# An Adaptive Process Monitoring Method Based on an Improved ADALINE Neural Network
## 5 Conclusions  
This paper presents a study using a machining process fault monitoring method based on status information. A method for implementing adaptive monitoring by monitoring sensor signals during operation is proposed. First, based on the modulus maximum, a method to achieve adaptive wavelet threshold denoising is proposed. Second, we consider the quality change trends and design monitoring strategies to sample for different quality trends, saving the computing power of the equipment and improving the monitoring efficiency. Third, an adaptive update approach of the control limit based on the ADALINE neural network is constructed. This approach combines real-time monitoring data and adjusts its weight with the ADALINE neural network, mining and learning process data, setting dynamic following control limits on the process parameters of the same point in the repetitive machining process, and then carrying out dynamic and spatiotemporal  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method Category** & **Variance** & **Mean** & **Median** \\ \hline Static monitoring method & 0.2842 & 72.88 & 72 \\ \hline VP variable sampling monitoring method & 0.0852 & 85.67 & 83 \\ \hline EWMA dynamic monitoring method & 0.0054 & 87.64 & 83 \\ \hline the proposed monitoring method & 0.0065 & 92.43 & 86 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results of the two process monitoring methods.  
control of the process parameters of the machining process. The proposed approach is more dynamic-sensitive than traditional control methods, and the obtained monitoring accuracy is higher.  
Finally, we use surface roughness processing quality monitoring as an example to verify the approach, and the results show that it is effective. This method is suitable for solving the monitoring problem in the scenario of strong periodic repetitive processing, providing a way to realize the adaptive monitoring of the machining process. For the machining process, the realization of adaptive monitoring of the process has practical significance and is worth discussing. Nevertheless, the proposed method still has certain drawbacks that necessitate further investigation. Firstly, the current method is more amenable to fixed production line manufacturing processes and may encounter certain limitations when applied to complex multi-process scenarios. For some processes, they can be decoupled into a single multi-process or segmented into multiple distinct stages for monitoring and control. However, for more intricate processes, their manufacturing may be highly coupled. In such cases, the model training cost of this method may be exorbitantly high, and its generalization ability may be insufficient. Consequently, our method may demand extensive and targeted optimization to be effectively applicable. Secondly, the present method is currently designed for adaptive control of a single type of quality characteristic. Further research is imperative for the monitoring of multiple quality characteristics, for instance, simultaneously monitoring the geometric dimensions, roughness, and flatness of parts. At present, this method merely focuses on enhancing product quality characteristics through control. However, it could also take into account the joint optimization of maintenance strategies, for example, replacing tools when the quality has declined to a certain level to enhance economic efficiency, which offers a promising direction for further research.