Search Results (15)

The reliability of solid rocket motors depends primarily on the structural integrity of their propellants. Internal cavity defects in the widely used hydroxyl-terminated polybutadiene (HTPB) propellant, formed during manufacturing and service, significantly degrade its mechanical properties and compromise motor safety. This study developed a constitutive model for HTPB propellant based on the generalized incremental stress–strain damage model (GISSMO). The validity of the constitutive model was verified through uniaxial tensile tests conducted at various tensile rates. Based on this constitutive model, numerical simulations were performed to examine the effects of initial modulus, impact rate, and cavity confining pressure on the failure modes of propellants containing cavities with radii from 40 to 100 mm. The results show that the simulation’s force–displacement curve agrees well with the test. The simulation accurately captures the propellant’s transition from elastic–plastic plateau at low rates to elastic response at high rates. The prediction error for the maximum tensile force is less than 5%. For cavities of 80 mm and 100 mm, local stress concentration causes damage to the inner wall, followed by rapid cavity extrusion, collapse, and possible cross-shaped matrix fracture. However, cavities of 40 mm and 60 mm show greater stability, experiencing only volume compression, which rarely causes overall damage. When the propellant’s initial modulus is higher than 24 MPa, damage propagation in large cavities over 80 mm is suppressed. A low modulus worsens structural deformation. At low impact velocity, cavity compression is significant, and the structure remains conformal. At high impact velocity (4000 MPa/s), the cavity stays conformal, the matrix collapses, and the damage value decreases. For 60 mm cavities, damage is localized, and the overall structure is most stable within a confining pressure of 5 to 9.5 MPa. This study clarifies the interaction between engineering parameters and cavity size, providing a basis for optimizing the safety of the propellant structure. Full article

As a key roadside safety feature, longitudinal guardrail steel barriers are purposefully designed to contain and redirect errant vehicles to prevent roadway departure, dissipate impact energy through plastic deformation, and reduce the severity of vehicle crashes. Nevertheless, these systems should be carefully designed and assessed, as localized rupturing, especially near splice or impact locations, can lead to catastrophic failures, compromising vehicle containment, violating crash safety standards, and ultimately jeopardizing the safety of occupants and other road users. Before conducting full-scale crash testing, finite element analysis (FEA) tools are widely employed to evaluate the design efficiency, optimize system configurations, and preemptively identify potential failure modes prior to expensive physical crash testing. To accurately assess system behavior, calibrated material models and precise failure criteria must be utilized in these simulations. Despite the existence of numerous failure criteria and material models, the material characteristics of AASHTO M-180 guardrail steel have not been fully investigated. This paper significantly advances the FE modeling of ductile fracture in guardrail steel, addressing a critical need within the roadside safety community. This study formulates stress-state-dependent failure criteria and proposes advanced material modeling techniques. Extensive experimental testing was conducted on steel specimens having various triaxiality and Lode parameter values to reproduce a wide spectrum of complex, three-dimensional stress-state loading conditions. The test results were then used to identify material properties and construct a failure surface. Subsequent FEA, which incorporated the Generalized Incremental Stress-State-Dependent Damage Model (GISSMO) in conjunction with two LS-DYNA material models, illustrates the capability of the developed surface and material input parameters to predict material behavior under various stress states accurately. A parametric study was completed to further validate the proposed models, highlighting their robustness and reliability. Full article

An intelligent optimization technology was proposed to mitigate prevalent multi-defects, particularly failure, wrinkling, and springback in sheet metal forming. This method combined deep neural networks (DNNs), genetic algorithms (GAs), and Monte Carlo simulation (MCS), collectively as DNN-GA-MCS. Our primary aim was to determine intricate process parameters while elucidating the intricate relationship between processing methodologies and material properties. To achieve this goal, variable blank holder force (VBHF) trajectories were implemented into five sub-stroke steps, facilitating adjustments to the blank holder force via numerical simulations with an oil pan model. The Forming Limit Diagram (FLD) predicted by machine learning algorithms based on the Generalized Incremental Stress State Dependent Damage (GISSMO) model provided a robust framework for evaluating sheet failure dynamics during the stamping process. Numerical results confirmed significant improvements in formed quality: compared with the average value of training sets, the improvements of 18.89%, 13.59%, and 14.26% are achieved in failure, wrinkling, and springback; in the purposed two-segmented mode VBHF case application, the average value of three defects is improved by 12.62%, and the total summation of VBHF is reduced by 14.07%. Statistical methodologies grounded in material flow analysis were applied, accompanied by the proposal of distinctive optimization strategies for the die structure aimed at enhancing material flow efficiency. In conclusion, our advanced methodology exhibits considerable potential to improve sheet metal forming processes, highlighting its significant effect on defect reduction. Full article

Hot-stamped steel is currently the most widely used lightweight material in automobiles, and accurately predicting its failure risk during the simulation is a bottleneck problem in the automobile industry. In this study, the fracture failure behavior of the hot-stamped PHS2000 steel manufactured by Ben Gang Group (Benxi, China) is investigated by experiments and simulation. Static tension and high-speed tension tests are conducted to obtain the elastic-plastic stress-strain relations, and a Swift + Hockett–Sherby model is proposed to describe the hardening behavior under static and high-speed loads. Tests under five kinds of stress states, namely static shear, static tensile shear, notched static tension, center-hole static tension, and static punching, are conducted to obtain the ultimate fracture strains under different stress states for establishing a failure model. The finite element method (FEM) is used to inversely achieve the fracture parameters of the material, and the GISSMO model in LS-Dyna is adopted to describe the fracture characteristics of the material. A fracture card is further established for simulation analysis by combining fracture characteristics with high-speed tension curves and simultaneously loading size effect curves of meshes. Finally, the card is applied in the simulation of the three-point bending test. High-precision results of fracture simulation matching the experimental results are obtained. This research proves that the proposed fracture card is accurate and can be widely used in the simulation of fracture behaviors of the hot-stamped PHS2000 steel. Full article

Increasing product requirements in the mechanical engineering industry and efforts to reduce time-to-market demand highly accurate and resource-efficient finite element simulations. The required parameter calibration of the material models is becoming increasingly challenging with regard to the growing variety of available materials. Besides the classical iterative optimization-based parameter identification method, novel machine learning-based methods represent promising alternatives, especially in terms of efficiency. However, the machine learning algorithms, architectures, and settings significantly affect the resulting accuracy. This work presents a comparative study of different machine learning algorithms based on virtual datasets with varying settings for the direct inverse material parameter identification method. Multilayer perceptrons, convolutional neural networks, and Bayesian neural networks are compared; and their resulting prediction accuracies are investigated. Furthermore, advantages in material parameter identification by uncertainty quantification using the Bayesian probabilistic approach are examined and discussed. The results show increased prediction quality when using convolutional neural networks instead of multilayer perceptrons. The assessment of the aleatoric and epistemic uncertainties when using Bayesian neural networks also demonstrated advantages in evaluating the reliability of the predicted material parameters and their influences on the subsequent finite element simulations. Full article

The quality of simulation results significantly depends on the accuracy of the material model and parameters. In high strain rate forming processes such as, e.g., electromagnetic forming or adiabatic blanking, two superposing and opposing effects influence the flow stress of the material: strain rate hardening and thermal softening due to adiabatic heating. The presented work contributes to understanding these influences better by quantifying the adiabatic heating of the workpiece during deformation and failure under high-speed loading. For this purpose, uniaxial tensile tests at different high strain rates are analyzed experimentally and numerically. A special focus of the analysis of the tensile test was put on identifying a characteristic time- and position-dependent strain rate. In the experiments, in addition to the measurement of the force and elongation, the temperature in the fracture region is recorded using a thermal camera and a pyrometer for higher strain rates. Simulations are carried out in LS-Dyna using the GISSMO model as a damage and failure model. Both experimental and simulated results showed good agreement regarding the time-dependent force-displacement curve and the maximum occurring temperature. Full article

A neural network (NN)-based method is presented in this paper which allows the identification of parameters for material cards used in Finite Element simulations. Contrary to the conventionally used computationally intensive material parameter identification (MPI) by numerical optimization with internal or commercial software, a machine learning (ML)-based method is time saving when used repeatedly. Within this article, a self-developed ML-based Python framework is presented, which offers advantages, especially in the development of structural components in early development phases. In this procedure, different machine learning methods are used and adapted to the specific MPI problem considered herein. Using the developed NN-based and the common optimization-based method with LS-OPT, the material parameters of the LS-DYNA material card MAT_187_SAMP-1 and the failure model GISSMO were exemplarily calibrated for a virtually generated test dataset. Parameters for the description of elasticity, plasticity, tension–compression asymmetry, variable plastic Poisson’s ratio (VPPR), strain rate dependency and failure were taken into account. The focus of this paper is on performing a comparative study of the two different MPI methods with varying settings (algorithms, hyperparameters, etc.). Furthermore, the applicability of the NN-based procedure for the specific usage of both material cards was investigated. The studies reveal the general applicability for the calibration of a complex material card by the example of the used MAT_187_SAMP-1. Full article

This article discusses the fracture modelling accuracy of strain-driven ductile fracture models when introducing damage of high strength sheet steel. Numerical modelling of well-known fracture mechanical tests was conducted using a failure and damage model to control damage and fracture evolution. A thorough validation of the simulation results was conducted against results from laboratory testing. Such validations show that the damage and failure model is suited for modelling of material failure and fracture evolution of specimens without damage. However, pre-damaged specimens show less correlation as the damage and failure model over-predicts the displacement at crack initiation with an average of 28%. Consequently, the results in this article show the need for an extension of the damage and failure model that accounts for the fracture mechanisms at the crack tip. Such extension would aid in the improvement of fracture mechanical testing procedures and the modelling of high strength sheet metal manufacturing, as several sheet manufacturing processes are defined by material fracture. Full article

High-voltage busbars are important electrical components in today’s electric vehicle battery systems. Mechanical deformations in the event of a vehicle crash could lead to electrical busbar failure and hazardous situations that pose a threat to people and surroundings. In order to ensure a safe application of busbars, this study investigated their mechanical behavior under high strain rate loading using a split Hopkinson pressure bar. Two different types of high-voltage busbars, consisting of a polyamide 12 and a glass-fiber-reinforced (30%) polyamide 6 insulation layer, were tested. Additionally, the test setup included a 1000 V electrical short circuit measurement to link the electrical with the mechanical failure. It was found that the polyamide 12 insulated busbars’ safety regarding insulation failure increases at high loading speed compared to quasi-static measurements. On the contrary, the fiber-reinforced polyamide 6 insulated busbar revealed highly brittle material behavior leading to reduced bearable loads and intrusions. Finally, the split Hopkinson pressure bar tests were simulated. Existing material models for the thermoplastics were complemented with an optimized generalized incremental stress state-dependent model (GISSMO) with strain rate dependency. A good agreement with the experimental behavior was achieved, although the absence of viscoelasticity in the underlying material models was notable. Full article

Heat-assisted forming processes are becoming increasingly important in the manufacturing of sheet metal parts for body-in-white applications. However, the non-isothermal nature of these processes leads to challenges in evaluating the forming limits, since established methods such as Forming Limit Curves (FLCs) only allow the assessment of critical forming strains for steady temperatures. For this reason, a temperature-dependent extension of the well-established GISSMO (Generalized Incremental Stress State Dependent Damage Model) fracture indicator framework is developed by the authors to predict forming failures under non-isothermal conditions. In this paper, a general approach to combine several isothermal FLCs within the temperature-extended GISSMO model into a temperature-dependent forming limit surface is investigated. The general capabilities of the model are tested in a coupled thermo-mechanical FEA using the example of warm forming of an AA5182-O sheet metal cross-die cup. The obtained results are then compared with state of the art of evaluation methods. By taking the strain and temperature path into account, GISSMO predicts greater drawing depths by up to $20\%$ than established methods. In this way the forming and so the lightweight potential of sheet metal parts can by fully exploited. Moreover, the risk and locus of failure can be evaluated directly on the part geometry by a contour plot. An additional advantage of the GISSMO model is the applicability for low triaxialities as well as the possibility to predict the materials behavior beyond necking up to ductile fracture. Full article

Automotive high-voltage busbars are critical electrical components in electric vehicle battery systems as they connect individual battery modules and form the connection to the vehicle’s powertrain. Therefore, a vehicle crash can pose a significant risk to safety by compromising busbar insulation, leading to electrical short circuits inside the battery. In turn, these can trigger thermal chain reactions in the cell modules of the battery pack. In order to ensure a safe design in future applications of busbars, this study investigated the mechanical behavior of busbars and their insulation. Our results indicated that crashlike compressive and bending loads lead to complex stress states resulting in failure of busbar insulation. To estimate the safety of busbars in the early development process using finite element simulations, suitable material models were evaluated. Failure of the insulation was included in the simulation using an optimized generalized incremental stress state dependent model (GISSMO). It was shown that sophisticated polymer models do not significantly improve the simulation quality. Finally, on the basis of the experimental and numerical results, we outline some putative approaches for increasing the safety of high-voltage busbars in electric vehicles, such as choosing the insulating layer material according to the range of expected mechanical loads. Full article

Acrylonitrile–Butadiene–Styrene (ABS) is a very significant and widely used amorphous thermoplastic that possesses high impact resistance, toughness, and heat resistance. Bending collapse is a predominant failure of polymeric structural members in the vehicle environment under angled and unsymmetrical collisions. Therefore, it becomes critical to investigate the flexural behavior of the ABS beam and find its energy absorption capabilities under a transverse loading scenario. Four-point bending tests were carried out at different strain rates and at two different span lengths to investigate the deformation behavior of ABS. This paper examines the influence of strain rate, friction coefficient, Generalized Incremental Stress-State MOdel (GISSMO) and Damage Initiation and Evolution (DIEM) damage models, yield surfaces, and the span length on the four-point flexural behavior of the ABS polymeric material. A Semi-Analytical material model (SAMP_1) in LSDYNA was utilized to numerically evaluate the behavior of ABS under four-point bending. From extensive investigative explorations, it was found that the flexural behavior of ABS is dependent upon the span length, loading strain rate, and friction coefficient between the specimen and the supports. The modeling of damage was successfully exemplified by using the inherent damage law of the SAMP-1 material model, GISSMO, and DIEM damage formulations. Full article

Owing to their outstanding strength, in recent years, there has been an increased use of advanced high-strength steel (AHSS) sheets in the automotive sector. Their low formability, however, poses a challenge to forming, and failure prediction requires accurate knowledge of its material behavior over a large strain range up to ultimate failure, in order to exploit their full capacity in forming, but also in crash events. For predicting the fracture of an adjusting guide loaded by a pin, first, the force–displacement data are extracted from tensile tests using DP980 specimens of diverse shapes, all of which represent a certain loading mode. Using digital image correlation (DIC), we determine the stress triaxialities corresponding to the diverse loading conditions and establish the triaxiality failure diagram (TFD), which serves as the basis for the generalized incremental stress state-dependent damage model (GISSMO). Then, the damage parameters (necking and failure strains) are determined for each loading mode by reverse engineering-based optimization. Finally, these damage parameters are applied to the adjusting guide, and the numerical results are compared with the experimental data. Comparisons of the external load–displacement curves and the local equivalent strain distributions show that using the damage model with the material parameters obtained in here allows for the accurate prediction of the guide’s failure behavior, and the applicability of GISSMO to complex loading cases. Full article

This study examines an extended method to obtain the parameters in the Generalized Incremental Stress State Dependent Damage (GISSMO) model. This method is based on an iterative Finite Element Method (FEM) method aiming at predicting the fracture behavior considering softening and failure. A large number of experimental tests have been conducted on four different alloys (7003 aluminum alloy, ADC12 aluminum alloy, ZK60 magnesium alloy and 20CrMnTiH Steel), here considering tests that span a wide range of stress triaxiality. The proposed method is compared with the two existing methods. Results show that the new extended Iterative FEM method gives the good estimate of the fracture behaviors for all four alloys considered. Full article

In the current research, the use of tailor-welded blanks (TWBs) comprising Usibor^® 1500-AS laser welded to more ductile Ductibor^® 500-AS is considered. The TWBs were hot stamped to form top-hat cross-section channels with axially tailored properties. Axial crush rails were assembled by spot welding together two of these hot stamped channels along their flanges. The tailored rails were crush tested under dynamic (crash) and quasi-static conditions using an 855 kg crash sled facility at 10.6 m/s impact speed, and a 670 kN servo-hydraulic press at 0.5 mm/s, respectively. Non-tailored channels composed entirely of Ductibor^® 500-AS were also tested for base material characterization and as a comparison to the tailored conditions. Numerical models of the crash experiments were developed. The material models include measured fracture loci using the generalized incremental stress state dependent damage model (GISSMO), with rate sensitive constitutive behavior. Spot weld failure was also considered based on tests of spot welded coupons. The accuracy of the predicted force-displacement and energy absorption response, extent of parent metal cracking, and extent of weld failure are evaluated in comparison to the experiments. The difference in response between quasi-static and dynamic testing is also evaluated. Full article