FibreCastML: An Open Web Platform for Predicting Electrospun Nanofibre Diameter Distributions

2026-01-08

arXiv.org

AI Summary

This study addresses a critical limitation in current electrospinning modeling approaches, which predict only the average fiber diameter while neglecting the full diameter distribution that profoundly influences scaffold performance. To overcome this, we propose the first distribution-aware machine learning framework capable of accurately predicting the complete fiber diameter spectrum from standard process parameters. Seven models were rigorously trained using nested cross-validation and a leave-one-study-out strategy, and their predictions were interpreted through an integrated multi-dimensional explainability analysis combining SHAP values, variable importance, and three-dimensional parameter maps. Evaluated across multiple biopolymers, nonlinear models achieved R² values exceeding 0.91, with experimental validation confirming strong agreement between predicted and measured diameter distributions, thereby significantly advancing data-driven optimization of scaffold architecture.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• electrospinning
• fibre diameter distribution
• machine learning
• scaffold performance
• process optimisation

Innovation

• distribution-aware machine learning
• electrospun nanofibre diameter prediction
• interpretable AI
• meta-dataset curation
• process-structure relationship