Break-out Detection for High-speed Small Hole Drilling EDM Based on Machine Learning

摘要

High-speed small hole drilling EDM is of the advantages of no cutting force, high machining accuracy and efficiency, therefore, it is very suitable for drilling multiple film cooling holes with variable tilting angles on the turbine blades which is made of high-temperature alloy. When machining through holes, however, the break-outs need to be accurately and timely detected in order to adjust the machining strategy to assure the quality of the drilled holes and prevent the back strike which may result in machining failure, hence the online break-out detection becomes very essential for the process itself, especially in a fully automated operation. This paper proposes a new method for break-out detection, which involves support vector machine (SVM) method, one of the effective machine learning algorithms. The inputs of the model include discharge pulse duration, pulse interval, peak current, effective discharge frequency and actual electrode feed rate, etc., and the output is whether the event of break-out is happening or not. The algorithm is seamlessly integrated in a newly developed computer numerical control (CNC) system, which is dedicated for High-speed small hole drilling EDM, with a sampling circuit collecting real-time current signals of electrical discharges. A series of experiments were carried out using the proposed method. Raw data were first acquired by machining through holes, then preprocessed offline to recognize effective discharge pulses from the pulse trains. The results along with the discharge parameters were used as the training data to build the SVM model for the subsequent online detection. Finally, the effectiveness of the proposed method was verified by drilling holes on workpieces with different thicknesses. The results show that the proposed method can detect the break-outs very effectively, with the correctness nearly 100% among 200 tests and the decision cycle time less than 100ms in current experimental conditions.