# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
## 4 Prediction of Linear Guide Preload--ANN Modeling
### Application Scenario of ANN Model  
Currently, this study has demonstrated that (1) the vibration characteristics of the feeding stage are affected by the linear guide preload with a significant correlation between them, and (2) the variation in the vibration level of the stage in motion is closely related to the change in the linear guide preload. With these phenomena, ANN models for predicting the preload state of the linear guide were developed, which were also shown to achieve good prediction accuracy based on the vibration signals measured on the stage.  
The application of the preload predictive model can be schematically illustrated by means of a three-axis milling machine, which is initially equipped with four high preload linear guides (Z2). As shown in Figure 17, a human-machine interface with a simple graphical user interface (GUI) display combining the ANN prediction method is proposed in this study. Using this figure, the scenarios of preload degradation of linear guides in three-axis feeding mechanisms can be illustrated as follows:  
1. Scenario 1, a normal preload condition is predicted for the X axis linear guide with green signs. The ANN models predict the rigidity at the value of 2020 N/um without preload decay in a short-term operation.
2. Scenario 2, a moderate preload degradation of the Y axis linear guide is predicted based on the measured vibration features. In this case, the linear guide is preloaded at the medium level (Z1), as in case (II), which is also considered to have a preload loss of 18% from the highest preload stage with a yellow sign.
3. Scenario 3, a severe preload degradation of the Z axis linear guide is predicted based on the measured vibration features, with a red sign. In this case, the linear guide is preloaded at a low level (Z0), as in case (III), which can be caused by the surface wear of the rolling elements or ball grooves. The decrease in the interference at the rolling interface reduces the preload by 30% or 42% from the initial medium (Z1) or the highest preload state (Z2).  
Prior to the implementation of this application, a method for monitoring the stage rigidity or preload from a linear guide was established. However, to be put in the real world, a user-friendly and interactive display of the ANN output value needs to be designed for the end user, as shown in Figure 17. The workflow for processing the data is shown in Figure 18. The entire process begins with the acquisition of vibration data from the physical system. Subsequently, the signal is processed until the feature can be extracted from the raw signal. This feature extraction is a step toward identifying the condition indicator block before the prediction model is built. After training the model, the ANN is deployed and tested for a new dataset from the user. The new dataset output prediction is integrated with the GUI from the SQL server, as shown in Figure 16. The display user easily interprets the output prediction of the current preload that represents the condition or health status of the linear guide in the system. The interactive layout with the color given in the half-doughnut indicator shows a warning or message to the user, indicating whether the linear guide in the system needs to be replaced.  
Figure 16: Scatters of vibration level against feed rate for stages with different preloads.  
However, currently the monitoring system is still under development. It is expected that in the near future, the workflow will be automated, thus providing more convenience for the user to obtain the actual rigidity or health status of the linear guide. This is possible because the GUI can be connected to a real-time up-to-date dataset in the server.