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. In this study, the consistency of feature importance across multiple ML model families was systematically evaluated using a curated dataset of 96 polyvinyl alcohol (PVA) electrospinning experiments. Twenty-one ML models representing linear, tree-based, kernel-based, neural network, and instance-based approaches were trained and compared. To provide a unified interpretability framework, SHAP (SHapley Additive exPlanations) values were used to calculate feature importance consistently across all models. A rank-based statistical analysis was then performed to quantify inter-model agreement and assess the robustness of parameter rankings. The results demonstrate that predictive performance and interpretive reliability are fundamentally distinct properties. Although several models achieved comparable predictive accuracy, substantial differences were observed in their feature importance rankings. Solution concentration emerged as the most robust and consistently influential parameter (variability = 0), whereas flow rate and applied voltage exhibited high ranking variability (variability > 0.9), indicating strong model dependence. These findings suggest that feature importance derived from a single ML model may be unreliable, particularly for small experimental datasets, and highlight the importance of cross-model validation for achieving trustworthy interpretation in ML-assisted electrospinning research.

翻译：静电纺丝是一种高度敏感的制造工艺，其中操作参数的微小变化会显著影响纤维形态和材料性能。机器学习方法越来越多地被用于建模这些过程-结构关系，并识别加工变量的相对重要性。然而，现有研究大多依赖于单一机器学习模型，隐含地假设所得特征重要性具有稳健性和可重复性。本研究利用包含96个聚乙烯醇静电纺丝实验的精选数据集，系统评估了多个机器学习模型家族中特征重要性的一致性。训练并比较了21个机器学习模型，涵盖线性、基于树、基于核、神经网络和基于实例的方法。为了提供统一的解释框架，采用SHAP值在所有模型中一致地计算特征重要性。随后进行基于秩的统计分析，以量化模型间的一致性并评估参数排序的稳健性。结果表明，预测性能和解释可靠性是根本不同的属性。尽管多个模型达到了相当的预测精度，但其特征重要性排序存在显著差异。溶液浓度成为最稳健且持续重要的参数（变异性=0），而流速和施加电压表现出较高的排序变异性（变异性>0.9），表明其强模型依赖性。这些发现表明，从单一机器学习模型导出的特征重要性可能不可靠，特别是在实验数据集较小的情况下，并强调了跨模型验证对于在机器学习辅助静电纺丝研究中实现可信解释的重要性。