Electromagnetic flanging is a potential forming technology for aluminum alloy. However, due to the temporal and spacial varied magnetic force acting on the workpiece, the forming process is not easy to be studied by experimental method and the final shape of the workpiece is difficult to control. This paper aims to investigate the forming process by numerical simulation and to utilize an efficient method to control the formed shape. The result shows that the plastic strain before collision is determined by the geometry, but not dependent on the discharge energy. The collision between the workpiece and die is benefit to the forming. The strain to failure of the annealed 2219 aluminum alloy for electromagnetic forming is 77.8% higher than that for quasi-static. In addition, the final shape of the workpiece can be precisely controlled by designing an appropriate die compensation angle.

A driving coil is one significant tool for transferring electrical energy to plastic energy during electromagnetic forming, and the coil structure plays a crucial role on the distribution of magnetic field and electromagnetic force acting on the workpiece and determines the forming characteristics and magnitude. Due to the limitation of the conventional coil on forming a large and thick-walled component, this paper proposes a novel multi-layer flat spiral coil for large and thick sheets based on theoretical analysis of the relations of coil inductance, skin depth of sheets and energy efficiency. Taking electromagnetic flanging forming of a large and thick-walled sheet for example, a 3D numerical model is developed to investigate the effects of coil structure on magnetic field and sheet forming. Finally, several electromagnetic flanging experiments with 5 mm 5056 aluminum alloy sheets by a three-layer coil are carried out to validate the simulation results and a comparison of the thickness distribution and the fittability degree between the die and the sheet after one-time and two-time forming is performed. The results show that the magnetic force loading on the workpiece increases obviously with the increase of the coil layer owing to the additive effect of each layer of the multi-layer coils, and further enlarges the deformation, while the pressure acting on the coils can be controlled effectively due to the share of each layer of the multi-layer coils. The energy efficiency of the multi-layer coils increases with the increase of the skin depth and peaks at 19.6% when the skin depth is equal to the sheet thickness. The experimental results of electromagnetic flanging based on a three-layer coil coincide with the simulation results.

This paper studies the effects of strain rate on dynamic mechanical behavior and microstructure evolution of 5A02-O aluminum alloy at room temperature. Based on the results of the dynamic tensile tests and compressive tests at strain rates of 1000–5000 s−1 by split Hopkinson bar as well as the results of quasi-static tests at strain rate of 0.001 s−1, it is shown that with increasing strain rate, the flow stress and tensile strength significantly increase and notable strain hardening and thermal softening behaviors are observed for 5A02-O with elongation of 63.00% and softening ratio of 73.23% at the strain rate of 4000 s−1. The strain rate sensitivity for 5A02-O is enhanced in the range of 1000–3000 s−1. Scanning electron microscopy (SEM) observations illustrate that the fracture surfaces are characterized by larger and deeper dimple-like structure with more precipitates at higher strain rates, which indicates the ductile failure mode. The enhancement of ductility is interpreted via the inertia effect which may contribute to diffuse necking, slow down the necking development and delay the onset of fracture. Furthermore, transmission electron microscopy (TEM) observations show that higher strain rate leads to higher dislocation density, smaller cell size with thinner cell wall and the appearance of dislocation wall with parallel dislocation lines. Dislocation cells are incomplete under dynamic deformation. In addition, the micro-hardness of 5A02-O increases with increasing strain rate.

•The calculated coefficient of strain rate by J–C model is too little.•Modified C–S model incorporates strain into strain rate function.•Predictability of modified R–K model decreases when strain rate exceeds 3000 s−1.•ANN model has the highest prediction precision.•Rank of predictability: ANN model > modified C–S model > modified R–K model > J–C model.In high-velocity forming process, material constitutive relationships are remarkably changed, which has a significant effect on forming process and deformation behaviors. Based on the quasi-static tensile experiments and compression experiments at different temperatures, and dynamic tensile experiments and compression experiments at room temperature, the constitutive behaviors of 5A02 aluminium alloy and the mechanism of high strain rate influencing material dynamic responses are investigated in this paper. According to the above experimental results, Johnson–Cook model (J–C model), modified Cowper–Symonds model (C–S model), modified Rusinek–Klepaczko model (R–K model) and artificial neural network model (ANN model) have been proposed to describe the stress–strain relationship of 5A02 aluminium alloy in high-velocity forming process, and their predictability are compared by employing several statistical evaluation measures. The results show that the prediction precision of J–C model is limited, for the calculated coefficient of strain rate is too little to reflect the increasing trend of stress as strain rate is elevated; modified C–S model takes into consideration the effect of strain in the description of strain rate effect, thus it has a more precise correlation between predicted stress and measured stress; based on the influences of strain and strain rate on the dislocation evolution mechanism, the prediction precision of modified R–K model is higher when the strain rate is in the range of 10−3–3000 s−1, while the precision decreases as the strain rate exceeds 3000 s−1 due to the changed micromechanism of dislocation evolution; ANN model exhibits most powerful predictability due to its self-organizing ability, self-learning ability and high capacity of error tolerance.