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Reliability-based EDM process parameter optimization using kriging model and sequential sampling

1 Henan Key Laboratory of Mechanical Equipment Intelligent Manufacturing, School of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou, MO 450002, China
2 College of Mathematics and Information Science, Zhengzhou University of Light Industry, Zhengzhou, MO 450002, China
3 China Railway Engineering Equipment Group Co., Ltd, Zhengzhou, MO 450002, China
4 Luoyang TiHot Railway Machinery Manufacturing Co., Ltd, Luoyang, MO 471002, China

Special Issues: Optimization methods in Intelligent Manufacturing

Electrical discharge machining (EDM) is an effective method to process micro-hole for electrically conductive materials regardless of the hardness. However, the machining accuracy and cost are greatly affected by EDM parameters, which are of slight fluctuations in actual machining process. In view of this, reliability-based design optimization (RBDO) method is introduced to balance the electrode wear and aperture gap when unavoidable uncertainties are considered. Kriging model trained by inherited Latin hypercube design (ILHD) and expected feasibility function with objective function (OEFF) criterion is applied to model the influences of peak current, pulse on time and pulse off time on electrode wear and aperture gap. By calling the Kriging model, the probability and corresponding gradient of aperture gap less than the requirement are calculated using Monte Carlo simulation (MCS) and the EDM process parameters are optimized using sequential approximation programming (SAP) algorithm. Using the optimal EDM parameters to perform verification experiments, the feasibility of proposed method is demonstrated, where smaller electrode wear as low as 174.2 µm is obtained with the reliability satisfaction ( β=3.02) of aperture gap.
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Keywords EDM; reliability-based design optimization; Kriging; sequential sampling

Citation: Ma Jun, Han Xinyu, Xu Qian, Chen Shiyou, Zhao Wenbo, Li Xiaoke. Reliability-based EDM process parameter optimization using kriging model and sequential sampling. Mathematical Biosciences and Engineering, 2019, 16(6): 7421-7432. doi: 10.3934/mbe.2019371

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