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Correlating the 3D melt electrospun polycaprolactone fiber diameter and process parameters using neural networks |
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| Authors: | Pasupuleti Lakshmi Narayana Xiao-Song Wang Jong-Taek Yeom Anoop Kumar Maurya Won-Seok Bang Ommi Srikanth Maddika Harinatha Reddy Jae-Keun Hong Nagireddy Gari Subba Reddy |
| Affiliation: | 1. Advanced Metals Division, Titanium Department, Korea Institute of Materials Science, Changwon, Republic of Korea Virtual Materials Lab, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, Republic of Korea;2. Advanced Metals Division, Titanium Department, Korea Institute of Materials Science, Changwon, Republic of Korea;3. Business Department, Gyeongsang National University, Jinju, Republic of Korea;4. Department of Mechanical Engineering, Dhanekula Institute of Engineering & Technology, Vijayawada, India;5. Department of Mechanical Engineering, St. Peters Engineering College, Hyderabad, India;6. Virtual Materials Lab, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju, Republic of Korea |
| Abstract: | In the present work, we developed an artificial neural networks (ANN) model to predict and analyze the polycaprolactone fiber diameter as a function of 3D melt electrospinning process parameters. A total of 35 datasets having various combinations of electrospinning writing process variables (collector speed, tip to nozzle distance, applied pressure, and voltage) and resultant fiber diameter were considered for model development. The designed stand-alone ANN software extracts relationships between the process variables and fiber diameter in a 3D melt electrospinning system. The developed model could predict the fiber diameter with reasonable accuracy for both train (28) and test (7) datasets. The relative index of importance revealed the significance of process variables on the fiber diameter. Virtual melt spinning system with the mean values of the process variables identifies the quantitative relationship between the fiber diameter and process variables. |
| Keywords: | fibers structure-property relationships theory and modeling |