Optimization of aluminium sheet hot stamping process using a multi-objective stochastic approach

Abstract

This article aims to investigate the means to obtain optimal hot stamping process parameters and the influence of the stochastic variability of these parameters on forming quality. A multi-objective stochastic approach, integrating response surface methodology (RSM), multi-objective genetic algorithm optimization non-dominated sorting genetic algorithm II (NSGA-II) and the Monte Carlo simulation (MCS) method is proposed in this article to achieve this goal. RSM was used to establish the relationship between the process parameters and forming quality indices. NSGA-II was utilized to obtain a Pareto frontier, which consists of a series of optimal process parameters. The MCS method was employed to study and reduce the influence of a stochastic property of these process parameters on forming quality. The results confirmed the efficiency of the proposed multi-objective stochastic approach during optimization of the hot stamping process. Robust optimal process parameters guaranteeing good forming quality were also obtained using this approach.

Keywords:

• aluminium hot stamping
• optimization of process parameters
• response surface methodology
• multi-objective genetic algorithm
• Monte Carlo simulation method

Acknowledgement

The authors thank Professor Jianguo Lin at Imperial College London for providing the use of the software PAM-STAMP 2G.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology [no. P2014-15], the Specialized Research Fund for the Doctoral Program of Higher Education of China [no. 20120006110017] and the Joint Funds of the National Natural Science Foundation of China [U1564202].