## 4 Conclusions  
By using experimental data, this study demonstrated herein that thermal degradation of 3D-printed PLA could be classified using ANNs based on FTIR. Among the eight types of FTIR datasets, the ANN model could classify four levels of thermal degradation. As the wavenumber range varied, the accuracy of the ANN model changed. The ANN model could suggest the best wavenumber ranges. The ANN model was 100% accurate for the seven wavenumber divisions of P2/4, P3/8, P7/8, P1/16, P2/16, P3/16, and P4/16. The wavenumber ranges, including the characteristic peaks of 757 cm\({}^{-1}\), 870 cm\({}^{-1}\), 956 cm\({}^{-1}\), 1184 cm\({}^{-1}\), 1757 cm\({}^{-1}\), and 3501 cm\({}^{-1}\), correspond to a higher accuracy of the ANN model. In this  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Name of Division**} & **Range of Wavenumber** & **Accuracy of the ANN** & **Characteristic Peaks** \\  & **(cm\({}^{-1}\))** & **Model (\%)** & **(cm\({}^{-1}\))** \\ \hline P6/8 & 2724–3160 & 45\% & 2946–2998 cm\({}^{-1}\) \\ P7/8 & 3160–3579 & 100\% & 3501 cm\({}^{-1}\) \\ P8/8 & 3579–4000 & 65\% & 3656 cm\({}^{-1}\) \\ P1/16 & 650–859 & 100\% & 757 cm\({}^{-1}\) \\ P2/16 & 859–1068 & 100\% & 870 cm\({}^{-1}\) \\  & & & 956 cm\({}^{-1}\) \\ P3/16 & 1068–1277 & 100\% & 1184 cm\({}^{-1}\) \\ P4/16 & 1277–1487 & 100\% & \\ P5/16 & 1487–1696 & 70\% & \\ P6/16 & 1696–1905 & 95\% & 1757 cm\({}^{-1}\) \\ P7/16 & 1905–2115 & 50\% & \\ P8/16 & 2115–2324 & 25\% & \\ P9/16 & 2324–2533 & 35\% & \\ P10/16 & 2533–2724 & 45\% & \\ P11/16 & 2724–2951 & 45\% & \\ P12/16 & 2951–3160 & 75\% & 2946–2998 cm\({}^{-1}\) \\ P13/16 & 3160–3370 & 50\% & \\ P14/16 & 3370–3579 & 50\% & 3501 cm\({}^{-1}\) \\ P15/16 & 3579–3788 & 65\% & 3656 cm\({}^{-1}\) \\ P16/16 & 3788–4000 & 65\% & \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Characteristic Peaks**} & **Description** \\ \hline
757 cm\({}^{-1}\) & -C-C- crystalline phase \\
870 cm\({}^{-1}\) & -C-C- amorphous phase \\
956 cm\({}^{-1}\) & C-CH\({}_{3}\) group \\
1184 cm\({}^{-1}\) & C-O-C group \\
1757 cm\({}^{-1}\) & Carbonyl Group (C=O) \\
2946–2998 cm\({}^{-1}\) & CH\({}_{3}\) group \\
3501 cm\({}^{-1}\) & The carboxylic acid terminal group \\
3656 cm\({}^{-1}\) & Hydroxyl group (O-H) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of diverse characteristic peaks for the neat PLA [9; 20; 21].  
study, the following classifiers for the thermal degradation of 3D-printed PLA were identified: (-C-C-) stretching in the crystalline and the amorphous phases, (C-CH\({}_{3}\)) stretching, (C-O-C) asymmetric stretching, carbonyl group (C=O) stretching, and stretching of the carboxylic acid terminal group.  
Currently, this methodology has used FTIR datasets without any preprocessing such as normalization. This study assumed ANNs could classify raw FTIR spectra. However, the preprocessing of the datasets may give better performance to ANNs so that it could be futher work.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2017R1C1B5074219) and the Korea Basic Science Institute(KBSI) National Research Facilities & Equipment Center (NFEC) grant funded by the Korea government (Ministry of Education) (No. 2019R1A6C1010045).  
The author declare no conflict of interest.