Integration of image processing and artificial neural networks (ANN) modeling for prediction of electrospun poly(ε-caprolactone) fibre diameters

• DiameterJ shows better accuracy and throughput in fibre analysis than SIMPoly. • ANN exhibits excellent predictive performance with R 2 > 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. Electrospun nanofibrous membranes are widely used in biomedical, filtration, and energy applications, where fibre diameter plays a key role in determining membrane morphology and performance. Conventional manual measurement of fibre diameters from scanning electron microscope (SEM) images is inefficient and subject to human error. This study compared two image-processing tools, DiameterJ and SIMPoly, to improve measurement efficiency and accuracy of fibre diameter. DiameterJ was selected for its higher reliability and batch-processing capability. Using DiameterJ, 144 datasets were collected and used to train an artificial neural network (ANN) model with four electrospinning parameters (molecular weight, solution concentration, flow rate, and tip-to-collector distance) as inputs. The ANN model showed high predictive accuracy, with correlation coefficients exceeding 0.97 and prediction errors below 4%. A response surface methodology (RSM) model was also developed for comparison, but showed limited predictive capability for unseen conditions, with errors up to 28.57%. The ANN exhibited superior reliability and generalizability. Index of relative importance (IRI) and contour analyses revealed molecular weight and concentration as dominant factors. The proposed integration of automated image analysis with ANN provides a data-driven and scalable framework for intelligent design and optimisation of electrospun materials, with potential applicability across diverse fibrous manufacturing systems.