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research-article

Iterative Learning Method for Drilling Depth Optimization in Peck Deep-Hole Drilling

[+] Author and Article Information
Ce Han

Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi'an 710072, P.R. China
hance@mail.nwpu.edu.cn

Ming Luo

Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi'an 710072, P.R. China
luoming@nwpu.edu.cn

Dinghua Zhang

Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi'an 710072, P.R. China
dhzhang@nwpu.edu.cn

Baohai Wu

Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi'an 710072, P.R. China
wubaohai@nwpu.edu.cn

1Corresponding author.

ASME doi:10.1115/1.4041420 History: Received February 27, 2018; Revised September 05, 2018

Abstract

Due to the enclosed chip evacuation space in the deep-hole drilling process, chips are accumulated in drill flutes as drilling depth increases, resulting in the increase of drilling torque and lead to drill breakage. Peck drilling is a widely used method to periodically alleviate the drilling torque caused by chip evacuation, the drilling depth in each step directly determines both drill life and machining efficiency. The existing drilling depth optimization methods face problems including low accuracy of the prediction model, the hysteresis of signal diagnosis, and onerous experiments. To overcome these problems, a novel drilling depth optimization method for peck drilling based on the iterative learning control is proposed. Firstly, the chip evacuation torque coefficients are introduced into the chip evacuation torque model to simplify the model for learning. Then, the effect of chip removal process in peck drilling on drilling depth is analyzed. The extended depth coefficient by chip removal is introduced to develop the relationship between the extended depth in each drilling step and drilling depth. On the foundation of the modeling above, an iterative learning method for drilling depth optimization in peck drilling is developed, in which a modified Newton's method is proposed to maximize machining efficiency and avoid drill breakage. In experiments with different cutting parameters, the effectiveness of the proposed method is validated by comparing the optimized and measured results. The results show that the presented learning method is able to obtain the maximum drilling depth accurately with the error less than 10%.

Copyright (c) 2018 by ASME
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