# An Adaptive Process Monitoring Method Based on an Improved ADALINE Neural Network
## 4 Experiment and Results Analysis
### Results Analysis and Discussion  
Drawing on the experimental data acquired in the preceding section, we conducted a comparison among the traditional static Shewhart method, the variable-sampling VP dynamic control limit method, and the EWMA dynamic control limit method, which takes into account control limit changes. Through comparing the proposed method with the above-mentioned monitoring methods, we validated the practical effectiveness of these methods. We carried out a statistical analysis of the surface roughness of actual machined parts and assigned scores based on their quality-drift degrees. The results are presented in Figure 12.  
Firstly, we compared different control sampling strategies, the static, and taking the VP variable sampling method as an example of traditional variable sampling strategy, to evaluate the effectiveness of our proposed sampling strategy. As depicted in Figure 12, the blue line corresponds to the traditional static monitoring sampling method, the green line represents the variable sampling VP control limit method, and the sky-blue line denotes the adaptive monitoring sampling method. Evidently, in contrast to the static monitoring sampling methods, the adaptive monitoring strategy predominantly samples products with low quality scores. For products scoring below 60, the coverage rate attains 96%. When contrasted with regular monitoring strategies, products with scores below 75 exhibit a coverage rate of 82%. Therefore, the implementation of the adaptive monitoring strategy does not compromise the monitoring of low-quality products. Moreover, the recognition performance of the VP control chart variable sampling technology shows negligible divergence compared to the adaptive monitoring strategy. Consequently, this proposed method can substantially enhance the monitoring efficiency. Furthermore, by using the proposed approach to compare with the traditional static and dynamic control limit state monitoring method and using the surface roughness quality score to evaluate and calculate the variance, mean value, and median scores, we obtain the statistical results in Table 4.  
Figure 12: Comparison of monitoring effects for different control limit monitoring sampling strategies. The results show that the mean and median of the surface roughness quality scores obtained by the adaptive monitoring method proposed in this paper are larger than those obtained using the traditional monitoring method, and the variance is smaller than that obtained using the traditional method. This method is more suitable for practical engineering applications. When delving into the underlying reasons, in comparison with static monitoring methods, both the VP variable sampling monitoring method and the EWMA control chart dynamic method, like the proposed method in this study, adopt an adaptive monitoring strategy and take into account the quality variations during the production process, enabling the adjustment of the control limit strategy. Although the EWMA control chart assigns relatively higher weights to the recent data changes to adapt dynamically to the gradual process alterations and can mirror the actual process state, our employment of the ALDINE neural network method endows it with distinct advantages. The ALDINE neural network method features low computational complexity and high efficiency in processing large volumes of data, allowing for more prompt responses. As a neural network algorithm, it can comprehensively consider the cumulative effect of data as the production line progresses, continuously enhancing the control accuracy. Consequently, compared with other methods, this method is capable of achieving superior outcomes. So, our method is more suitable for practical engineering applications. Surface roughness can significantly impact the wear resistance, fatigue strength, and fit performance between parts.