# An Adaptive Process Monitoring Method Based on an Improved ADALINE Neural Network
###### Abstract
Monitoring approaches use static control limits, which have two critical issues that need to be solved. The first is that continuous dynamic changes require timely monitoring and machining conditions. However, static monitoring methods ignore the change information in the process, leading to a lag in controlling increasingly complex machining processes in modern society. Second, the efficiency of monitoring and the large amount of data generated during the machining process, which bring new challenges to condition monitoring, require us to design more efficient monitoring strategies. To improve the efficiency of statistical process monitoring, promptly detect quality fluctuations, stabilize the production process, and simultaneously save monitoring costs, it is necessary to design more efficient adaptive control limit methods. In the research on adaptive control limits, [PERSON] et al. introduced the method of multiple sets of warning limits [24]. Compared with the traditional fixed-threshold method, this hierarchical approach of adopting different strategies for monitoring and control provides an idea for solving different quality drift problems. Additionally, based on the above, [PERSON] and [PERSON] et al. further proposed the Variable Sampling Interval (VSI) control chart, which considers determining whether to change the sampling samples by comparing the statistic with the warning limit [25]. Essentially, the VSI approach is a control chart design method with variable sampling frequencies. However, when quality drift occurs in the production process (i.e., phenomena such as regular gradual changes in product quality, like the decrease in roughness after tool wear), the above-mentioned monitoring effect cannot be detected in a timely manner. Therefore, the CumulativeSum control chart (CUSUM control chart) based on the sequential test theory and the Exponentially Weighted Moving Average control chart (EWMA control chart) [26; 27; 28] have been developed for monitoring small quality deviations. Correspondingly, numerous scholars have carried out various works, combining the above-mentioned methods with the VSI method, appropriately adjusting the size of the single-sampling sample according to the change in the monitoring statistic to ensure the monitoring ability of the control chart. Thus, the VSS-type control chart method with both warning limits and control limits has been developed. By setting sampling strategies under different control limits and adjusting the sample size information, potential drifts can be detected in a timely manner. [PERSON] [29] combined the advantages of the VSI control chart and the VSS control chart and first proposed the variable sample size and sampling interval. The VSSI control chart dynamically adjusts the sampling sample size and sampling interval according to the change in the statistic. However, the warning limits and control limits for judging the process state in this method remain unchanged and do not change with the change in the process state, which may reduce the monitoring efficiency of the adaptive control chart. In response to the above problems, many scholars at home and abroad have started to study control charts with adaptively optimized sampling sample sizes, sampling intervals, warning limits, and control limits. This type of control chart is collectively referred to as the VP control chart. This approach integrates the advantages of the above-mentioned methods, but the setting of its control limits is essentially still a hierarchical setting method, and it does not consider the utilization and processing of big data in the actual process. Regarding the machining process, the related solutions to solve the above problems are still poor. The development of artificial intelligence technology has provided new inspiration [30]. Through much research, we have found that the ADALINE neural network has a good adaptive ability. Therefore, we aim to establish an adaptive monitoring model for the machining process.