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

• Published in: Engineering optimization Vol. 48; no. 12; pp. 2173 - 2189
• Main Authors: Xiao, Wenchao, Wang, Baoyu, Zhou, Jing, Ma, Wenyu, Yang, Lei
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01.12.2016; Taylor & Francis Ltd
• Subjects: aluminium hot stamping; Computer simulation; Forming; Genetic algorithms; Hot stamping; Machine tools; Monte Carlo simulation; Monte Carlo simulation method; multi-objective genetic algorithm; Multiple objective analysis; Optimization; optimization of process parameters; Process parameters; Quality; response surface methodology; Stochasticity
• ISSN: 0305-215X; 1029-0273
• DOI: 10.1080/0305215X.2016.1163483

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.