Abstract
Electrospinning is a manufacturing technique used to produce nanofibers for engineering applications. This process depends on several control parameters (such as solution concentration, applied voltage, flow rate, tip-to-collector distance) whose variations during the experiments may lead to the formation of defective nanofibers (D-NF) along with non-defective nanofibers (ND-NF). D-NF present either with impurities or morphological defects that prevent their practical use in nanotechnology. In this context, here, a data augmentation based machine learning approach is proposed to classify Scanning Electron Microscope (SEM) images in two classes (i.e., D-NF vs. ND-NF). To this end, a custom Convolutional Neural Network (CNN) is developed to perform the binary classification task, achieving an accuracy rate up to 93.85%. Moreover, the explainability of the proposed CNN is also explored by means of an occlusion sensitivity analysis in order to investigate which area of the SEM image mostly contributes to the classification task. The achieved encouraging findings stimulate the use of the proposed framework in industrial applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Teo, W.E., Ramakrishna, S.: A review on electrospinning design and nanofibre assemblies. Nanotechnology 17(14), R89 (2006)
Agarwal, S., Wendorff, J.H., Greiner, A.: Use of electrospinning technique for biomedical applications. Polymer 49(26), 5603–5621 (2008)
Khil, M.S., Cha, D.I., Kim, H.Y., Kim, I.S., Bhattarai, N.: Electrospun nanofibrous polyurethane membrane as wound dressing. J. Biomed. Mater. Res. Part B: Appl. Biomater. Official J. Soc. Biomater. Japan. Soc. Biomater. Aust. Soc. Biomater. Korean Soc. Biomater. 67(2), 675–679 (2003)
Hu, X., Liu, S., Zhou, G., Huang, Y., Xie, Z., Jing, X.: Electrospinning of polymeric nanofibers for drug delivery applications. J. Controlled Release 185, 12–21 (2014)
Suja, P., Reshmi, C., Sagitha, P., Sujith, A.: Electrospun nanofibrous membranes for water purification. Polym. Rev. 57(3), 467–504 (2017)
Li, X., et al.: Electrospinning-based strategies for battery materials. Adv. Energy Mater. 11(2), 2000845 (2021)
Ieracitano, C., Mammone, N., Bramanti, A., Hussain, A., Morabito, F.C.: A convolutional neural network approach for classification of dementia stages based on 2d-spectral representation of EEG recordings. Neurocomputing 323, 96–107 (2019)
Ieracitano, C., Adeel, A., Morabito, F.C., Hussain, A.: A novel statistical analysis and autoencoder driven intelligent intrusion detection approach. Neurocomputing 387, 51–62 (2020)
Mammone, N., Ieracitano, C., Morabito, F.C.: A deep CNN approach to decode motor preparation of upper limbs from time-frequency maps of EEG signals at source level. Neural Netw. 124, 357–372 (2020)
Carrera, D., Manganini, F., Boracchi, G., Lanzarone, E.: Defect detection in SEM images of nanofibrous materials. IEEE Trans. Indus. Inf. 13(2), 551–561 (2016)
Napoletano, P., Piccoli, F., Schettini, R.: Anomaly detection in nanofibrous materials by CNN-based self-similarity. Sensors 18(1), 209 (2018)
Ieracitano, C., Pantó, F., Mammone, N., Paviglianiti, A., Frontera, P., Morabito, F.C.: Toward an automatic classification of SEM images of nanomaterials via a deep learning approach. In: Esposito, A., Faundez-Zanuy, M., Morabito, F.C., Pasero, E. (eds.) Neural Approaches to Dynamics of Signal Exchanges. SIST, vol. 151, pp. 61–72. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-8950-4_7
Ieracitano, C., Paviglianiti, A., Mammone, N., Versaci, M., Pasero, E., Morabito, F.C.: SoCNNet: an optimized Sobel filter based convolutional neural network for SEM images classification of nanomaterials. In: Esposito, A., Faundez-Zanuy, M., Morabito, F.C., Pasero, E. (eds.) Progresses in Artificial Intelligence and Neural Systems. SIST, vol. 184, pp. 103–113. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5093-5_10
Ieracitano, C., Paviglianiti, A., Campolo, M., Hussain, A., Pasero, E., Morabito, F.C.: A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers. IEEE/CAA J. Automatica Sinica 8(1), 64–76 (2021)
Tjoa, E., Guan, C.: A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE Transactions on Neural Networks and Learning Systems (2020)
Stepin, I., Alonso, J.M., Catala, A., Pereira-Fariña, M.: A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence. IEEE Access 9, 11974–12001 (2021)
Meske, C., Bunde, E., Schneider, J., Gersch, M.: Explainable artificial intelligence: Objectives, stakeholders, and future research opportunities. Inf. Syst. Manage. 1–11 (2020)
Doshi, J., Reneker, D.H.: Electrospinning process and applications of electrospun fibers. J. Electrostat. 35(2–3), 151–160 (1995)
Uyar, T., Besenbacher, F.: Electrospinning of uniform polystyrene fibers: the effect of solvent conductivity. Polymer 49(24), 5336–5343 (2008)
Zeng, J., Haoqing, H., Schaper, A., Wendorff, J.H., Greiner, A.: Poly-l-lactide nanofibers by electrospinning-influence of solution viscosity and electrical conductivity on fiber diameter and fiber morphology. e-Polymers 3(1), (2003)
Okutan, N., Terzi, P., Altay, F.: Affecting parameters on electrospinning process and characterization of electrospun gelatin nanofibers. Food Hydrocolloids 39, 19–26 (2014)
Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp. 1–6. IEEE (2017)
Zhao, B., Feng, J., Wu, X., Yan, S.: A survey on deep learning-based fine-grained object classification and semantic segmentation. Int. J. Autom. Comput. 14(2), 119–135 (2017)
Wang, W., Yang, Y., Wang, X., Wang, W., Li, J.: Development of convolutional neural network and its application in image classification: a survey. Optical Eng. 58(4), 040901 (2019)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Acknowledgement
This work was supported by POR Calabria FESR FSE 2014–2020 grant number: C39B18000080002. The authors thank the Materials for Environmental and Energy Sustainability Laboratory from the University Mediterranea of Reggio Calabria, Italy, for the D-NF/ND-NF SEM images dataset.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ieracitano, C., Mammone, N., Paviglianiti, A., Morabito, F.C. (2021). Toward an Augmented and Explainable Machine Learning Approach for Classification of Defective Nanomaterial Patches. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-80568-5_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80567-8
Online ISBN: 978-3-030-80568-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)