# Improved Ensemble-Learning Algorithm for Predictive Maintenance in the Manufacturing Process
## 5 Experiment
### Quality Prediction of Scissor Product Packaging in Blister Packing Machining Process  
This study used the preprocessed data to predict the quality of packaging in the blister packing machining process (Table 6). An accuracy and recall rate of 0.992 and 0.997, respectively, were obtained using the proposed method. The boosted decision tree achieved an accuracy of 0.991 and a recall rate of 0.993. The result demonstrated that the proposed method and boosted decision tree achieved the same predictive performance. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Semiconductor Case** & **Blister Packing Machine Case** \\ \hline Predictive Problem & The yield failure in the semiconductor manufacturing process. & The quality of scissor product packaging in the blister packing machining process. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of case studies. \begin{table}
\begin{tabular}{c c c} \hline \hline
**AUC Range** & **Level of Discrimination** \\ \hline AUC = 0.5 & no discrimination \\
0.7 \(\leq\) AUC \(\leq\) 0.8 & acceptable discrimination \\
0.8 \(\leq\) AUC \(\leq\) 0.9 & excellent discrimination \\
0.9 \(\leq\) AUC \(\leq\) 1.0 & outstanding discrimination \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of AUC range. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Ensemble** & **Single** \\ \hline Algorithms & The Proposed Method & Boosted Decision tree & Decision Jungle \\ Accuracy & 0.974 & 0.966 & 0.941 & 0.952 \\ Recall rate & 0.957 & 0.945 & 0.925 & 0.928 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of semiconductor case. Figure 8 shows that ensemble learning is suitable for the blister packing machine case, where all ensemble methods achieved an AUC greater than 0.9. The experimental results have shown that the ensemble method can outperform the traditional single module in prognostic accuracy. Unlike conventional decision trees, which only allow one path to each node, the result shows that the ELAs achieve higher accuracy than the decision tree. In summary, the result obtained using the proposed method was compared with that of the boosted decision tree and decision tree. Furthermore, the obtained result indicates that the ELA achieves more than 95% accuracy in both the semiconductor case and the blister packing machine case. The proposed method outperformed the single learning method in the two cases. The trained model was deployed as a RESTful API and WS, which can be used in edge- or cloud-based analytics for predictive maintenance manufacturing. The data analytics process is fully automated after sending the analytics \begin{table}
\begin{tabular}{c c c c} \hline
**Type** & **Ensemble** & **Single** \\ \hline Algorithms & The Proposed Method & Boosted Decision tree & Decision Jungle & Decision Tree \\ Accuracy & 0.992 & 0.991 & 0.992 & 0.987 \\ Recall rate & 0.997 & 0.993 & 0.992 & 1 \\ \hline \end{tabular}
\end{table} 
Table 6: Results of blister packing machine case. Figure 8: ROC curve of the blister packing machine case. Figure 7: ROC curve of semiconductor case. requirement to the server side via the API. The submission is detected by the server-side process and automatically mapped to a calculation workflow that specifies the task type (e.g., preprocessing, ML training module) according to the API message. The server-side response to the analytics result equipment data can be imported to the cloud platform to improve textile quality by predicting equipment settings. This study uses Python to develop the proposed PdM APIs based on the REpresentational State Transfer (REST) pattern for the web. The RESTful APIs use commands to obtain resources. The state of a resource at any given timestamp is called a resource representation. RESTful APIs have different execution types: \"GET\", \"PUT\", \"POST\", and \"DELETE\". GET is used to retrieve information; PUT is used to transmit information, change the state of or update information; POST is used to transmit information and create a resource; and DELETE is used to delete information.