AccScience Publishing / IJAMD / Online First / DOI: 10.36922/IJAMD025260022
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ORIGINAL RESEARCH ARTICLE

Data-driven optimization of biaxial shrinkage and stability in electrospun membranes via machine learning and Monte Carlo simulation

Shiyu He1,2 ,  Chentong Gao2,3 ,  Runzhi Lu2,4 ,  Fei Xiao1,5* ,  Li Cong Huang6 ,  Wei Min Huang2*
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1 State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
2 School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
3 College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
4 School of Civil Engineering, Southeast University, Nanjing, Jiangsu, China
5 Department of Computer Science, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
6 School of Computing, National University of Singapore, Singapore
IJAMD, 025260022 https://doi.org/10.36922/IJAMD025260022
Received: 26 June 2025 | Revised: 15 August 2025 | Accepted: 21 August 2025 | Published online: 9 September 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Controlling shrinkage behavior in electrospun membranes is critical for applications that require precise dimensional or mechanical performance. However, experimental variability and limited datasets often hinder the development of robust process models. This study introduces a data-driven framework that combines machine learning with Monte Carlo simulation to enable both accurate and stable shrinkage control in electrospinning using a small experimental dataset. Multiple regression models were trained to predict biaxial shrinkage ratios and their variability, with support vector regression and extreme gradient boosting showing the best performance for accuracy and stability prediction, respectively. Feature importance analysis revealed applied voltage and thermoplastic polyurethane concentration as the dominant parameters. A Monte Carlo-based optimization strategy was employed to identify process parameter sets that achieve target shrinkage ratios while minimizing output variability. The proposed approach enables multi-objective optimization in low-data, high-variability manufacturing environments, offering practical insights into precision fabrication of stimulus-responsive membranes.

Graphical abstract
Keywords
Electrospinning
Shrinkage stability
Machine learning
Monte Carlo simulation
Process parameter optimization
Funding
This work was funded by the National Natural Science Foundation of China (grant no.: 52031005, 52571227), Natural Science Foundation of Shanghai (grant no.: 24ZR1438200), Shanghai Academy of Spaceflight Technology Joint Research Fund (grant no.: USCAST2023-19), Equipment Development Department Huiyan Action (grant no.: 5D3D1365), and China Scholarship Council (grant no.:202406230025).
Conflict of interest
The authors declare they have no competing interests.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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