## 1 Introduction  
As the fast development of aerospace industry, the look for efficient machining methods of low pressure turbine (LPT) components becomes more and more important. During the whole process of LPT machining, as the complicate geometry and requirement of high accuracy [1], fir-tree slot (Figure 1a) is one of the most difficult part for machining. Due to the high accuracy, low cost, high flexibility and some other advantages [2], Wire electrical discharge machining (WEDM) has gradually become a better choice for machining fir-tree slot than broaching in application of aeronautic industry [3]. The defect detection is a crucial link in WEDM machining. Today, tolerance defect detecting is almost done by coordinate measuring machine (Figure 1b) after the end of machining. Through the comparison between the ideal model and the actual machining result, the direction of improvement of processing parameter can be obtained. This kind of measuring method is quite complicated and time costing because of the moving of the workpiece from WEDM machine to measuring machine every time. Similarly, the ideal model needs to be created every time when a new profile or cutting task is required. Therefore, some improvement of defect detecting method should be explored.  
Deep Learning (DL) is an emerging field of machine learning research that aims to further realize Artificial Intelligence. The concept of deep learning stems from the study of artificial neural networks. A multi-layered sensor with multiple hidden layers constitutes a deep learning network. Similarly, deep learning discovers distributed representations of data by combining low-level features to create more abstract high-level representation attribute categories or features [4]. Through the constant exploration of about 60 years, [PERSON] et al. proposed formally the concept of deep learning in 2006. Through an unsupervised learning method they trained the algorithm layer by layer and then used a supervised back propagation algorithm for tuning. It brings hope to solve the deep structure related optimization problem [5]. Further in 2012, [PERSON] et al. successfully used the Relu activation function to fundamentally solve the problem of gradient disappearance, and adopted the GPU to greatly improve the speed of the model. Nowadays, with the appearance of more advanced calculating equipment and more efficient method of recording data, the application of deep learning in more and more fields become possible, such as image classification, speech recognition, fact extraction and so on [6; 7; 8]. Deep learning has previously been used by the authors for unexpected event prediction in Wire EDM operations [9]. However, to the best knowledge of the authors, no research work can be found in literature dealing with the use of deep learning for defect detection in WEDM machining.  
For finding easier and more efficient defect detecting method, a novel way which adopts deep learning is proposed in this paper. In a previous research work, [PERSON] et al. [10] explained that unsupervised machine learning can be successfully used to extract useful information from ignition delay time. This variable can be measured under industrial conditions during actual machining from the WEDM machine.  
In this paper, a further step is presented. Following Big Data trends, and taking into account that very large amounts of data can be on-line collected during machining, a deep learning neural network approach is proposed for defect detection during the Wire EDM machining of fir-tree slots for aerospace industry. After the Introduction in Section 1, the structure of the network and the definition of the machining experiments required for training the network is described in Section 2. Section 3 presents the main findings of the work, which include an excellent agreement between the conventional and the new proposed method. Finally, the main conclusions of the work are summarized.  
Figure 1: (**a**) Geometry of the fir-tree slot (**b**) Coordinate Measuring Machine (CMM).