The paper presents a hybrid strategy to determine constitutive parameters for thin-walled tubes based on experimental responses from hydraulic bulge tests. This developed procedure integrates the analytical model, finite element analysis and gradient-based optimization algorithm, where initial guesses of material parameters are generated quickly by a theoretical method, then they are input to an inverse framework integrating Gauss-Newton algorithm and finite element method. The solving for this inverse problem leads to a more accurate identification of material parameters by reducing the discrepancies between simulated results and experimental data. To evaluate its feasibility and performance, hydraulic bulge tests with different end-conditions for annealed 6060 and 5049 aluminium tubes are carried out. The strength coefficient and hardening exponent are determined using the hybrid strategy based on the collected measurements in the experiment. These material parameters are used to compare with those obtained by a single analytical model and inverse model. The comparison validates that the proposed hybrid strategy is not sensitive to starting points and can improve the calculation efficiency and determine more accurate constitutive parameters.

Sheet Metal Forming Optimization: Bioinspired Approaches

Identification of constitutive parameters for thin-walled aluminium tubes using a hybrid strategy

This article describes the optimization of processing parameters for the surface roughness of AISI316 austenitic stainless steel. While experimenting, parameters in the process like feed rate (fd), speed (vc), and depth of cut (DoC) were used to study the outcome on the surface roughness (Ra) of the workpiece. The experiment was carried out using the design of experiments (DOE) on a computer numerical control (CNC) lathe. The surface roughness is tested for three conditions i.e. Dry, Wet, and cryogenic conditions after the turning process. Samples are step turned on CNC Lathe for all three conditions with a set of experiments designed. The response surface methodology is implemented, and mathematical models are built for all three conditions. The nature-inspired algorithm is the best way to get the optimal value. For the discussed problem in the paper, nature-inspired techniques are used for obtaining the optimum parameter values to get minimum surface roughness for all set conditions. The Grasshopper optimization algorithm (GOA) is the technique that is the most effective method for real-life applications. In this research, GOA is used to get optimum values for the surface roughness (Ra) at Dry, Wet and cryogenic conditions. Finally, results are compared, and it's observed that the values obtained from GOA are minimum in surface roughness value.

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Sheet Metal Forming Optimization: Bioinspired Approaches

Citations

Identification of constitutive parameters for thin-walled aluminium tubes using a hybrid strategy

Parameter optimization of AISI 316 austenitic stainless steel for surface roughness by Grasshopper optimization algorithm

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