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Multi-Objective Selection of Cutting Conditions in Advanced Machining Processes via an Efficient Global Optimization Approach

[+] Author Affiliations
Mohamed Aly, Ashraf O. Nassef

American University in Cairo, Cairo, Egypt

Karim Hamza

University of Michigan, Ann Arbor, MI

Mohammed Tauhiduzzaman, Stephen Veldhuis

McMaster University, Hamilton, ON, Canada

Mouhab Meshreki

National Research Council of Canada, Ottawa, ON, Canada

Helmi Attia

National Research Council of Canada, Ottawa, ON, CanadaMcGill University, Montreal, QC, Canada

Paper No. DETC2014-34624, pp. V02AT03A003; 8 pages
doi:10.1115/DETC2014-34624
From:
  • ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2A: 40th Design Automation Conference
  • Buffalo, New York, USA, August 17–20, 2014
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4631-5
  • Copyright © 2014 by ASME and Her Majesty the Queen in Right of Canada

abstract

Optimum 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 (high-dimension 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 under-explored. 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 ultra-precision diamond turning of Aluminum alloy as well as high-speed drilling of woven composites.

Copyright © 2014 by ASME and Her Majesty the Queen in Right of Canada

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