Correlating the 3D melt electrospun polycaprolactone fiber diameter and process parameters using neural networks

Narayana, P.L.; Wang, Xiaosong; Yeom, Jong‐Taek; Maurya, A.K.; Bang, Wonseok; Srikanth, O.; Reddy, M. Harinatha; Hong, Jae‐Keun; Reddy, Nagireddy Gari Subba

doi:10.1002/app.50956

Cited by 17 publications

(4 citation statements)

References 31 publications

(34 reference statements)

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“…Compared to traditional optimization methods such as trial-and-error experimentation and statistical modeling, ANNs can handle large amounts of data and identify nonlinear relationships between process parameters and fiber properties that may not be captured by other methods. Additionally, ANNs can continuously learn and improve over time, making them well-suited for dynamic and complex systems like electrospinning [21][22][23][24]. Overall, the use of ANNs in electrospinning optimization has the potential to accelerate the development of new PAN nanofiber materials with tailored properties for various applications.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Electrospun Nanofiber Diameter Based on Artificial Neural Network

Zhou

Gao

et al. 2023

Polymers

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Electrospinning technology enables the fabrication of electrospun nanofibers with exceptional properties, which are highly influenced by their diameter. This work focuses on the electrospinning of polyacrylonitrile (PAN) to obtain PAN nanofibers under different processing conditions. The morphology and size of the resulting PAN nanofibers were characterized using scanning electron microscopy (SEM), and the corresponding diameter data were measured using Nano Measure 1.2 software. The processing conditions and corresponding nanofiber diameter data were then inputted into an artificial neural network (ANN) to establish the relationship between the electrospinning process parameters (polymer concentration, applied voltage, collecting distance, and solution flow rate), and the diameter of PAN nanofibers. The results indicate that the polymer concentration has the greatest influence on the diameter of PAN nanofibers. The developed neural network prediction model provides guidance for the preparation of PAN nanofibers with specific dimensions.

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Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Electrospun Nanofiber Diameter Based on Artificial Neural Network

Zhou

Gao

et al. 2023

Polymers

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“…Even though EBB is the most prevalent, a priori printing parameter optimization has also been proposed for other bioprinting technologies [86][87][88][89]96]. For instance, Wu et al used an imaging system to monitor the droplet formation and flight of bioinks in piezoelectric IJB (figure 4(b)).…”

Section: For Pre-process Qcmentioning

confidence: 99%

Enhancing quality control in bioprinting through machine learning

Bonatti,

Vozzi,

De Maria

2024

Biofabrication

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Bioprinting technologies have been extensively studied in literature to fabricate three-dimensional constructs for Tissue Engineering applications. However, very few examples are currently available on clinical trials using bioprinted products, due to a combination of technological challenges (i.e., difficulties in replicating the native tissue complexity, long printing times, limited choice of printable biomaterials) and regulatory barriers (i.e., no clear indication on the product classification in the current regulatory framework). In particular, quality control solutions are needed at different stages of the bioprinting workflow (including pre-process optimization, in-process monitoring, and post-process assessment) to guarantee a repeatable product which is functional and safe for the patient. In this context, machine learning algorithms can be envisioned as a promising solution for the automatization of the quality assessment, reducing the inter-batch variability and thus potentially accelerating the product clinical translation and commercialization. In this review, we comprehensively analyse the main solutions that are being developed in the bioprinting literature on quality control enabled by machine learning, evaluating different models from a technical perspective, including the amount and type of data used, the algorithms and the performance measures. Finally, we give a perspective view on current challenges and future research directions on using these technologies to enhance the quality assessment in bioprinting.

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“…Since the error values were minimal with two hidden layers, the configuration 4-8-8-1 was selected for maximal optimization, reporting R 2 values of 0.79 and 0.94 for the training and testing data, respectively. Lakshmi Narayana et al [ 20 ] developed an ANN in order to predict and analyze the diameter of polycaprolactone (PCL) fibers as a function of the parameters of the 3D melt electrospinning process. The model employed the backpropagation algorithm for training and process variables such as collector rate, tip-to-nozzle distance, applied pressure, voltage, and average microfiber diameter (output variable) were considered.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks for Predicting the Diameter of Electrospun Nanofibers Synthesized from Solutions/Emulsions of Biopolymers and Oils

Cuahuizo-Huitzil,

Olivares-Xometl,

Eugenia Castro

et al. 2023

Materials

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In the present work, different configurations of nt iartificial neural networks (ANNs) were analyzed in order to predict the experimental diameter of nanofibers produced by means of the electrospinning process and employing polyvinyl alcohol (PVA), PVA/chitosan (CS) and PVA/aloe vera (Av) solutions. In addition, gelatin type A (GT)/alpha-tocopherol (α-TOC), PVA/olive oil (OO), PVA/orange essential oil (OEO), and PVA/anise oil (AO) emulsions were used. The experimental diameters of the nanofibers electrospun from the different tested systems were obtained using scanning electron microscopy (SEM) and ranged from 93.52 nm to 352.1 nm. Of the three studied ANNs, the one that displayed the best prediction results was the one with three hidden layers with the flow rate, voltage, viscosity, and conductivity variables. The calculation error between the experimental and calculated diameters was 3.79%. Additionally, the correlation coefficient (R2) was identified as a function of the ANN configuration, obtaining values of 0.96, 0.98, and 0.98 for one, two, and three hidden layer(s), respectively. It was found that an ANN configuration having more than three hidden layers did not improve the prediction of the experimental diameter of synthesized nanofibers.

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Correlating the 3D melt electrospun polycaprolactone fiber diameter and process parameters using neural networks

Cited by 17 publications

References 31 publications

Analysis and Prediction of Electrospun Nanofiber Diameter Based on Artificial Neural Network

Analysis and Prediction of Electrospun Nanofiber Diameter Based on Artificial Neural Network

Enhancing quality control in bioprinting through machine learning

Artificial Neural Networks for Predicting the Diameter of Electrospun Nanofibers Synthesized from Solutions/Emulsions of Biopolymers and Oils

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