Multi-objective optimization of an aluminum torch brazing process by means of genetic programming and R-NSGA-II

Multi-objective optimization of an aluminum torch brazing process by means of genetic programming... This paper presents a hybrid of genetic programming (GP) and reference-point-based non-dominated sorting genetic algorithm (R-NSGA-II) for the multi-objective optimization of an aluminum torch brazing process for the fabrication of condensers for the automotive industry. The objectives to be optimized are a vacuum leakage test (quality of product), cycle time, and energy consumption (production cost). GP is used to find a mathematical model that describes the relationship between input and output process parameters. Thereafter, reference-point-based NSGA-II procedure is employed to provide the decision maker with a set of solutions close to her/his preferences. Results show that this approach may support the decision makers of the process to set the optimal input process parameters in order to achieve competitive advantages in quality and production costs. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

Multi-objective optimization of an aluminum torch brazing process by means of genetic programming and R-NSGA-II

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer-Verlag London
Subject
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
ISSN
0268-3768
eISSN
1433-3015
D.O.I.
10.1007/s00170-017-0102-y
Publisher site
See Article on Publisher Site

Abstract

This paper presents a hybrid of genetic programming (GP) and reference-point-based non-dominated sorting genetic algorithm (R-NSGA-II) for the multi-objective optimization of an aluminum torch brazing process for the fabrication of condensers for the automotive industry. The objectives to be optimized are a vacuum leakage test (quality of product), cycle time, and energy consumption (production cost). GP is used to find a mathematical model that describes the relationship between input and output process parameters. Thereafter, reference-point-based NSGA-II procedure is employed to provide the decision maker with a set of solutions close to her/his preferences. Results show that this approach may support the decision makers of the process to set the optimal input process parameters in order to achieve competitive advantages in quality and production costs.

Journal

The International Journal of Advanced Manufacturing TechnologySpringer Journals

Published: Feb 9, 2017

References

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