Multiple-parameter optimization for CNC machining via machine learning

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Proceedings titleInternational Conference on Manufacturing Automation: Advanced Design and Manufacturing in Global Competition
ConferenceInternational Conference on Manufacturing Automation, October 26-29 2004, Wuhan, Hubei, China
Pages331338; # of pages: 8
SubjectCAD/CAM; CNC machining; machining performance; multiple-parameter optimization; machine learning
AbstractComputed-aided design / computer-aided manufacturing (CAD/CAM) and computerized numerical control (CNC) machining are among the most efficient and commonly used processes by the manufacturing industry. Extensive research and development has been conducted and continuously ongoing in this important area to improve quality of the machined part and to reduce the cycle time. The research generated extensive knowledge on machining and often focused on specific goal and objective. A recent development of an intelligent process planning system for CNC programming identified the need of multiple-parameter optimization for controlling different factors of CNC machining such as feeds, speeds, tools sizes, etc. in order to achieve a good surface finishing, low tool load, fast cycle time and other machining goals. This paper describes a method based on the machine learning approach for the optimization of multiple-parameter CNC machining.
Publication date
AffiliationNRC Industrial Materials Institute; National Research Council Canada
Peer reviewedYes
NPARC number21272548
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Record identifierae7fe23a-7f15-4dfc-8f81-187494c65b30
Record created2014-12-02
Record modified2016-05-09
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