## 4 Experiments and Results
### Model EvaluationResults  
This section shows that the approach proposed in this paper can produce meaningful results. This paper used three AI algorithms for AI learning. The most basic models of MLP (multi layer perceptron), LSTM, and GRU were used, and learning was carried out with a hyper parameter with a batch size of 40, number of epochs of 1000, and learning rate of 0.001. As an evaluation index, an evaluation was conducted based on the aforementioned MAE. As a result, when comparing the training data model and the testing data model for all three models, we found that the MAE change in value according to the increase in epochs is similar, which shows that it is an accurate model that it is not overfitted to one side. In addition, all three models showed convergence close to 0.1 (hours).  
Figure 11 shows the progression of training data used to predict the remaining cycle time of the manufacturing process using the MLP algorithm. The left side is a graph of the results after training using the training data, and the right side is a graph where the MAE is extracted by inputting the test data into the MLP model trained using the testing data. Both the left and right graphs show a relatively similar downward trend, and the fact that the model is not overfitted is confirmed by the shape of both graphs, indicating that the data are not too biased to one side. This graph using the MLP algorithm shows mild convergence after 400 epochs. As a result, it can be confirmed that the best MAE value is close to 0.14 (hours).  
Figure 12 shows the progression of training data used to predict the remaining cycle time of the manufacturing process using the LSTM algorithm. As in Figure 11, the left side is after training using the training data, and the right side considers the MAE extracted by inputting the test data into the LSTM model trained using the testing data. These models also have a downward trend to some extent, similar for both the left and right graphs. Thus, it can be seen that it is a valid model by noting it is not overfitted to one side. In the case of the LSTM learning model, it can be seen that the MAE rapidly decelerates after 100 epochs. It can be seen from the testing graph on the right that the best MAE value converges to 0.12 (hours).  
Figure 10: Manufacturing process overview.  
Figure 13 shows the results of training to predict the remaining cycle time of the manufacturing process using the GRU algorithm. As in Figures 11 and 12, the left side is after training using the training data, and the right side is obtained by extracting the MAE by inputting the test data into the GRU model trained using the testing data. The model also has a downward trend in both left and right graphs, demonstrating that the learning results are valid by noting there is no overfitting to one side too much. In the case of the GRU learning model, it can be seen that the MAE rapidly decelerates after 50 epochs, showing the best results among the three models. It can be seen from Figure 13 that the MAE value of the GRU learning model converges to 0.1 (hours).  
Figure 11: Comparison between train data and test data of the model using the MLP algorithm.  
Figure 12: Comparison between train data and test data of the model using the LSTM algorithm.