# Research on Process Quality Prediction and Control of Spindle Housings in Flexible Production Lines
## 6 Conclusions  
The high degree of automation in the manufacturing process of a flexible production line can easily cause product quality to be reduced due to uncontrolled process quality. In addition, flexible production lines have variations of \"multiple steps and few processes\", and the process quality needs to consider the comprehensive influence of many factors. There is no predictive control of the process quality. Therefore, in this study, we investigated variations in quality characteristics of key process features in a flexible production line and the control method, to determine a model that could effectively predict variations in process quality characteristics. In this study, we used a spindle box flexible production line as the research object and studied process quality control. A decision analysis model of process quality influencing factors, a combined prediction model of process quality characteristic values, and a process quality monitoring and capability assessment model of a spindle box flexible production line was established. The research findings of the paper are as follows:  
1. A decision analysis model of process quality influencing factors of a spindle box flexible production line is established, and process quality control decisions are formulated. Through a comprehensive analysis of the process quality influencing factors, a hierarchical model of the spindle box process quality influencing factors is established, combined with the \"5M1E\" analysis method and fuzzy hierarchical analysis method, to analyze and make decisions on the influence of multiple factors. The fuzzy weight ranking value of each influencing factor is used to improve the process quality control decision of the spindle box flexible production line. Figure 12: Spindle bore diameter error control: (**a**) Actual \(\overline{X}-R\) control chart; (**b**) predicted \(\overline{X}-R\) control chart; (**c**) actual EWMA control chart; (**d**) predicted EWMA control chart. Figure 13: Production line process quality monitoring platform interface. 2. A combined prediction model of process quality characteristic values is established. The dynamic GM(1,1) predicts the fundamental process quality characteristic values and obtains the potential change trend of process quality. In order to avoid prediction failure due to large residual values, a BP neural network is used to correct the residuals of the predicted values. A model is obtained with predicted data close to the actual data variation. The average relative error is improved by 61% compared with dynamic GM(1,1), effectively reflecting the trend change of process quality characteristic values. 3. A process quality monitoring and process capability assessment model is established for a spindle box flexible production line of spindle cases. Using the principle of statistical process control (SPC), the process quality control process of a spindle box flexible production line is developed, and multiple control charts are used to jointly determine whether there is an uncontrolled state or trend in process quality. The actual and predicted values of the process quality characteristics are analyzed by combining multiple control charts. Based on the actual data and predicted changes in quality characteristics, we use time weighted EWAM control charts in conjunction with average and extreme difference control charts to determine the production line's current process quality status and to analyze the production line's quality problems. The process quality is again monitored for potential out-of-control trends based on predicted data using multiple control charts jointly. Two points should be noted for future research. First, the quality control of the spindle housing of a flexible production line is easily affected by several factors.