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Integration of image processing and artificial neural networks (ANN) modeling for prediction of electrospun poly(ε-caprolactone) fibre diameters
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DOI：10.1016/j.matdes.2025.115180.png)
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Abstract
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• DiameterJ shows better accuracy and throughput in fibre analysis than SIMPoly. • ANN exhibits excellent predictive performance with R2 > 0.97 and errors <4 %. • ANN outperforms RSM in generalizability and reliability for diameter prediction. • Molecular weight and concentration are the dominant factors affecting diameter. • Image processing combined with machine learning aids fibre morphology prediction.
Keywords:
Electrospun PCL nanofiber
Image processing
DiameterJ
Artificial neural network
Response surface methodology
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