Optimization of cutting conditions in vibration assisted drilling of composites via a multi-objective EGO implementation

DOIResolve DOI: http://doi.org/10.1115/DETC2015-47532
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Proceedings titleVolume 2A: 41st Design Automation Conference
Series titleASME Proceedings
ConferenceASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, August 2-5, 2015, Boston, Massachusetts, USA
Article numberV02AT03A003
Subjectcomposite materials; drilling; optimization; vibration; cutting
AbstractA recent and promising technique to overcome the challenges of conventional drilling is vibration-assisted drilling (VAD) whereby a controlled harmonic motion is superimposed over the principal drilling feed motion in order to create an intermittent cutting state. Two additional variables other than the feed and the speed are introduced, namely the frequency and the amplitude of the imposed vibrations. Optimum selection of cutting conditions in VAD operations of composite materials is a challenging task due to several reasons; such as the increase in the number of controllable variables, the need for costly experimentation, and the limitation on the number of experiments that can be performed before tool degradation becomes an issue in the reliability of measurements. Additionally, there are often several objectives to consider, some of which may be conflicting, while others may be somewhat correlated. Pareto-optimality analysis is needed for conflicting objectives; however the existence of several objectives (high-dimension Pareto space) makes the generation and interpretation of Pareto solutions difficult. An attractive approach to the optimization task is thus to employ Kriging meta-models in a multi-objective efficient global optimization (m-EGO) framework for incremental experimentation of optimal setting of the cutting parameters. Additional challenge posed by constraints on machine capabilities is accounted for through domain transformation of the design variables prior to the construction of the Kriging models. Study results using a baseline exhaustive experimental data shows opportunity for employing m-EGO for the generation of well distributed Pareto-frontiers with fewer experiments.
Publication date
AffiliationAerospace; National Research Council Canada
Peer reviewedYes
NPARC number23001577
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Record identifier87838443-91d0-42a7-b83e-356493429dce
Record created2017-03-07
Record modified2017-03-07
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