Multi-objective selection of cutting conditions in advanced machining processes via an efficient global optimization approach

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Proceedings titleProceedings of the ASME Design Engineering Technical Conference
ConferenceASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2014, 17 August 2014 through 20 August 2014
SubjectAluminum alloys; Diamond drilling; Global optimization; Interpolation; Machining centers; Optimization; Pareto principle; Principal component analysis; Diamond turning; Efficient global optimization; High speed machining; Kriging; Principle component analysis; Multiobjective optimization
AbstractOptimum selection of cutting conditions in high-speed and ultra-precision machining processes often poses a challenging task due to several reasons; such as the need for costly experimental setup and the limitation on the number of experiments that can be performed before tool degradation starts becoming a source of noise in the readings. Moreover, oftentimes there are 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 (highdimension Pareto space) makes the generation and interpretation of Pareto solutions difficult. The approach adopted in this paper is a modified multi-objective efficient global optimization (m-EGO). In m-EGO, sample data points from experiments are used to construct Kriging meta-models, which act as predictors for the performance objectives. Evolutionary multi-objective optimization is then conducted to spread a population of new candidate experiments towards the zones of search space that are predicted by the Kriging models to have favorable performance, as well as zones that are underexplored. New experiments are then used to update the Kriging models, and the process is repeated until termination criteria are met. Handling a large number of objectives is improved via a special selection operator based on principle component analysis (PCA) within the evolutionary optimization. PCA is used to automatically detect correlations among objectives and perform the selection within a reduced space in order to achieve a better distribution of experimental sample points on the Pareto frontier. Case studies show favorable results in ultraprecision diamond turning of Aluminum alloy as well as highspeed drilling of woven composites.
Publication date
PublisherAmerican Society of Mechanical Engineers
AffiliationNational Research Council Canada; Aerospace
Peer reviewedYes
NPARC number21275585
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Record identifier0400e48b-3821-4a7a-ad3a-e67fc4ce142d
Record created2015-07-14
Record modified2016-05-09
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