Inverse Identification of Constitutive Model for GH4198 Based on Genetic-Particle Swarm Algorithm.

A precise Johnson-Cook (J-C) constitutive model is the foundation for precise calculation of finite-element simulation. In order to obtain the J-C constitutive model accurately for a new cast and forged alloy GH4198, an inverse identification of J-C constitutive model was proposed based on a genetic-particle swarm algorithm. Firstly, a quasi-static tensile test at different strain rates was conducted to determine the initial yield strength A, strain hardening coefficient B, and work hardening exponent n for the material's J-C model. Secondly, a new method for orthogonal cutting model was constructed based on the unequal division shear theory and considering the influence of tool edge radius. In order to obtain the strain-rate strengthening coefficient C and thermal softening coefficient m, an orthogonal cutting experiment was conducted. Finally, in order to validate the precision of the constitutive model, an orthogonal cutting thermo-mechanical coupling simulation model was established. Meanwhile, the sensitivity of J-C constitutive model parameters on simulation results was analyzed. The results indicate that the parameter m significantly affects chip morphology, and that the parameter C has a notable impact on the cutting force. This study addressed the issue of missing constitutive parameters for GH4198 and provided a theoretical reference for the optimization and identification of constitutive models for other aerospace materials.

Calibration of Yld2000-2D Anisotropy Yield Criterion with Traditional Testing and Inverse Identification Strategies.

In order to improve predictive capabilities of numerical simulations, Yld2000-2D yield criterion is used to model the plastic anisotropic behaviors of AA5086 sheets. The parameters of Yld2000-2D yield criterion are identified based on the traditional testing strategy and the inverse identification strategy, respectively. The traditional testing strategy considers uniaxial and equi-biaxial tensile tests. The inverse identification strategy relies on the finite element model update (FEMU) method that couples with a biaxial tensile test using a dedicated cruciform specimen or the Pottier bulging test. The identified parameters are preliminarily evaluated by comparing predicted and experimental yield stresses, r-values, and yield loci. Then, the deep drawing test and simulations are performed. The identified parameter sets of Yld2000-2D yield criterion are further evaluated in terms of practical forming by comparing the predicted earing profile height with the experimental results. The results show that the inverse identification strategy can be an effective alternative to identify the parameters of Yld2000-2D yield criterion, and a well-designed heterogeneous test could lead to a better identification result.

Inverse Identification of a Constitutive Model for High-Speed Forming Simulation: An Application to Electromagnetic Metal Forming.

Forming simulation requires a constitutive model whose parameters are typically determined with tensile tests assumed static. However, this conventional approach is impractical for high-speed forming simulation characterized by high strain rates inducing transient effects. To identify constitutive parameters in relation to high-speed forming simulation, we formulated the problem of constitutive modeling as inverse parameter estimation addressed by regularized nonlinear least squares. Regarding the proposed inverse constitutive modeling, we adopted the L-curve method for proper regularization and model order reduction for rapid simulation. For demonstration, we corroborated the proposed strategy by identifying the modified Johnson-Cook model in the context of a free bulge test with electromagnetic metal forming simulation. The L-curve method allowed us to systematically choose a regularization parameter, and model order reduction brought enormous computational savings. After identifying constitutive parameters, we successfully verified and validated the reduced and original simulation models, respectively, with a manufactured workpiece. In addition, we validated the numerically identified constitutive model with a dynamic material test using a split Hopkinson pressure bar. Overall, we showed that inverse constitutive modeling for high-speed forming simulation can be effectively tackled by regularized nonlinear least squares with the help of an L-curve and a reduced-order model.

Inverse Finite Element Approach to Identify the Post-Necking Hardening Behavior of Polyamide 12 under Uniaxial Tension.

Finite-element (FE) simulations that go beyond the linear elastic limit of materials can aid the development of polymeric products such as stretch blow molded angioplasty balloons. The FE model requires the input of an appropriate elastoplastic material model. Up to the onset of necking, the identification of the hardening curve is well established. Subsequently, additional information such as the cross-section and the triaxial stress state inside the specimen is required. The present study aims to inversely identify the post-necking hardening behavior of the semi-crystalline polymer polyamide 12 (PA12) at different temperatures. Our approach uses structural FE simulations of a dog-bone tensile specimen in LS-DYNA with mesh sizes of 1 mm and 2 mm, respectively. The FE simulations are coupled with an optimization routine defined in LS-OPT to identify material properties matching the experimental behavior. A Von Mises yield criterion coupled with a user-defined hardening curve (HC) were considered. Up to the beginning of necking, the Hockett−Sherby hardening law achieved the best fit to the experimental HC. To fit the entire HC until fracture, an extension of the Hockett−Sherby law with power-law functions achieved an excellent fit. Comparing the simulation and the experiment, the following coefficient of determination R2 could be achieved: Group I: R2 > 0.9743; Group II: R2 > 0.9653; Group III: R2 > 0.9927. Using an inverse approach, we were able to determine the deformation behavior of PA12 under uniaxial tension for different temperatures and mathematically describe the HC.

Identification of Orthotropic Elastic Properties of Wood by a Synthetic Image Approach Based on Digital Image Correlation.

This work aims to determine the orthotropic linear elastic constitutive parameters of Pinus pinaster Ait. wood from a single uniaxial compressive experimental test, under quasi-static loading conditions, based on two different specimen configurations: (a) on-axis rectangular specimens oriented on the radial-tangential plane, (b) off-axis specimens with a grain angle of about 60° (radial-tangential plane). Using digital image correlation (DIC), full-field displacement and strain maps are obtained and used to identify the four orthotropic elastic parameters using the finite element model updating (FEMU) technique. Based on the FE data, a synthetic image reconstruction approach is proposed by coupling the inverse identification method with synthetically deformed images, which are then processed by DIC and compared with the experimental results. The proposed methodology is first validated by employing a DIC-levelled FEA reference in the identification procedure. The impact of the DIC setting parameters on the identification results is systematically investigated. This influence appears to be stronger when the parameter is less sensitive to the experimental setup used. When using on-axis specimen configuration, three orthotropic parameters of Pinus pinaster (ER, ET and νRT) are correctly identified, while the shear modulus (GRT) is robustly identified when using off-axis specimen configuration.

State and Force Estimation on a Rotating Helicopter Blade through a Kalman-Based Approach.

The interaction between the rotating blades and the external fluid in non-axial flow conditions is the main source of vibratory loads on the main rotor of helicopters. The knowledge or prediction of the produced aerodynamic loads and of the dynamic behavior of the components could represent an advantage in preventing failures of the entire rotorcraft. Some techniques have been explored in the literature, but in this field of application, high accuracy can be reached if a large amount of sensor data and/or a high-fidelity numerical model is available. This paper applies the Kalman filtering technique to rotor load estimation. The nature of the filter allows the usage of a minimum set of sensors. The compensation of a low-fidelity model is also possible by accounting for sensors and model uncertainties. The efficiency of the filter for state and load estimation on a rotating blade is tested in this contribution, considering two different sources of uncertainties on a coupled multibody-aerodynamic model. Numerical results show an accurate state reconstruction with respect to the selected sensor layout. The aerodynamic loads are accurately evaluated in post-processing.

Investigation on masticatory muscular functionality following oral reconstruction - An inverse identification approach.

The human masticatory system has received significant attention in the areas of biomechanics due to its sophisticated co-activation of a group of masticatory muscles which contribute to the fundamental oral functions. However, determination of each muscular force remains fairly challenging in vivo; the conventional data available may be inapplicable to patients who experience major oral interventions such as maxillofacial reconstruction, in which the resultant unsymmetrical anatomical structure invokes a more complex stomatognathic functioning system. Therefore, this study aimed to (1) establish an inverse identification procedure by incorporating the sequential Kriging optimization (SKO) algorithm, coupled with the patient-specific finite element analysis (FEA) in silico and occlusal force measurements at different time points over a course of rehabilitation in vivo; and (2) evaluate muscular functionality for a patient with mandibular reconstruction using a fibula free flap (FFF) procedure. The results from this study proved the hypothesis that the proposed method is of certain statistical advantage of utilizing occlusal force measurements, compared to the traditionally adopted optimality criteria approaches that are basically driven by minimizing the energy consumption of muscle systems engaged. Therefore, it is speculated that mastication may not be optimally controlled, in particular for maxillofacially reconstructed patients. For the abnormal muscular system in the patient with orofacial reconstruction, the study shows that in general, the magnitude of muscle forces fluctuates over the 28-month rehabilitation period regardless of the decreasing trend of the maximum muscular capacity. Such finding implies that the reduction of the masticatory muscle activities on the resection side might lead to non-physiological oral biomechanical responses, which can change the muscular activities for stabilizing the reconstructed mandible.

Sensitivity-based virtual fields for the non-linear virtual fields method.

The virtual fields method is an approach to inversely identify material parameters using full-field deformation data. In this manuscript, a new set of automatically-defined virtual fields for non-linear constitutive models has been proposed. These new sensitivity-based virtual fields reduce the influence of noise on the parameter identification. The sensitivity-based virtual fields were applied to a numerical example involving small strain plasticity; however, the general formulation derived for these virtual fields is applicable to any non-linear constitutive model. To quantify the improvement offered by these new virtual fields, they were compared with stiffness-based and manually defined virtual fields. The proposed sensitivity-based virtual fields were consistently able to identify plastic model parameters and outperform the stiffness-based and manually defined virtual fields when the data was corrupted by noise.