# Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory
## 5 Conclusions  
In this paper, we have proposed a method for predicting PQP using statistical analysis and long short-term memory. There are several findings of this study. First, no studies sufficiently consider stability and normality of process conditional variables and time-series work patterns, which we define as work repeatability. And we also have proposed a method for estimating process capability and performance by comprehensively considering periodic and repetitive work pattern characteristics over time. Second, statistical, machine learning, and deep learning analysis can be applied to predict PQP characteristics and estimate the anomalies in the production process. To do it better, we have demonstrated that LSTM, a deep learning algorithm-based method, can evaluate the stability of the production process with the highest accuracy compared with other state-of-the-art ANN, RF, and ANN methods. One of the essential applications of predicting PQP characteristics is predictive maintenance in smart manufacturing. That is, we can collect process condition variables over time by monitoring the states of product manufacturing and use these variables to predict possible failures, process capability, and performance in the future. Third, the result of this study is exemplified by a real case study, a smart manufacturing system in the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Recall** & **Precision** & **Accuracy** \\ \hline LSTM & 0.91 & 0.73 & 0.93 \\ ANN & 0.90 & 0.70 & 0.83 \\ Random Forest & 0.92 & 0.65 & 0.89 \\ ARIMA & 0.89 & 0.69 & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Result of model comparison. automotive industry, to verify the product quality improvement. The approach proposed in this research is currently used by quality management departments in the manufacturing industry to monitor the production process and prevent product defects. From the results demonstrated in this study, we can conclude that utilizing statistical analysis involving MCtA, PCCA, PTAD, and SPCA can effectively determine PQP in manufacturing processes. In addition, the application of deep learning methods is found to be effective in predicting PQP in future processes and determining the optimal working condition, which can be helpful for manufacturing operators. In other words, our proposed model accomplished its goals, and so is effective and beneficial for the automotive industry, which can identify and prevent quality defects of final products, increasing overall product quality significantly. During this research, some challenges were found that could potentially be considered as future work. Some limitations with the dataset used for training and validating the prediction model need to be addressed. Future research will collect more valuable data from machines in the manufacturing industry to improve our dataset. We suppose that our proposed approach will perform better after conducting experiments with a larger dataset. Furthermore, we intend to explore more feature extraction from the original dataset by using various types of process analysis to improve the accuracy of the prediction model and other types of unsupervised feature learning. In addition, we plan to improve the prediction algorithm by optimization and by hyper-parameter learning model. Conceptualization, T.P. and K.-H.Y. ; methodology, T.P. ; formal analysis, T.P. ; data curation, T.P., S.-H.K. and G.-A.R. ; writing--original draft preparation, T.P. ; writing--review and editing, T.C. and A.N. ; visualization, T.P. ; supervision, K.-H.Y. and A.N. ; project administration, K.-H.Y. ; funding acquisition, K.-H.Y. and A.N. All authors have read and agreed to the published version of the manuscript.