Electrospinning is a scalable technique for producing fibrous scaffolds with tunable micro- and nanoscale architectures for applications in tissue engineering, drug delivery, and wound care. While machine learning (ML) has been used to support electrospinning process optimisation, most existing approaches predict only mean fibre diameters, neglecting the full diameter distribution that governs scaffold performance. This work presents FibreCastML, an open, distribution-aware ML framework that predicts complete fibre diameter spectra from routinely reported electrospinning parameters and provides interpretable insights into process structure relationships. A meta-dataset comprising 68538 individual fibre diameter measurements extracted from 1778 studies across 16 biomedical polymers was curated. Six standard processing parameters, namely solution concentration, applied voltage, flow rate, tip to collector distance, needle diameter, and collector rotation speed, were used to train seven ML models using nested cross validation with leave one study out external folds. Model interpretability was achieved using variable importance analysis, SHapley Additive exPlanations, correlation matrices, and three dimensional parameter maps. Non linear models consistently outperformed linear baselines, achieving coefficients of determination above 0.91 for several widely used polymers. Solution concentration emerged as the dominant global driver of fibre diameter distributions. Experimental validation across different electrospinning systems demonstrated close agreement between predicted and measured distributions. FibreCastML enables more reproducible and data driven optimisation of electrospun scaffold architectures.

翻译：电纺丝是一种可扩展的技术，可用于生产具有可调控微米级和纳米级结构的纤维支架，广泛应用于组织工程、药物递送和伤口护理领域。尽管机器学习已被用于支持电纺丝工艺优化，但现有方法大多仅预测平均纤维直径，忽略了决定支架性能的完整直径分布。本研究提出了FibreCastML，一个开源的、具备分布感知能力的机器学习框架，能够根据常规报告的电纺丝参数预测完整的纤维直径谱，并提供对工艺-结构关系的可解释性分析。我们构建了一个元数据集，包含从1778项研究中提取的68538个独立纤维直径测量值，涵盖16种生物医用聚合物。采用六项标准工艺参数（溶液浓度、施加电压、流速、针尖到收集器距离、针头直径和收集器转速），通过嵌套交叉验证（外部折叠采用留一研究法）训练了七种机器学习模型。模型可解释性通过变量重要性分析、SHapley加性解释、相关矩阵和三维参数映射实现。非线性模型始终优于线性基线模型，在多种常用聚合物上实现了高于0.91的决定系数。溶液浓度成为影响纤维直径分布的主导全局因素。在不同电纺丝系统中的实验验证表明，预测分布与实测分布高度吻合。FibreCastML为电纺支架结构的优化提供了更具可重复性和数据驱动性的解决方案。