The purpose of this study is trying to apply VM  for measuring machining precision of machine tools. As shown in the left portion of Fig. 1, the AV M server  needs process data, which contain sensor data and machining parameters, as inputs for predicting machining accuracies. However, most machine tools do not possess sensors for providing raw process data such as vibration, current, etc. Furthermore, machining operations usually possess severe vibrations and loud noises. It makes the raw data collected from sensors attached to machine tools with low signal-to-noise (S/N) ratios, thereby affecting the prediction accuracy of VM. Even though the AVM has been an effective way to conduct workpiece measurements in high-tech industries , there exist challenges when applying the AVM to machining industry. Besides embedding essential sensors on the machine tool effectively, the challenges are 1) Segmentation: to accurately segment essential part of the raw process data from the original NC file, 2) Data Cleaning: to effectively handle raw process/sensor data with low S/N ratios, and 3) Feature Extraction: to properly extract significant features from the segmented raw process data. This work proposes a novel GED-plus-AVM (GAVM) system as depicted in Fig.1 to resolve all the challenges mentioned above. These challenges are judiciously addressed and successfully resolved in this paper.