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contributor authorXu, Ke
contributor authorTang, Kai
date accessioned2017-11-25T07:17:42Z
date available2017-11-25T07:17:42Z
date copyright2016/10/11
date issued2017
identifier issn1087-1357
identifier othermanu_139_05_051003.pdf
identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4234740
description abstractEnergy consumption in five-axis machining of freeform surfaces can be considerably large for large-size parts. This paper presents a study on how to setup the workpiece in order to minimize the energy consumption without modifying the toolpath itself. For an arbitrary freeform workpiece, the way how it is setup on the working table highly affects the machine's kinematic behavior, which dominates the overall processing time and energy consumption. Taking into account the speed and acceleration limit of each axis of the machine, we first establish the energy consumption model as a function of the workpiece setup. However, this original model involves certain critical physically pertinent coefficients (such as the moment of inertial of a rotary table) which are usually unavailable in practice. Instead, by exploring insightful geometric characteristics of the five-axis machine, an alternative energy consumption model is established which is independent of those hard-to-obtain coefficients. A simple algorithm is then designed to optimize this model. Both computer simulations and physical cutting experiments demonstrate that, when compared with an arbitrary setup, the optimized workpiece setup is able to achieve a significant saving (as much as 50%) in both energy consumption and total machining time, both using a same tool path.
publisherThe American Society of Mechanical Engineers (ASME)
titleOptimal Workpiece Setup for Time-Efficient and Energy-Saving Five-Axis Machining of Freeform Surfaces
typeJournal Paper
journal volume139
journal issue5
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4034846
journal fristpage51003
journal lastpage051003-16
treeJournal of Manufacturing Science and Engineering:;2017:;volume( 139 ):;issue: 005
contenttypeFulltext


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