Stage identification and process optimization for fast drilling EDM of film cooling holes using KBSI method

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Stage identification and process optimization for fast drilling EDM of film cooling holes using KBSI method

Jian Wang , Xue-Cheng Xi , Ya-Ou Zhang , Fu-Chun Zhao , Wan-Sheng Zhao

Advances in Manufacturing ›› 2023, Vol. 11 ›› Issue (3) : 477 -491.

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Advances in Manufacturing ›› 2023, Vol. 11 ›› Issue (3) : 477 -491. DOI: 10.1007/s40436-022-00434-w

Article

Stage identification and process optimization for fast drilling EDM of film cooling holes using KBSI method

Jian Wang ¹^,²
, Xue-Cheng Xi ¹^,²
, Ya-Ou Zhang ¹^,²^,^c
, Fu-Chun Zhao ¹^,²
, Wan-Sheng Zhao ¹^,²

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¹ State Key Laboratory of Mechanical System and Vibration, 200240, Shanghai, People’s Republic of China

² School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, People’s Republic of China

^c yaou_zhang@sjtu.edu.cn

Jian Wang is currently pursuing a doctoral degree in the Discipline of Mechanical Engineering at School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. He has completed a B.S degree in Aviation Manufacturing Engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China. His areas of interests are electrical discharging machining, process control, and artificial intelligence in manufacturing.

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Xue-Cheng Xi is currently working as an Assistant Professor in the Discipline of Mechanical Engineering at School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. He received the M.S. and Ph.D. degrees in Automatic Control and Mechatronics from the National University of Singapore, Singapore, in 2003 and 2008. His areas of interests are electrical discharging machining, process and motion control, CNC systems, and artificial intelligence in manufacturing. He has contributed to more than forty publications in international journals/conference proceedings.

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Ya-Ou Zhang is currently working as an Assistant Professor in the Discipline of Mechanical Engineering at School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. He received the M.S. degree from Nanjing University of Aeronautics and Astronautics in 2003 and Ph.D. degree from Shanghai Jiao Tong University in 2008. His areas of interests are electrical discharging machining, specialized robot, and smart manufacturing.

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Fu-Chun Zhao is currently working as a research manager in the Discipline of Mechanical Engineering at School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. He received the B.S. degree from Harbin Institute of Technology in 2010 and M.S. degree from Shanghai Jiao Tong University in 2013. His areas of interests are electrical discharging machining, industrial control system, and EtherCAT protocol.

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Wan-Sheng Zhao is currently working as a Professor in the Discipline of Mechanical Engineering at School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. He received the B.S, M.S. and Ph.D. degrees in Mechanical Engineering from Harbin Institute of Technology in 1982, 1984, and 1989. His research interests includes electrical discharging machining, CNC systems for EDM machines towards smart manufacturing, and artificial intelligence in manufacturing.

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Abstract

Fast drilling electrical discharge machining (EDM) is widely used in the manufacture of film cooling holes of turbine blades. However, due to the various hole orientations and severe electrode wear, it is relatively intricate to accurately and timely identify the critical moments such as breakout, hole completion in the drilling process, and adjust the machining strategy properly. Existing breakout detection and hole completion determination methods are not suitable for the high-efficiency and fully automatic production of film cooling holes, for they almost all depend on preset thresholds or training data and become less appropriate when machining condition changes. As the breakout and hole completion detection problems can be abstracted to an online stage identification problem, in this paper, a kurtosis-based stage identification (KBSI) method, which uses a novel normalized kurtosis to denote the recent changing trends of gap voltage signals, is developed for online stage identification. The identification accuracy and generalization ability of the KBSI method have been verified in various machining conditions. To improve the overall machining efficiency, the influence of servo control parameters on machining efficiency of each machining stage was analyzed experimentally, and a new stage-wise adaptive control strategy was then proposed to dynamically adjust the servo control parameters according to the online identification results. The performance of the new strategy is evaluated by drilling film cooling holes at different hole orientations. Experimental results show that with the new control strategy, machining efficiency and the machining quality can be significantly improved.

Keywords

Fast drilling electrical discharge machining (EDM) / Film cooling holes / Breakout detection / Hole completion determination / Stage identification / Process optimization

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Jian Wang, Xue-Cheng Xi, Ya-Ou Zhang, Fu-Chun Zhao, Wan-Sheng Zhao. Stage identification and process optimization for fast drilling EDM of film cooling holes using KBSI method. Advances in Manufacturing, 2023, 11(3): 477-491 DOI:10.1007/s40436-022-00434-w

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Funding

National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809(52175426)

National Science and Technology Major Projects of China(2018ZX04005001)

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