# Development of an On-Line Defect Detection System for EDM Process
## 1 Introduction
[6] proposed a model-engraving electrical discharge machining monitoring to achieve zero-defect manufacturing. To find out the correlation between processing parameters and inappropriate processing conditions, the model was established with eight highly correlated parameters and monitored with a sampling interval of 32 milliseconds. [PERSON] et al. [7] used a sensor to collect voltage and current signals at a high sampling rate to find ten most relevant features of electrical discharge machining. However, due to the long processing time and the huge amount of data, [PERSON] et al. [8] proposed to use data mining algorithms to solve the feature dimension problem. They reduced the dimension through feature selection models and supervised learning methods, using a self-paced regularizer and \(\ell_{2,1}\)-norm control model. Proposed to study the sensors and EDM and established a linear regression model with current and pulse time to predict the surface roughness of the workpiece, by collecting its voltage and current signals, through effective wave extraction, feature calculation, feature matching, and selection of important features and established a model to estimate machining accuracy [9; 10; 11]. In the EDM process, [PERSON] (1974) proposed that the heat load in the processing was the main factor. The process was affected by heat, causing the temperature to exceed the melting point and even evaporate. Therefore, the edge area of the workpiece corroded by sparks was divided into a solidified layer and a heat-affected zone. (HAZ) and residued form a stress zone (RSZ) on the workpiece. The thickness of this layer depended on the processing parameters [12]. Anomaly detection is an important topic in many fields, and there are many different solutions. [PERSON] [13] applied statistics and neural-like machine learning methods, which provided a wide range of sample techniques, and proposed three basic principles of clustering methods (clustering approach), classification approaches (classification approach) and novelty approaches (novelty approach). The timing of use depended on the type of data to determine whether to pre-mark the data so that abnormal values were found to process the data. [PERSON] [14] et al. 's novelty detection system based on neural network method was compared to the feature extraction of the data set. The algorithm had equivalent accuracy. Aiming at the method of anomaly detection, [PERSON] et al. [15] analyzed arc welding. First, the pre-processing of the arc spectrum was performed on 50 features, and then a measurement index was proposed based on the mean accuracy. Finally, six features were selected to establish an anomaly recognition model based on random forest [16; 17]. [PERSON] [18] proposed the limit gradient enhancement method (XGBoost), which was a scalable enhancement system. Through additive training, the current model was retained for each training and a new function is added to the model. Enhancement of the basis was beneficial to the improvement of the objective function. Figure 2: Bumps during EDM processing (by the authors). Characteristics of electrical discharge machining: as the phenomena changes in electrical discharge machining are affected by the relationship between voltage and current [1], the machining accuracy can be estimated with this index [2; 11]. Previous research analysis steps and methods [11; 12] found the key trend of abnormal electrical discharge machining. Suitable for single-machine monitoring architecture: due to the difference in processing characteristics between machines, the threshold or model needs to be adjusted. Otherwise it is only suitable for a single machine [12]. After finding the key features, the multiple of steps to monitoring is more convenient and can be presented instantly. Anomaly detection method: the main reasons for the abnormality of electrical discharge machining are thermal influence and processing parameters [14], the application of statistical methods [15] to detection, even similar neural networks, random forests [16], and XGB-RF.