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Automated cutter size and orientation determinations for multi-axis sculptured part milling


Automated cutter size and orientation determinations for multi-axis sculptured part milling

Liu, Gang (2007) Automated cutter size and orientation determinations for multi-axis sculptured part milling. Masters thesis, Concordia University.

Text (application/pdf)
MR34706.pdf - Accepted Version


In CNC machining of sculptured surface parts, using larger cutters can get better surface quality and higher productivity. However, the chance of gouging and interfering with the part surfaces during machining increases. In my thesis research, an innovative, generic method is proposed to determine optimal cutter sizes and orientations for 3- and 4-axis free-form surface end-milling without gouging and interference. The main research contribution is that, to cut a part surface at a cutter-contact point, the size and orientation of the optimal cutter, which is the minimum of all imaginary cutters that over-cut or collide with the part surfaces, are formulated with a global optimization problem. My work has proposed a hybrid global optimization method, which is a combination of the particle swarm optimization method and the local optimization method, and applied it to this optimization problem. Thus, the calculated optimal cutter is the largest tool to machine the cutter-contact point without gouging and interference. Based on the optimal cutters for all the cutter-contact points, a group of standard end-mills can be selected to be as large as possible for machining these part surfaces. To demonstrate the advantages of this new approach, two sculptured parts are adopted, one is a hair dryer mold with 24 free-form surface patches and the other is an axial-flow compressor. By applying this approach, a group of cutters and their orientations can be determined to achieve a high machining efficiency and ensure the surface accuracy and finish. The 3- and 4-axis machining for these parts are simulated with the CATIA V5 CAD/CAM system.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical and Industrial Engineering
Item Type:Thesis (Masters)
Authors:Liu, Gang
Pagination:x, 88 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Mechanical and Industrial Engineering
Thesis Supervisor(s):Chen, Zezhong C
ID Code:975312
Deposited By: Concordia University Library
Deposited On:22 Jan 2013 16:05
Last Modified:18 Jan 2018 17:40
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