Electrospinning is a highly sensitive fabrication process in which small variations in operating parameters can significantly influence fiber morphology and material performance. Machine learning (ML) methods are increasingly employed to model these process-structure relationships and to identify the relative importance of processing variables. However, most existing studies rely on a single ML model, implicitly assuming that the resulting feature importance is robust and reproducible.