# Research on Process Quality Prediction and Control of Spindle Housings in Flexible Production Lines
## 5 Process Quality Monitoring and Evaluation of Flexible Production Line for Spindle Housing
### Spindle Box Process Quality Case Analysis and Verification  
Take the case of a spindle box flexible manufacturing line for the processing of the spindle hole as an example; analysis of the processing quality requirements for the diameter of \(\varphi\) precision boring machining of the spindle hole, a micrometer measuring tool to obtain the data of the spindle hole, as shown in Table 15. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Serial** & **Measured** & **Deviation Conversion** & **Serial** & **Measured** & **Deviation Conversion** \\
**Number** & **Value/mm** & **Value/mm** & **Number** & **Value/mm** & **Value/mm** \\ \hline \multirow{3}{*}{1} & 150.017 & 0.017 & & 150.021 & 0.021 \\  & 150.015 & 0.020 & 13 & 150.022 & 0.022 \\  & 150.021 & 0.021 & & 150.027 & 0.027 \\ \hline \multirow{3}{*}{2} & 150.018 & 0.018 & & 150.027 & 0.027 \\  & 150.024 & 0.024 & 14 & 150.016 & 0.016 \\  & 150.028 & 0.021 & & 150.022 & 0.022 \\ \hline \multirow{3}{*}{3} & 150.019 & 0.019 & & 150.019 & 0.019 \\  & 150.016 & 0.016 & 15 & 150.025 & 0.025 \\  & 150.021 & 0.021 & & 150.032 & 0.032 \\ \hline \multirow{3}{*}{4} & 150.022 & 0.022 & & 150.023 & 0.023 \\  & 150.018 & 0.018 & 16 & 150.029 & 0.029 \\  & 150.025 & 0.025 & & 150.030 & 0.030 \\ \hline \multirow{3}{*}{5} & 150.024 & 0.024 & & 150.021 & 0.021 \\  & 150.028 & 0.028 & 17 & 150.015 & 0.015 \\  & 150.018 & 0.018 & & 150.017 & 0.017 \\ \hline \multirow{3}{*}{6} & 150.023 & 0.023 & & 150.024 & 0.024 \\  & 150.015 & 0.015 & 18 & 150.021 & 0.021 \\  & 150.019 & 0.019 & & 150.023 & 0.023 \\ \hline \multirow{3}{*}{7} & 150.022 & 0.022 & & 150.028 & 0.028 \\  & 150.024 & 0.024 & 19 & 150.019 & 0.019 \\  & 150.031 & 0.021 & & 150.021 & 0.021 \\ \hline \multirow{3}{*}{8} & 150.020 & 0.020 & & 150.031 & 0.031 \\  & 150.027 & 0.027 & 20 & 150.033 & 0.033 \\  & 150.016 & 0.016 & & 150.027 & 0.027 \\ \hline \multirow{3}{*}{9} & 150.026 & 0.026 & & 150.023 & 0.023 \\  & 150.027 & 0.027 & 21 & 150.023 & 0.023 \\ \cline{1-1}  & 150.030 & 0.030 & & 150.025 & 0.025 \\ \hline \end{tabular}
\end{table} 
Table 15: Spindle bore measurement data. In order to further predict potential process quality problems in the current production line, the characteristic quality values in Table 15 are predicted and simulated using the GM(1,1)-BP combined prediction model based on machining characteristic quality spindle bore data, the actual data and the predicted data are analyzed for statistical process control, and the corresponding control charts are drawn from the actual and predicted samples of the process quality data, as shown in Figure 12.  
As can be seen from Figure 12 in Figure 12a the actual sample, in the control chart, there is an abnormal sample point outside the control range, that is, it predicts that in the production process there is an out-of-control quality trend. However, at this time, Figure 12b shows the EWAM control chart without the appearance of abnormal samples, which predicts that, with the weighted passage of time, the quality of production processing does not show the some out-of-control quality trend, that is, there is no severe loss of control as the process progresses. At this point, the process quality prediction analysis can be performed for out-of-control samples to ensure the product's quality and the production task's regular operation. In Figure 12d, the EWMA control chart of the second and third data samples appears to be out of control, but in the predicted data samples of the Figure 12c control chart, all the data samples are within the control range, and with the weighted passage of time, the data samples in the control range are increasing, indicating that, as the processing process of the production line progresses, processing quality could have a specific adjustment, i.e., with an increase in the time of processing there is a risk of out-of-control quality. The staff should pay attention to processing quality changes in the production line.