The identification of the post-necking strain hardening behavior of metal sheet is important for finite element analysis procedures of sheet metal forming process. The inverse modeling method is a practical way to determine the hardening curve to large strains. This study is thus focused on the evaluation of the inverse modeling method using a novel material performance test. In this article, hot uniaxial tensile test of a commercially pure titanium sheet with rectangular section was first conducted. Utilizing the raw data from the tensile test, the post-necking hardening behavior of the material is determined by a FE-based inverse modeling procedure. Then the inverse method is compared with some classical hardening models. In order to further evaluate the applicability of the inverse method, biaxial tensile test at elevated temperatures was performed using a special designed cruciform specimen. The cruciform specimen could guarantee that the maximum equi-biaxial deformation occurs in the center section. By using the inverse modeling procedure, the hardening curves under biaxial stress state are able to be extracted. Finally the stress-strain curves obtained from the two experiments are compared and analysis studies are provided.2 of 15 point. However, many sheet forming processes usually involve large deformation and can generate strains far beyond the necking point, so the stress-strain curve up to the necking point is not sufficient for numerical simulation procedures. Generally, the available pre-necking stress-strain curves are extrapolated to large strains using different hardening models [5]. It is obvious that the predicted post-necking hardening curve greatly depends on the choice of phenomenological constitutive models and one model that is best fitted to a certain material may not suits for another [6].Recently, numerous investigations have been made to acquire the hardening curve beyond diffuse necking by many researchers [7][8][9][10][11]. The initial attempt was made by Bridgman [7], who derived a set of analytical models for the distribution of stress and strain across the diffuse necking region for a round bar. However, the correction and compensation for stress and stress values of Bridgman's method is based on the measurement of evolving geometrical parameters of the necking area, which requires much experimental effort. Ling [8] and Zhang et al. [9] extended the research of Bridgman by proposing new models for determining the post-necking hardening behavior of strip specimen with rectangular section. Other researchers [10,11] further studied the necking problem experimentally based on Bridgman's work in more sophisticated ways. Currently, a new optical-numerical measurement techniques, i.e., the Digital Image Correlation (DIC) method, is used widely for obtaining full-field information for both in-plane displacements and strains of the test specimen and has been applied by many researchers for determining the post-necking hardening curve of uniaxial test [12,13], Scheider et al. [14] investigat...
Due to the acceptable high-temperature deformation resistance of Inconel 718, its welding parameters such as bonding temperature and pressure are inevitably higher than those of general metals. As a result of the existing punitive processing environment, it is essential to control the deformation of parts while ensuring the bonding performance. In this research, diffusion bonding experiments based on the Taguchi method (TM) are conducted, and the uniaxial tensile strength and deformation ratio of the experimental joints are measured. According to experimental data, a deep neural network (DNN) was trained to characterize the nonlinear relationship between the diffusion bonding process parameters and the diffusion bonding strength and deformation ratio, where the overall correlation coefficient came out to be 0.99913. The double-factors analysis of bonding temperature–bonding pressure based on the prediction results of the DNN shows that the temperature increment of the diffusion bonding of Inconel 718 significantly increases the deformation ratio of the diffusion bonding joints. Therefore, during the multi-objective optimization of the bonding performance and deformation of components, priority should be given to optimizing the bonding pressure and duration only.
The high temperature tensile test of Inconel 718 under the conditions of deformation temperature of 950 °C–1100 °C and strain rate of 0.0005 s−1–0.1 s−1 was carried out, and its true stress–true strain curve was drawn. Through the analysis of the flow stress of Inconel 718 under different conditions, it can be seen that the high-temperature rheological behavior of Inconel 718 is affected by the coupling of strain hardening effect and dynamic softening effect, and has significant loading history correlation. By applying the stretched data, a long short term memory (LSTM) recurrent neural network was trained to characterize the constitutive relationship of Inconel 718. The experimental results show that the prediction results of the LSTM constitutive model are extremely consistent with the experimental data, which is significantly better than the modified Johnson–Cook (M-JC) model. Finally, high temperature tensile experiments under variable strain rates were carried out to verify the feasibility of the LSTM constitutive model in the complex loading and unloading stages.
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