# An Adaptive Process Monitoring Method Based on an Improved ADALINE Neural Network
## 4 Experiment and Results Analysis
### Implementation of the Proposed Approach
#### 4.2.2 Adaptive Monitoring  
For monitoring strategies, by considering the quality characteristic changes during the monitoring process and designing adaptive monitoring strategies to improve monitoring efficiency, the quality trend in the machining process is divided into three types, and corresponding monitoring schemes are set up. In the realm of advanced process monitoring technology, the core competencies lie in monitoring efficiency and quality. The quality characteristic drift \(\delta\) represents the deviation between the quality of the processed product and its most relevant state. In this study, the recognition success rate of products with diverse quality characteristic drifts was employed to characterize the monitoring quality. When there is a quality change during the production process, the probability of successful identification can be determined. The calculation method is as follows: suppose the sample set is \(S\) and the total number of samples is \(m\), which consists of \(m_{1}\) products with a quality offset of \(\delta_{1}\) and \(m_{2}\) products with a quality offset of \(\delta_{2}\). That is, \(S=\{(\delta_{1},m_{1}),(\delta_{2},m_{2}),...,(\delta_{m},m_{m})\}\); for the \(m_{1}\) products with a quality offset of \(\delta_{1}\), the probability of successful recognition is \(P=\sum\limits_{i=1}^{m}P_{i,m_{i}}\). Production process data with different quality offsets were utilized, and the pyDOE2 library in Python was employed to test the recognition scenarios at different levels. Based on sampling experience, the sampling interval \(n_{\text{max}}\) = 20 and the upper limit of the monitoring quantity \(h_{\text{max}}\) = 20 was set. Considering the monitoring strategies, \(n_{1}<n_{2}<n_{3}\), \(h_{1}>h_{2}>h_{3}\). The parameter optimization objectives \(P=\max(\sum\limits_{i=1}^{m}P_{i,m_{i}})\) were established to find the maximum recognition success rate. Simultaneously, the monitoring efficiency \(E\) is characterized in terms of the sampling frequency of process monitoring. The sample size retrieved each time varies dynamically in accordance with the real-time monitoring strategy. In different monitoring modes, the sampling frequency is denoted as \(e\), and the cumulative sampling frequency over a certain period is \(E=e_{1}+e_{2}+e_{3}\). By conducting in-depth analysis, the impacts of the sampling interval and the number of monitoring parameters within the model parameters on both monitoring quality and efficiency are determined. Some of the obtained results are presented in Table 2. And the procedure of a set of adaptive sampling is illustrated in Figure 9.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Noise Reduction Method** & **SNR** & **SSIM** & **RMSE** \\ \hline Original method & 13.07 & 0.89 & 0.091 \\ Adaptive layering method & 16.96 & 0.91 & 0.037 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of the noise reduction results.  
Figure 9: The procedure of a set of adaptive monitoring adaptive sampling.  
The results clearly demonstrate that an increase in the sampling interval and a reduction in the number of monitors can effectively enhance the monitoring efficiency but lead to a decrease in the monitoring success rate. Conversely, a reduction in the sampling interval and an increase in the number of monitors will result in an elevation of the monitoring success rate. The 13 th experiment had a higher recognition success rate of 0.9837 and fewer sampling times of 114, with the optimal parameter combination for this particular process. The corresponding monitoring mode is designed as shown in Table 3.