# Development of an On-Line Defect Detection System for EDM Process
## 3 Case Study  
### Experimental Results of Feature Calculation  
This diagnostic recording experiments on the actual launch processing machine will be conducted. Aiming at the high points caused by uneven distribution of diamond particles, the processing experiments and analysis are conducted.  
After calculating the seven features, the coefficient of variation of each feature per minute is calculated. Taking 26 min (1-25 min) before abnormal processing as the normal value and 23 min (40-63 min) after processing as interval 2, interval 1 and interval 2 are converted into probability, and the KLD value of the two intervals is calculated. It has occurred, and the time when it appears with probability is not obvious. In a period of time, the second period is significantly shorter. KLD increases by 0.209. The abnormal method can determine the normal and processing trends shown in Figure 4.  
Figure 4: APDC feature calculation and analysis results.  
In the average spark frequency (ASF), the KLD value of interval 1 is 3.097, and the KLD value of interval 2 is 4.236. In this feature, the KLD value of both intervals is greater than 3, indicating that there is a clear trend of positive and abnormal machining in two intervals, especially in interval 2, the distribution of normal processing is almost concentrated in the range of 0.05 to 0.15, while the distribution of abnormal processing is in the range of 0.2 to 0.4, shown in Figure 5.  
In the average energy (ADE), the KLD value of interval 1 is 4.128 and the KLD value of interval 2 is 1.476. This feature is as high as 4 in interval one, indicating that the coefficient of variation of normal processing energy is relatively concentrated (0.002-0.004), while abnormal processing energy variation coefficient is relatively scattered (0.004-0.016), which makes the distance elongated. The KLD value is therefore very large, meaning that normal and abnormal processing trends can be observed in the first interval, shown in Figure 6.  
Figure 5: ASF feature calculation and analysis results.  
Figure 6: ADE feature calculation and analysis results.  
In the average discharge current pulse duration (ADCPD), the KLD value of interval 1 is 0.889. The KLD value of interval 2 is 0.864, and the KLD value of both intervals is less than 1. Although the difference can be seen in the distribution, the distribution value is only between 0.02 and 0.1, shown in Figure 7.  
In the average gap voltage (AGV), the KLD value of interval 1 is 2.617, the KLD value of interval 2 is 3.16, and the KLD values of both intervals are greater than 2.5, indicating that normal and abnormal processing can be distinguished, especially in interval 2, where the two distributions are clearly separated, shown in Figure 8.  
In the average gap voltage (AGV), the KLD value of the interval 1 is 0.124, the KLD value of the interval 2 is 1.419, and the KLD value of the interval 2 is 1.295 larger than that of the interval, shown in Figure 9.  
Figure 8: AGV feature calculation and analysis results.  
Figure 7: ADCPD feature calculation and analysis results.  
In the open circuit ratio (OCR), the KLD value of interval 1 is 2.844, the KLD value of interval 2 is 1554, the distribution of abnormal processing in interval 1 is more concentrated in 0-0.4, and the distribution of normal processing in interval 2 is more concentrated in 0-0.5. This means that the two processing abnormal behaviors are different, although they can still be classified, shown in Figure 10.