International Journal of Material Forming最新文献

This study comprehensively examined the tensile properties and damage evolution behavior of AA2024/AA7075 dissimilar alloy Friction Stir Welding (FSW) joints using the Gurson-Tvergaard-Needleman (GTN) damage model. Through integrating tensile testing, microscopic characterization, and statistical analysis, the GTN model parameters for various welding zones were accurately calibrated. A multi-region coupled refined finite element model was established. The stress-strain curves obtained from FE simulation exhibited excellent consistency with the experimental results, thereby validating the accuracy of the proposed model. It was found that the microstructural inhomogeneity of the welded joint significantly influenced the damage evolution. Specifically, the Advancing Side of Heat Affected Zone (AS-HAZ), characterized by grain coarsening and stress concentration, emerged as the primary crack initiation region. The fracture mode of the joint exhibited a mixed ductile-brittle nature, wherein second-phase particles played a critical role by promoting void nucleation and accelerating crack propagation. Damage variables rapidly accumulated within the HAZ and propagated from the center to the edge of the cross-section along the direction of maximum shear stress, eventually leading to fracture. This study clarified the damage evolution mechanism of FSW joints, providing a quantitative theoretical basis for process optimization and performance improvement of the joints.

Cold-forming broaching with a shaped tool represents a contemporary method for forming the geometry of internal surfaces with a periodic profile via plastic deformation. A key limitation of this process is the progressive depletion of the material’s plasticity during groove formation, which leads to the degradation of mechanical strength properties. This study presents an experimental investigation into the mechanics of deformation, specifically examining the influence of tool geometry on the parameters governing deformation-induced strengthening of the material, as well as assessing the residual plasticity during the formation of internal grooves. Fracture work calculations for grooved specimens produced via сold forming broaching revealed that the specific fracture energy decreases with reductions in both the tool tooth sharpening angle and the base width of the trapezoidal profile. In order to accurately define сold forming broaching process parameters for different materials, deformation paths were constructed for the most critical points within the workpiece. The stress–strain state was evaluated using the LS-DYNA finite element software. The study establishes the impact of both the inclination angle of the tool tooth and the base dimension of its trapezoidal section on the residual plasticity resource of the workpiece.

Based on the numerical thermal-fluid coupling simulation and experiments of laser melting deposition(LMD), microstructure growth and evolution characteristics of the molten pool at different scanning speeds were investigated. Integrating experimental metallographic observations with quantitative analysis on shape control factor(G/R), cooling rate(G×R) and volume fraction of the equiaxed grains(φ) derived from the numerical temperature gradient(G) and solidification rate(R), the molten pool is divided into bottom, middle and top regions, and the grain type, grain size as well as grain transformation in each region are investigated separately and integrally. When increasing the scanning speed, the length of dendrites in the direction of temperature gradient decreases and it splits into smaller grain structures, and the grains are refined to be thinner and smaller, and therefore the microhardness of the deposition layer is improved. Scanning speed is an effectively controllable process parameter in LMD that affects G and R, which are the critical factors to be regulated for the targeted microstructures and mechanical properties. By qualitative and quantitative discussion on solidification parameters, microstructure and microhardness, this integrated numerical and experimental analysis effectively relates process parameter, microstructure and mechanical property with intrinsic mechanisms.

Modeling a material in its manufacturing process saves resources by optimizing the process conditions without using the material in trial-and-error tests. A finite element material model is in development to predict the deformation of highly-aligned discontinuous fiber prepregs in thermoforming. Unlike other high-performance composite prepregs, this material can stretch even in the fiber direction, due to the short fibers flowing and repositioning in the sheet. During stretching, this material exhibits highly-anisotropic, viscous behavior with a dominant viscosity in the fiber direction, as well as a unique stress-strain response called strain softening in which the material viscosity reduces with increasing strain. Both of these behaviors are inherently unstable: the material can stretch with little resistance in the transverse direction, and the more it stretches longitudinally, the easier it can stretch further due to strain softening. An equation was developed that describes this material’s extension behavior and implemented into the finite element solver AniForm. The parameters that describe the stretching behavior were derived from axial extension tests performed on TuFF made of IM7 carbon fibers and 977-3 thermoset resin under one set of process conditions (temperature, strain rate). The model was verified by matching the experimental data in longitudinal extension. The model was then validated by accurately predicting the extension-dominated deformation of a 4-ply laminate in gas-pressure bladder molding through a die similar to realistic part forming. The strain state on the surface ply was tracked in-situ using a DIC setup during forming and the results were compared with the AniForm simulation in which the constitutive equation that described the highly-aligned discontinuous fiber prepreg behavior was implemented.

Aiming at the nonlinear characteristics of the driver head formation process in interference-fit riveting, the difficulty in establishing correlations between driver head dimension and riveting force, and the inability of existing methods to properly guide riveting force determination for joints with different process parameters, this paper proposes a theoretical model based on metal plastic forming mechanics theory to predict the required riveting force for achieving standard driver head dimensions. Finite element simulation technology and riveting experiments were employed to obtain driver head dimensions of Φ4 × 9 mm and Φ5 × 10 mm 2A10 semi-spherical rivets under different riveting forces. The results show that the theoretical riveting forces calculated from simulation-measured driver head dimensions have average errors of 1.41% and 1.89% compared to set values. The theoretical riveting forces calculated from experimentally measured driver head height and maximum diameter initially exhibit larger errors. After introducing an interference correction coefficient, the average errors decrease to 3.01% and 0.81% respectively, demonstrating good agreement.

Superplastic forming technology exhibits broad application prospects in the manufacturing of aluminum alloy components, with advantages including high dimensional accuracy, low residual stresses, and minimal rebound, it enables substantial weight reduction without compromising strength while even enhancing stiffness. However, formed components still face challenges such as uneven wall thickness and cracking. This paper analyses the rheological behavior of 5083 aluminum alloy plates at temperatures of 470 °C, 490 °C, and 510 °C and strain rates of 0.01 s⁻¹, 0.004 s⁻¹, 0.0075 s⁻¹, and 0.0006 s⁻¹. Using ABAQUS software, numerical simulations of superplastic forming for 5083 aluminum alloy cup shell were conducted. To maximize the thinning rate of cup shell parts, response surface methodology was employed to perform numerical simulations of their preforming process, revealing the relationship between superplastic forming process parameters and the minimum thickness of cup shell, yielding the optimal combination of superplastic forming process parameters for 5083 aluminum alloy; furthermore, the forming height under varying rated pressures was analyzed, and forming tests were conducted using the optimal parameters to verify the accuracy of the experimental and simulation results. Finally, mechanical property evaluations at different locations of formed components were performed via tensile and hardness testing, while microstructural characterization was carried out via electron backscatter diffraction (EBSD). Results indicated that the superplastic forming cup shell parts demonstrating uniformly refined microstructures and high forming quality. These findings offer critical insights for manufacturing more complex aluminum alloy superplastic forming prats in aerospace and transportation industries.

Graphical abstract

To increase the ratio of manufactured recycled poly(ethylene terephthalate) (rPET) bottle, the control of ISBM (Injection Stretch Blow Moulding) process must account for varying mechanical and thermal properties of mechanically recycled PET. Calibration and optimization of the process have been successfully realized in past works, but they use costly PDE-based models. Therefore, they are difficult to use online for applications where the process parameters need to be regularly adjusted, i.e. whenever a new batch of preforms made of recycled PET is considered. To address this, a meta-algorithm is proposed to replace the PDE-based digital twin. A gaussian process regression is trained offline using the PDE-based digital twin results in the process’s variabilities range. Using the internal pressure history of a test ISBM operation, our strategy allows us to obtain calibrated results without solving PDEs online. To show the capability of the methodology, a simplified process and its associated parametric uncertainties are enunciated. Finite element simulations of the ISBM process where the properties follow a multivariate Gaussian distribution are used to realize the Gaussian process regression. The quality of the digital twin’s predictions is assessed. Then, an illustrative example is presented, demonstrating the use of the digital twin predictions to optimize the thickness distribution of a bottle following the blowing process.

The study investigates the effects of process parameters for Electrical Discharge Machining (EDM) on the machining performance of hard materials, such as ceramic matrix composites (CMC). For difficult-to-machine materials (Silicon Nitride-Titanium Nitride), EDM provides benefits including low machining force and affordable equipment. To improve control parameters, like discharge current, pulse-on (Pon) and pulse-off times, and dielectric pressure, experiments were carried out utilizing a complete factorial method and Taguchi L₂₅ Orthogonal Array. Significant results were examined, including geometric tolerances, surface roughness, electrode wear rate (EWR), and material removal rate (MRR). The most important components were found to be current and pulse-on time after correlations between input parameters and output features were established using Response Surface Methodology (RSM). Optimal Pareto solutions were found using Genetic Algorithm-based Particle Swarm Optimization (GAPSO), which was validated by confirmation studies. The findings showed notable gains in machining efficiency, such as shorter machining times, higher rates of MRR (0.0118 g/min), decreased rates of EWR (0.001 g/min), and better geometric tolerances to roughness (3.108 μm). The outcome of a global optimization that factored in all seven replies is also shown According to these results, EDM may be used for intricate ceramic parts with ideal process parameters, advancing contemporary manufacturing sectors.

Industrial sheet metal components often have complex geometries with both concave and convex features. For small batch sizes, such components can be manufactured by incremental sheet forming, using the pressure of an active medium underneath the workpiece to create the convex feature. However, the additional load superimposed by the pressure causes instability and renders the process more prone to failure, in particular to cracking of the workpiece. The reliability of the manufacturing process could be improved if the occurrence of failure were predictable and thus preventable. To achieve this goal, the trend of the forming forces and the change of the workpiece geometry prior to cracking are experimentally analyzed. Subsequently, the dataset obtained from the experiments is used to fit a model based on long short-term memory and on a sliding window approach. This model reliably predicts the probability of failure with an accuracy and recall of 0.97 and 0.89 respectively, demonstrating its potential for online monitoring of the manufacturing process.

Optimisation and machine learning techniques are increasingly used with net-shape composites forming methods like resin transfer moulding (RTM) for process monitoring, control, and defect detection. However, the standard finite element-control volume modelling approach used in these simulations is discontinuous with respect to model parameters and time. We demonstrate that the use of a neural network surrogate, designed with smooth activation layers, can approximate these models, while eliminating any nonsmooth or discontinuous behaviour. To illustrate the benefits of this method, we use Bayesian inference to predict permeability and race tracking strengths from pressure measurements, where the neural network prevents discontinuities from propagating to the posterior, forming smooth posterior distributions that incorporate the model approximation error.