Geometrical Defect Detection in the Wire Electrical Discharge Machining of Fir-Tree Slots Using Deep Learning Techniques

• Main Authors: Jun Wang, Jose A. Sanchez, Jon A. Iturrioz, Izaro Ayesta
• Format: Article
• Language: English
• Published: MDPI AG 2018-12-01
• Series: Applied Sciences
• Subjects: defect detection; traceability; aerospace; wire electrical discharge machining; artificial intelligence; deep learning
• Online Access: http://www.mdpi.com/2076-3417/9/1/90

Traceability is a critical issue in the manufacturing of aerospace components. However, extracting understandable information from huge amounts of data from manufacturing processes may become a very difficult task. In this paper, a novel proposal for geometrical defect detection in the manufacturing of fir-tree slots for disk turbines using wire electrical discharge machining is presented. Useful data about the wire Electrical Discharge Machining (WEDM) process are collected every 5 ms and each single discharge is classified as a function of ignition delay time. Information from this large amount of data is extracted by using a deep neural network, which includes two hidden dense layers, each with 64 units and Relu activation, and it ends with a single unit with no activation. The average of the per-epoch absolute error (MAE) scores has been used to decide the optimum training situation for the deep learning network. Validation of the method has been carried out by machining a high-precision fir-tree slot for a disk turbine under industrial conditions. Results show that even though a strict tolerance band of ±5 µm has been applied, as many as 80% of the predictions from the network match the results of the conventional measuring method (coordinate measuring machine).