## 4 Automatic Virtual Metrology Control Method  
The AVM method and operational process data are shown in Figure 2. The method process data are adjusted through the AVM method, and then used to build various VM methods. The data used are from the newly manufactured screw process parameter prediction method. The operating principle of the AVM method mainly includes the RPE check of the operation sample, along with the grouping of method samples (process data) and adjustment of their target values. A set of deterministic samples are defined in AVM, which are then extracted from injection molding machine parameters and used to automatically calculate the target values. The required sample size must be defined, as it is used it to determine the number of conversions of the AVM method; experts recommend about 3-5 per group. The target value (TV) of the operational sample is determined by the average measurement value, as shown in Formula (1), where \(y_{j}\) represents the actual measurement value of the j number of elements, and n = the size of the TV [3].  
The AVM system converts sampling inspections with measurement delays into real-time and online total inspections, while the TVA method aims to strengthen the adaptive customization function of the AVM to accomplish the MC production goals automatically and quickly which are shown in Figure 3. To meet the requirements of MC production, this paper proposes to apply the AVM system together with the target value method.  
\[\text{Target value}=\ yDS=\frac{\sum_{1}^{n}yj}{n} \tag{1}\]  
Figure 3: Operating scenarios of method and running data of the AVM method.  
It is necessary to reconfigure this to ensure the measurement accuracy of both the functions of the production tool and the compression ratio; the first few tests after the polymer material is changed might be unstable until measurement accuracy is achieved.  
To ensure the measurement quality after operations, the AVM system first conducts a quality inspection that is used to calculate the incremental difference (\(\delta\)) between adjacent samples; this inspection determines whether \(\delta\) is greater than the value of \(\delta(\delta_{\text{T}})\), where \(\delta_{\text{T}}\) is calculated using the specification tolerance and the tolerance ratio (\(\alpha\)), defined in Formula (2).  
\[\delta_{\text{T}}=\alpha*\left|\text{UCL}_{\text{New Type}}-\text{LCL}_{\text{ New Type}}\right| \tag{2}\]  
The default specification includes the upper control limit (UCL) and lower control limit (LCL) that can be set with new samples. After the new measurement sample is checked, it will be added to the deterministic sample. It takes a lot of time and cost to collect a sufficient number of specific types of samples to build a VM method; it is possible to create a method by using multiple types of samples, thus reducing the time required for the MC process. However, the problem of insufficient causality between measurement values and different types of samples must be resolved. To distinguish different types of samples with different TVs, it is necessary to group the method samples. As shown on the left side of Figure 2, the method sample contains three types of metrics with different TVs. They can be sorted into ascending order by applying Formula (3) to obtain \(y_{\text{M}}\), where \(y_{\text{i}}\) represents is the i number of elements of the method sample, m = SIZE \(y_{\text{M}}\) = the size of the method sample.  
\[y_{\text{M}}=\begin{bmatrix}\text{sort}(\text{Model }y_{\text{i}})_{\text{ Ascending}}\\ \text{i }=1,\,2,\,\ldots,\,\text{m}\end{bmatrix} \tag{3}\]