Degradation Classification of 3D Printing Thermoplastics Using Fourier Transform Infrared Spectroscopy and Artificial Neural Networks
Abstract
:1. Introduction
2. Methodology
2.1. Fused Deposition Modeling
2.2. High-Temperature Storage Test for Polymer Degradation
2.3. Fourier Transform Infrared Spectroscopy
2.4. Artificial Neural Networks (ANNs)
- The size of the input layer and that of the output layer are 6948 and 4.
- The size of the hidden layers are between the size of the input layer and that of the output layer.
- The number of hidden neurons is one half of the number of neurons in the previous hidden layer.
3. Results and Discussion
3.1. Input Datasets for ANNs
3.2. Validation of ANN Models
4. Conclusions and Future Work
Funding
Acknowledgements
Conflicts of Interest
References
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Zhang, S.-U. Degradation Classification of 3D Printing Thermoplastics Using Fourier Transform Infrared Spectroscopy and Artificial Neural Networks. Appl. Sci. 2018, 8, 1224. https://doi.org/10.3390/app8081224
Zhang S-U. Degradation Classification of 3D Printing Thermoplastics Using Fourier Transform Infrared Spectroscopy and Artificial Neural Networks. Applied Sciences. 2018; 8(8):1224. https://doi.org/10.3390/app8081224
Chicago/Turabian StyleZhang, Sung-Uk. 2018. "Degradation Classification of 3D Printing Thermoplastics Using Fourier Transform Infrared Spectroscopy and Artificial Neural Networks" Applied Sciences 8, no. 8: 1224. https://doi.org/10.3390/app8081224