Metamodel-based multi-objective optimization of a turning process by using finite element simulation

This study investigates the advantages and potentials of the metamodel-based multi-objective optimization (MOO) of a turning operation through the application of finite element simulations and evolutionary algorithms to a metal cutting process. The objectives are minimizing the interface temperature...

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Bibliographic Details
Published in:Engineering optimization 2020-07, Vol.52 (7), p.1261-1278
Main Authors: Amouzgar, Kaveh, Bandaru, Sunith, Andersson, Tobias, Ng, Amos H. C.
Format: Article
Language:English
Subjects:
Computer simulation
Computing time
Cutting wear
Deformation wear
Evolutionary algorithms
Finite element method
Genetic algorithms
Machining
Material removal rate (machining)
Metal cutting
Metamodeling
Metamodelling
Metamodels
Multi-objective optimization
Multiple objective analysis
Pareto optimization
Process parameters
Production and Automation Engineering
Produktion och automatiseringsteknik
Radial basis function
Simulation
Surrogate models
Tool wear
Turning
Turning (machining)
VF-KDO
Virtual Manufacturing Processes
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Summary:This study investigates the advantages and potentials of the metamodel-based multi-objective optimization (MOO) of a turning operation through the application of finite element simulations and evolutionary algorithms to a metal cutting process. The objectives are minimizing the interface temperature and tool wear depth obtained from FE simulations using DEFORM-2D software, and maximizing the material removal rate. Tool geometry and process parameters are considered as the input variables. Seven metamodelling methods are employed and evaluated, based on accuracy and suitability. Radial basis functions with a priori bias and Kriging are chosen to model tool-chip interface temperature and tool wear depth, respectively. The non-dominated solutions are found using the strength Pareto evolutionary algorithm SPEA2 and compared with the non-dominated front obtained from pure simulation-based MOO. The metamodel-based MOO method is not only advantageous in terms of reducing the computational time by 70%, but is also able to discover 31 new non-dominated solutions over simulation-based MOO.
ISSN:0305-215X
1029-0273
1029-0273
DOI:10.1080/0305215X.2019.1639050