Abstract
Reducing the mass of vehicles is an effective way to improve energy efficiency and mileage. Therefore, hot stamping is developed to manufacture lightweight materials used for vehicle production, such as magnesium and aluminum alloys. However, in comparison with traditional cold stamping, hot stamping is a high-energy-consumption process, because it requires heating sheet materials to a certain temperature before forming. Moreover, the process parameters of hot stamping considerably influence the product forming quality and energy consumption. In this work, the energy-economizing indices of hot stamping are established with multiobjective consideration of energy consumption and product forming quality to find a pathway by which to obtain optimal hot stamping process parameters. An energy consumption index is quantified by the developed models, and forming quality indices are calculated using a finite element model. Response surface models between the process parameters and energy-economizing indices are established by combining the Latin hypercube design and response surface methodology. The multiobjective problem is solved using a multiobjective genetic algorithm (NSGA-II) to obtain the Pareto frontier. ZK60 magnesium alloy hot stamping is applied as a case study to obtain an optimal combination of parameters, and compromise solutions are compared through stamping trials and numerical simulations. The obtained results may be used for guiding process optimization regarding energy saving and the method of manufacturing parameters selection.
Highlights
Rapid economic development has recently accelerated increases in the consumption of energy, especially in industrial sectors, which is causing a series of environmental problems, such as greenhouse gas (GHG) emissions [1]
In order to reduce the influence of the stochastic property of process parameters on forming quality, Xiao et al integrated multiobjective stochastic approaches, such as response surface methodology (RSM), nondominated sorting genetic algorithm II (NSGA-II), and Monte Carlo simulations (MCSs), to obtain the optimal process parameters of aluminum hot stamping [19]
The surrogate model between each process parameter and its corresponding index value is established on the basis of RSM, and the process parameters are optimized on the basis of the multiobjective genetic algorithm
Summary
Rapid economic development has recently accelerated increases in the consumption of energy, especially in industrial sectors, which is causing a series of environmental problems, such as greenhouse gas (GHG) emissions [1]. An efficient way to improve the energy efficiency and driving range of vehicles is mass reduction Some lightweight materials, such as magnesium alloys, aluminum alloys, and ultra-high-strength steels, have been rapidly increasing in quantity and have been applied to the automotive industry [3]. Many researchers have focused on the optimization of these parameters to solve the product forming quality problem of hot stamping. In order to reduce the influence of the stochastic property of process parameters on forming quality, Xiao et al integrated multiobjective stochastic approaches, such as RSM, NSGA-II, and Monte Carlo simulations (MCSs), to obtain the optimal process parameters of aluminum hot stamping [19]. A novel parameter optimization method for the hot stamping process is proposed with the multiobjective improvement of forming quality and process energy consumption
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
