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New feed rate optimization formulation in a parametric domain for 5-axis milling robots

New feed rate optimization formulation in a parametric domain for 5-axis milling robots

Chu, Anh My, Duong, Xuan Bien, Bui, Hoang Tung, Nguyen, Van Cong and Le, Chi Hieu ORCID: 0000-0002-5168-2297 (2019) New feed rate optimization formulation in a parametric domain for 5-axis milling robots. In: ICCSAMA 2019: Advanced Computational Methods for Knowledge Engineering. Advances in Intelligent Systems and Computing, 1121 . Springer, Cham, Switzerland, pp. 403-411. ISBN 978-3030383640 ISSN 2194-5357 (Print), 2194-5365 (Online) (doi:https://doi.org/10.1007/978-3-030-38364-0_36)

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Abstract

When producing a numerical control (NC) program for a 5-axis CNC machine (the so-called milling robot) to mill a sculptural surface, a constant feed rate value is usually assigned based on programmer’s experiences. For this reason, the feed rate in most of NC programs is often not optimized, it is much lower than maximum reachable value. To increase the productivity of the machining process, the feed rate in NC programs for the milling robots need to be maximized. This paper proposes a new feed rate optimization model, of which the objective function and all the kinematic constraints are transformed and expressed explicitly in a parametric domain which is commonly used in the tool path generation process performed by current CAM systems. Thus, the optimal feed rate values along a parametric tool path can be computed in an effective and simplified manner. Numerical examples demonstrate the effectiveness of the proposed method.

Item Type: Conference Proceedings
Title of Proceedings: ICCSAMA 2019: Advanced Computational Methods for Knowledge Engineering
Uncontrolled Keywords: high speed milling, 5-axis milling robot, kinematic modelling, feed rate interpolation
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Design, Manufacturing and Innovative Products Research Theme
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 22 Jan 2020 11:43
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/26695

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