## 4 Results and Discussion
### The Performance of Conv1D Model for Computational Cost  
A small computational cost while the machine learning model is running is preferred so that it can be deployed on adequate hardware. In addition, choosing the right model can satisfy the requirements on the response speed in the real manufacturing order. With accuracy requirements met, models with smaller computational costs and faster runtimes will have higher utility values. Figure 11 compares the difference in the number of parameters between the Conv1D model and the commonly used fully connected neural network (FCNN) (40). In the convolutional stage, the number of parameters used in the Conv1D model was smaller than that of the FCNN model, floating from 10 times to 1000 times. It is noted that the definition of the machine learning model's training parameters refers to those participating in forward and backward propagation, in which circumstance layers like max-pooling contain no training parameters. The advantage of the smaller number of parameters comes from the computation principle of the sliding convolution kernel on the input parameters shown in Figure 4 at Section 3, which makes the Conv1D model require significantly fewer parameters compared to the FCNN model when processing the same input data. Therefore, the Conv1D model has a significant advantage in terms of ROM occupation. Figure 10: Loss curves of transfer training and base training (300 epochs). Figure 12a gives the training time required for both models over 200 to 2000 iterations and Figure 12b shows the highest accuracy achieved in the training stage. Although the Conv1D model has a more complex architecture than the FCNN model, the overall computation time was still quicker than that of the FCNN model due to the difference in the parameter numbers between the convolutional layer and the fully connected layer. As shown in Figure 12a, when the number of iterations reached 2000, the FCNN model took 26,245 s, while the Conv1D model took 20% less time, which was 21,042 s, to complete the training. Since the training parameters of the FCNN model were much greater than those of the Conv1D model, this resulted in the FCNN model having a slightly higher prediction accuracy than the Conv1D model in the overall training stage. However, when the number of iterations was increased to 1000 and above, the average prediction difference between the two models was less than 0.5 \({}^{\circ}\)C, which is numerically acceptable. Table 4 gives the running time, Read-Only Memory occupation, and R2 performances of the three Conv1D, FCNN, and FEA models on the same dataset with a length of 100. In terms of running time, the two neural network models required much less time than the finite element model, with a time requirement of only 0.5% of the latter, while the Conv1D model took 0.5 s less time compared to the FCNN model, speeding up the forward propagation by about 50%. Considering the aspect of ROM occupation, the Conv1D model was only half the size of the FCNN model, occupying a size of 52 MB, and the size of the FEM results file was 14 times larger than that of the Conv1D model. As for accuracy, the difference between the three models was almost negligible. Figure 11: Comparison of the parameter numbers between Conv1D model and FCNN model. Figure 12: Comparations between Conv1D model and FCNN model at perspectives of (**a**) training time and (**b**) accuracy. Table 5 summarizes the performances of typical CNN and FCNN models. It can be seen that the overall accuracy of the CNN model was better than that of the FCNN model. With appropriate modification to the model structure, the prediction ability of the ML model can be improved. The three sections in this chapter provide a systematic evaluation corresponding to the dimensions of the Conv1D model's accuracy, robustness, and capabilities.