Energy-Economizing Optimization of Magnesium Alloy Hot Stamping Process

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.

Special Issue on “Green Technologies for Production Processes”

“…Considering the constraints of machine tool equipment performance and processing quality requirements, the grinding wheel's linear velocity, cutting feed rate, and the rotation speed of the workpiece were selected as the optimization variables, and the improved NSGA-II algorithm was applied to solve the optimization model. Considering that the process parameters of hot stamping considerably influence the product forming quality and energy consumption, Gao et al [12] introduce the energy-economizing indices of hot stamping with multiobjective consideration of energy consumption and product forming quality to find a pathway by which to obtain optimal hot stamping process parameters. The obtained results may be used for guiding process optimization regarding energy saving and the method of manufacturing parameters selection.…”

A Reinforcement Learning Control in Hot Stamping for Cycle Time Optimization

“…Decision-making and control problems have been traditionally faced with human-based expertise, rule-based techniques, mathematical optimization, metaheuristic, and heuristics. For instance, genetic algorithms in metaheuristics have been widely employed for multi-objective process parameters optimization in hot stamping production, ensuring forming quality [ 6 ], considering stochastic variability of these parameters [ 7 ], and reducing energy consumption [ 8 ]. Classical optimal control solutions are considered offline, require complete and previous knowledge of the environment dynamics, and are not able to react to unexpected changes and handle uncertainties favorably.…”

Effect of process parameters on AA6061/Q345 bimetal composite for hot stamping

“…Compared with traditional cold stamping forming workpieces, it has higher forming accuracy and less springback, and can greatly improve the forming quality problems such as wrinkles and cracks. 5 Hot stamping is mostly used in manufacturing metal products such as automobile B-pillar and anti-collision beam in the automotive industry where materials such as advanced high strength steel, 6,7 aluminum alloys, 8,9 magnesium alloys materials 10 and composites 11–13 are used.…”

“…15 Mohamed et al 8 using the finite element (FE) simulation of the hot stamping process, predict the failure characteristics and formability of aa6082 under different forming rates. Gao et al 10 established the response surface model between hot stamping process parameters and energy-saving index of ZK60 magnesium alloy. The optimal combination of parameters was obtained by the multi-objective genetic algorithm.…”