Abstract
To control the welding residual stress and deformation of metal inert gas (MIG) welding, the influence of welding process parameters and preheat parameters (welding speed, heat input, preheat temperature, and preheat area) is discussed, and a prediction model is established to select the optimal combination of process parameters. Thermomechanical numerical analysis was performed to obtain the residual welding deformation and stress according to a 100 × 150 × 50 × 4 mm aluminum alloy 6061-T6 T-joint. Owing to the complexity of the welding process, an optimal Latin hypercube sampling (OLHS) method was adopted for sampling with uniformity and stratification. Analysis of variance (ANOVA) was used to find the influence degree of welding speed (7.5–9 mm/s), heat input (1500–1700 W), preheat temperature (80–125 °C), and preheat area (12–36 mm). The range of research parameters are according to the material, welding method, thickness of the welding plate, and welding procedure specification. Artificial neural network (ANN) and multi-objective particle swarm optimization (MOPSO) was combined to find the effective parameters to minimize welding deformation and stress. The results showed that preheat temperature and welding speed had the greatest effect on the minimization of welding residual deformation and stress, followed by the preheat area, respectively. The Pareto front was obtained by using the MOPSO algorithm with ε-dominance. The welding residual deformation and stress are the minimum at the same time, when the welding parameters are selected as preheating temperature 85 °C and preheating area 12 mm, welding speed is 8.8 mm/s and heat input is 1535 W, respectively. The optimization results were validated by the finite element (FE) method. The error between the FE results and the Pareto optimal compromise solutions is less than 12.5%. The optimum solutions in the Pareto front can be chosen by designers according to actual demand.
Highlights
With the rapid development of rail vehicles in China in recent years, an increasing number of high requirements have been put forward for the materials used in railway vehicles [1]
Matthew [10] used a design of experiments (DOE) to study the welding process parameters, such as velocity, weld pressure, upset distance, and preheat temperature, on weld strength, heat affected zone, and energy usage for the friction welding of AISI
The optimization objectives of welding residual deformation and welding stress only reflect the results in the stress field after thermal-mechanical coupling analysis
Summary
With the rapid development of rail vehicles in China in recent years, an increasing number of high requirements have been put forward for the materials used in railway vehicles [1]. Due to the high linear expansion coefficient of 6061-T6, a study of methods to reduce the welding residual stress and deformation is urgently needed [5,6]. Many scholars have chosen to study and optimize the welding process parameters to improve the welding qualities and to reduce the welding residual deformation and residual tensile stress [7,8]. Kumar [9] studied the effects of MIG welding process parameters such as current, voltage, and preheat temperature and optimized them using gray-based Taguchi methodology. Khoshroyan [12] studied three modes of current, two different speeds, and two different sequences, through comparative analysis; the influence parameters of reducing transverse deformation were found
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