## 5 Conclusions  
In this paper, a one-dimensional convolutional neural network was constructed for predicting the two-dimensional plane temperature distribution of a workpiece during plasma arc additive manufacturing processing using titanium-aluminum twin-wire. Through the reasonable use of machine learning, this paper explored a neural network combined numerical simulation method and process prediction workflow that included the finite element method, a convolutional neural network, and transfer learning. The accuracy of the prediction reached more than 99%. The main contributions of this paper can be summarized as follows:  
1. The article organically combined the FEM with the machine learning method and transfer training methods. A basic training and transfer training workflow was proposed, which provides a large amount of training data. At the same time, it transforms the model's training process into a dynamic process to strengthen the model's prediction robustness. 2. The one-dimensional convolutional neural network model designed in this paper can effectively be fed one-dimensional processed features and predict temperature results during the manufacturing process. The MSE of the temperature field predicted by the neural network was reached within 0.5, and the prediction accuracy exceeded 99%. 3. The model performed well in the validation set and had a good robustness. The R\({}^{2}\) of the prediction results in the validation set could reach 0.999963, and the main error was concentrated in the high-temperature region of the workpiece. Through transfer training, the prediction error could be reduced to the desired value after 30 iterations. 4. The proposed Conv1D model had a better performance than the fully connected neural network model by using 50% of the running time, 80% of the training time, and only 50% of the ROM occupation. Compared with the traditional FEM prediction of temperature, the neural network model has obvious advantages in running time and ROM usage. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Running Time** & **Read-Only Memory Occupation** & **R\({}^{2}\)** \\ \hline Conv1D & 0.7 s & 52 MB & 0.99999251 \\ \hline FCNN & 1.2 s & 111.5 MB & 0.99999597 \\ \hline FEM & 3 min 55 s & 721 MB & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparations between machine learning methods and finite element method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **[PERSON]** & **[PERSON]**[39] & **[PERSON]**[40] & **[PERSON]**[41] & **[PERSON]**[42] \\ \hline MSE & 0.8995 & / & / & 12.8881 & 18.6624 \\ \hline R\({}^{2}\) & 0.9999 & 0.9995 & 0.9951 & / & 0.9980 \\ \hline Model type & 1D-CNN & 2D-CNN & FCNN & Physical-informed FCNN & Physical-informed FCNN \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the performance between the Conv1d model and typical ML models. The characterization method of matching experimental parameters and experimental phenomena through neural networks will remain our focus in subsequent studies, and we will explore the relationship between other parameters and the prediction effect based on this study. In addition, there is still room for improvement in the structure of the ML model. We will continue to explore the construction of the model to obtain a more accurate, robust, and better-performing neural network model. Conceptualization, N.P. and X.Y. ; methodology, N.P. and X.Y. ; validation, N.P., X.Y., P.X. and G.Z. ; formal analysis, N.P. ; investigation, N.P., P.X. and G.Z. ; writing--original draft preparation, N.P. ; writing--review and editing, X.Y. ; project administration, X.Y. ; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript. This research was supported by the Class III Peak Discipline of Shanghai-Materials Science and Engineering (High-Energy Beam Intelligent Processing and Green Manufacturing). Not applicable. Data Availability Statement: Data available on request due to restrictions.