Papers by Keyword: Inverse Identification

Abstract: The efficient development of high-quality sheet metal components increasingly depends on predictive numerical simulations conducted prior to forming operations. Achieving such accuracy requires precise calibration of models that represent the complex mechanical behaviour of metals. Mechanical testing provides the essential data for calibration, revealing material anisotropy, strain hardening, and ductile fracture. However, traditional characterisation approaches are often labor-intensive, time-consuming, and prone to operator variability. Within the phenomenological framework, numerous tests are typically required to capture the full material response, including repeats for statistical reliability, leading to high costs and extended lead times. To address these limitations, this study introduces an automated mechanical testing platform designed to rapidly acquire experimental data useful for material models. The use of a cobot enables fully automated test sequences, ensuring high repeatability and reducing manual intervention. When combined with automated model calibration, this approach provides a direct link between the physical material (metallic sheet) and its virtual mechanical representation.

Abstract: Computer-aided engineering systems rely on constitutive models and their parameters to describe the material behaviour. The calibration of more elaborated material models with a larger number of parameters becomes very time and cost consuming. The development of image-based technology has enhanced the interest in inverse identification methods, which, when coupled with full-field measurements, have the potential to reduce the number of experimental tests required to accurately identify material properties. This work aims to identify the Swift hardening law parameters of a dual-phase steel using a tensile test on a heterogeneous dogbone specimen under uniaxial and quasi-static loading conditions using the finite element model updating (FEMU) technique. The numerical results were used to generate synthetic images, which were then processed by digital image correlation (DIC) and used as the reference in the identification procedure. Two different approaches were tested: (i) directly comparing the numerical results to the reference; (ii) using DIC-levelled numerical data by iteratively generating synthetic images and using the DIC filter with the same settings as were used on the reference (virtual experiment). The identification results obtained from both approaches are compared and discussed.

Abstract: To fully exploit the predictive accuracy of advanced anisotropic yield functions, a large number of classical mechanical tests is required for calibration purposes. The Finite Element Model Updating (FEMU) technique enables to simultaneously extract multiple anisotropic parameters when fed with heterogeneous strain fields obtained from a single information-rich experiment. This inverse approach has the potential to mitigate the experimental calibration effort by resorting to a single, yet more complex experiment augmented with Digital Image Correlation. In this paper, we inversely identify the sought anisotropic parameters of two selected yield functions for a low carbon steel sheet based on the previously designed information-rich tensile specimen. The experimentally acquired strain field data is used to inversely identify the Hill48 yield criterion and the Yld2000-2d yield function, respectively. The results are compared with conventional calibration methods for both anisotropic yield functions. The inverse identification is then thoroughly studied using virtual experiments enabling to disentangle the effect of the material model error and the strain reconstruction error (DIC), respectively. It is shown that the material model error dominates the inverse identification of the Hill48 yield criterion. The reduced material model error for the Yld2000-2d yield function enables obtain inversely identified anisotropic parameters that are closer to the reference parameters. The paper clearly shows the importance of the predictive accuracy of the selected anisotropic yield function when applying inverse identification. Keywords: Anisotropic yield criteria; Material parameters identification; Heterogeneous mechanical tests; Inverse identification; DIC.

Abstract: The Yoshida-Uemori combined kinematic and isotropic hardening model is widely applied to numerical prediction of spring-back during sheet metal forming process. With the experimental plastic behavior of aluminum alloy AA5182-O sheet under single cyclic loading, the semi-analytical method was presented to calibrate the parameters of Yoshida-Uemori hardening model. Meanwhile, an inverse identification method was suggested by parameter optimization for minimizing the error between the experimental and predicted results. By comparing the two methods, the Yoshida-Uemori hardening model identified by inverse method is found to be more accurate for description of the Bauschinger effect than the one identified by semi-analytical method, especially for transient softening phenomenon.

Abstract: The present paper aims to assess the accuracy of identification methods used in the evaluation of the flow stress relationship of tubular materials for hydroforming applications. Based on experimental data acquired from home designed and manufactured experimental tool and results collected from literature, flow stress parameters are determined using both analytical and inverse identification methods. The obtained results are coped to experimental measurements to validate the proposed approaches. It is shown from the analysis based on the comparative assessment of flow stress inferred from tube bulge test that, inverse parameter identification method is the appropriate methodology that contribute to a more accurate tube hydroforming characterization.

Abstract: A inverse identification method is proposed to deal with the nonlinear boundary parameter identification problem. The boundary condition is modeled as unknown equivalent substructure applied on the initial linear structure and the parameter identification inverse problem is translated into a linear-model based equivalent force identification problem. Based on a given multi-point approximations method, the time history of the equivalent force is identified using the measured dynamic response. The unknown dynamic forces due to substructure are parameterized by combination the base functions on a series of space-time points. Using the moving least squares method, a matrix is derived to extract the force time history from the measured dynamic response. Then, the boundary stiffness and damping are identified using the input-output data of the equivalent substructure. The proposed approach has been demonstrated by some numerical examples and the boundary parameters are successfully identified.

Abstract: A novel PID controller identification method based on internal model control structure is proposed. The proposed method avoids the necessity of approximating the time delay for designing the PID controller. It results in a robust and effective PID controller tuning. The method is effective for both time constant and time delay dominant systems, with much improved performance for the latter case.

Abstract: Total thrust on shield tunneling machine is the most important mechanical quantity, while its also the core parameter which can reflect the geological adaptability of the shield. Mechanical model of total thrust is established by applying the mechanical analysis firstly. Multiple regression method is applied to indentify undetermined coefficients of mechanical model. Based on the on-site data acquired from Rd. TieDong to Rd. ZhangXingZhuang of Tianjin No.3 subway project, regularity and compositions of model residual are discussed and inverse identification model of total thrust is further proposed. The comparison between identification results and the on-site data verifies the feasibility of inverse identification model. Analysis results indicate that inverse identification model can dynamically reflect the relationship among total thrust, operating parameters and geological parameters. This work can offer helpful references for thrust selection and real-time control.

Abstract: Tube hydroforming processes are an excellent way for manufacturing reduced weight parts with complex shapes in widespread fields. Accurate numerical simulation of tube hydroforming process is particularly based on precise material parameters deduced from experimental tests. The free bulge test is widely employed for the parameter identification of tubular material behavior models by means of analytical [1] and numerical methods [2]. In this context, an inverse identification methodology using free bulge tests was developed. These tests were carried out by means of a new home-designed and manufactured bulge forming machine. The objective of this work is the validation of the inverse identification method using tube hydroforming in square cross-section die. The analysis of this particular hydroforming process with respect to material parameters is performed. For this purpose, circular section tubes made of low carbon steel S235 and aluminum alloy AA6063-O are hydroformed against square-cross sectional die using our bulge forming machine. Afterwards, FE model is constructed to simulate square-sectional hydroformed parts. The influence of some parameters, such as strain hardening exponent, anisotropy parameter and friction coefficient, on numerical square cross-sectional hydroformed part thickness is analyzed. It permits to assess the sensitivity of the thickness relative to used material parameters in the FE model. In order to validate the inverse identification procedure for both materials, experimental thicknesses along the profile of cross-sectional hydroformed parts are measured and compared with the corresponding numerical thicknesses predicted by FE model. It is proven after analyzing the obtained results that the chosen response, i.e. thickness distribution along the profile of the tube hydroforming against the square cross-section die, used for the validation is sensitive to the identified material properties. Particularly, it is demonstrated for low carbon steel S235 that numerical thickness is in good agreement with experimental data. However, for aluminum alloy AA6063-O, a discrepancy between experimental and predicted thicknesses is noticed. Anyway, it is demonstrated that inverse identification approach leads to sufficiently accurate parameters used for numerical tube hydroforming simulations. Furthermore, it seems that Hill48’s yield criterion is more suitable for describing steels plastic behavior than aluminum alloys for tube hydroforming processes. Concerning aluminum alloy, certainly the choice of appropriate yield criterion is of paramount importance on the prediction of tubular plastic behavior in tube hydroforming. Consequently, it is shown that the use of simple tube hydroforming in square-section die is suitable for the validation of FE model which is identified by inverse method using free bulge test.

Abstract: The simulation of the metal forming processes requires accurate constitutive models describing the material behaviour at finite strain, and taking into account several conditions. The choice of a rheological model and the determination of its parameters should be made from a test that generates such conditions. The major difficulty encountered is that there is no experimental test satisfying all these criteria. The use of more than one test seems well adapted, and is utilized to characterize the rheological behaviour at operating conditions corresponding to metal forming applications. Inverse analysis is then considered. Therefore, the difficulty lies with the long computing time that was taken when an optimization procedure is coupled with a finite element computation (FEC) to identify the material parameters. In order to solve the computing time problem, this paper proposes a hybrid identification method based on an artificial neural network and a genetic algorithm (ANN-GA). The proposed strategy is applied to identify the damage material parameters of the AISI 304 steel and using the bulge test.