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contributor authorHan, Ce
contributor authorLuo, Ming
contributor authorZhang, Dinghua
contributor authorWu, Baohai
date accessioned2019-02-28T11:02:11Z
date available2019-02-28T11:02:11Z
date copyright10/5/2018 12:00:00 AM
date issued2018
identifier issn1087-1357
identifier othermanu_140_12_121009.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4251960
description abstractDue to the enclosed chip evacuation space in 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 optimization is proposed. First, the chip evacuation torque coefficients (CETCs) 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 (EDCbCR) 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%.
publisherThe American Society of Mechanical Engineers (ASME)
titleIterative Learning Method for Drilling Depth Optimization in Peck Deep-Hole Drilling
typeJournal Paper
journal volume140
journal issue12
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4041420
journal fristpage121009
journal lastpage121009-12
treeJournal of Manufacturing Science and Engineering:;2018:;volume( 140 ):;issue: 012
contenttypeFulltext


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