Research Papers

Multiobjective Optimization Under Uncertainty in Advanced Abrasive Machining Processes Via a Fuzzy-Evolutionary Approach

[+] Author and Article Information
Adel T. Abbas

Department of Mechanical Engineering,
King Saud University,
P.O. Box 800,
Riyadh 11421, Saudi Arabia
e-mail: aabbas@ksu.edu.sa

Mohamed Aly

Department of Mechanical Engineering,
American University in Cairo,
New Cairo 11835, Egypt
e-mail: mfawzyaly@aucegypt.edu

Karim Hamza

Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: khamza@umich.edu

1Corresponding author.

Manuscript received January 27, 2015; final manuscript received January 6, 2016; published online March 8, 2016. Assoc. Editor: Jianjun Shi.

J. Manuf. Sci. Eng 138(7), 071003 (Mar 08, 2016) (9 pages) Paper No: MANU-15-1063; doi: 10.1115/1.4032567 History: Received January 27, 2015; Revised January 06, 2016

This paper considers multiobjective optimization under uncertainty (MOOUC) for the selection of optimal cutting conditions in advanced abrasive machining (AAM) processes. Processes considered are water jet machining (WJM), abrasive water jet machining (AWJM), and ultrasonic machining (USM). Decisions regarding the cutting conditions can involve optimization for multiple competing goals, such as surface finish, machining time, and power consumption. In practice, there is also an issue of variations in the ability to attain the performance goals. This can be due to limitations in machine accuracy or variations in material properties of the workpiece and/or abrasive particles. The approach adopted in this work relies on a strength Pareto evolutionary algorithm (SPEA2) framework, with specially tailored dominance operators to account for probabilistic aspects in the considered multiobjective problem. Deterministic benchmark problems in the literature for the considered machining processes are extended to include performance uncertainty and then used in testing the performance of the proposed approach. Results of the study show that accounting for process variations through a simple penalty term may be detrimental for the multiobjective optimization. On the other hand, a proposed fuzzy-tournament dominance operator appears to produce favorable results.

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Grahic Jump Location
Fig. 3

Simplified illustration of an AWJM process

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Fig. 2

Simplified illustration of a WJM process

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Fig. 1

Illustration of some designs with performance variations in two objectives

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Fig. 4

Simplified illustration of an USM process

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Fig. 7

Two solutions for WJM showing their Monte Carlo samples and average performance

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Fig. 5

Fuzzy membership function for dominance check

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Fig. 6

Numerical experiment observations of a Bernoulli event for different values of Pr(Event)

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Fig. 8

Pareto plots for WJM

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Fig. 9

Pareto plots for AWJM

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Fig. 10

Pareto plots for USM




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