International Journal of Material Forming最新文献

Although hot strip rolling is a fundamental process that has been established over the last decades, industry and science are continuously working on optimizing the rolling parameters and their influence on the final product. A major problem is that geometric errors in hot-rolled coils, which are subsequently processed in downstream processes such as cold rolling, can only be corrected to a limited extent afterwards. Consequently, data-driven methods, especially machine learning (ML), have become increasingly important for predicting key process and quality variables like strip thickness, width and the strip shape in hot strip mills (HSM). To provide a structured overview of the current state of research, this article presents a systematic literature review of ML-based prediction approaches addressing relevant quality and process parameters in HSM. The studies examined are categorized according to the predicted variables, plant sections, and applied modeling approaches, with a particular focus on their relevance to be deployed for potential process optimization. In a second step, representative prediction models are implemented for the main focus areas of the literature search, using publicly available HSM datasets. These are methodologically based on the essential part of the published literature, use common ML algorithms, and support the discussion on dataset dependencies, methodical limitations, and challenges in comparing reported prediction results across different studies. By combining literature insights with reference predictions, this work clarifies the role of ML-based prediction in hot strip rolling, outlines potentials and limitations in model deployment for process optimization and identifies key research gaps for future research.

This study deals with the white layer formation mechanism on GH4169 nickel-based superalloy machined surfaces during PCBN tool turning under high-pressure cooling (HPC). GH4169 is widely used in critical aerospace components for its superior high-temperature mechanical properties, corrosion resistance and microstructural stability, but machining-induced brittle white layer severely degrades component fatigue strength and service life. HPC has shown great potential to suppress white layer formation, yet its underlying mechanism and influencing rules remain unclear. To address this, we conducted 2D finite element cutting simulations and HPC turning experiments of GH4169. Simulations quantified the cutting thermo-mechanical loads via transient temperature, strain and strain rate fields of the machined surface. Experiments explored the effects of cutting speed and cooling pressure on surface work hardening, characterized the element distribution, phase composition and microstructure evolution of the white layer and matrix using EDS, XRD and EBSD, and finally quantified the influences of cutting parameters on white layer thickness. Results show that element diffusion, phase transformation and severe grain refinement occur in the white layer, with the coupling of phase transformation and grain refinement dominating its formation. Under HPC, white layer thickness increases with rising cutting speed and decreases with increasing cooling pressure.

In this study, the third-generation advanced high-strength 10Mn5 medium-manganese steel (medium-Mn steel) sheet was successfully used to stamp the structural parts of the automobile A-pillar beam reinforcement plate. The finite element (FEM) simulation of its stamping process was carried out, and the simulation and experimental results were compared in terms of forming state, thinning rate distribution and the equivalent plastic strain (EPS) distribution. A numerical simulation platform suitable for the forming process of medium-Mn steel was established. Different EPS regions in the stamped automobile structural part were selected as the research objects, the dynamic deformation law and the characteristics of phase transformation induced plasticity (TRIP) were investigated by correlating macroscopic strain with microscopic austenite phase evolution. The grid method and magnetic characterization were used to quantify the plastic deformation and austenite transformation in different regions of the formed part, and the relationship between the EPS and the austenite volume fraction was fitted, which was verified based on the OC model martensite transformation formula. A quantitative relationship between the amount of plastic deformation and microstructure evolution during dynamic deformation of medium-Mn steel was established. The 10Mn5 steel nanoindentation hardness was studied to measure the mechanical properties after forming. The relationship between EPS and material hardness was established, enabling the establishment of a three-dimensional evolution relationship among plastic deformation, deformation microstructure, and mechanical properties of medium-Mn steel structural parts during the stamping process.

Rotational molding, also known as rotomolding, is a widely used manufacturing process for composite tank liners. Developing this process to obtain high quality liners is usually a long and delicate step. The implementation of numerical simulation can facilitate and expedite its development. It is challenging to obtain information about polymer movement through experimental methods. In this paper, we present a two-dimensional thermo fluid simulation of rotomolding using the immersed boundary approach with the level set technique. Numerical simulation using the Eulerian approach with the finite element method with anisotropic mesh refinement and the immersed boundary method has proven to be a powerful tool for analyzing and optimizing the molding processes with the help of implicit boundary and adaptive anisotropic meshing techniques. The finite element method is a valuable tool that allows us to combine several physical elements. This approach enables us to identify critical process variables and optimize design choices. To verify our methodology, we first simulated a 2D cross section of the mold by incorporating a two phase flow problem involving the polymer/air interface.

Ultra-thin-walled microchannel structures are widely applied in fields such as micro-bioreactors and bipolar plates of hydrogen fuel cells. The soft mold roll forming process for fabricating microchannel structures offers the advantages of being non-damaging and highly efficient, yet it also presents drawbacks such as a low depth-to-width ratio and shape asymmetry defects. Therefore, this study proposes to introduce radial ultrasonic vibration into the soft mold roll forming process and investigates its effect on mitigating deformation defects of microchannel structures. The results reveal that under radial ultrasonic vibration, the peak contact pressure between the foil and the microstructured roller is significantly reduced, while the intrusion volume of the soft mold increases, leading to an enhanced depth-to-width ratio of the microchannels. The application of radial ultrasonic vibration induces a divergent characteristic in the synthetic vector of the soft mold, markedly reducing unilateral impact forces and thereby alleviating asymmetry defects. At an ultrasonic amplitude of 1.6 μm, the microchannel structure with a t/d of 1.5 achieved the maximum increase in depth-to-width ratio (13.4%) and the maximum reduction in asymmetry degree (8.9%). Ultrasonic vibration has no effect on the deformation of the crown region of the microchannels. Owing to stress penetration and impact rolling induced by ultrasonic vibration, the hardness and surface quality of the bending region are significantly improved, which is beneficial for extending the service life of microchannel structures. These findings provide a novel approach for the efficient and high-quality fabrication of microchannel structures.

Thin-walled polygonal tubes, particularly seamless square profiles, are widely used in heat exchange and energy transport due to their favorable strength-to-weight ratio and assembly efficiency. However, conventional manufacturing routes remain costly and inflexible, and the deformation mechanisms in incremental tube forming (ITF)—especially thinning and springback in the XY and XZ planes during circular-to-square transformation—are not yet well quantified. This study investigates these mechanisms through full-factorial ITF experiments on thin-walled copper tubes using a three-axis CNC machine with a ball-tip tool over a 16 mm forming length, supported by explicit LS-DYNA shell simulations and quadratic response surface modelling. Numerical predictions show strong agreement with experimental measurements of both responses, with thickness prediction errors (MAPE) in the range of 4–5% and springback prediction errors below 3%, respectively, along with corresponding mean absolute error (MAE) values. Statistical analysis reveals a clear hierarchy of process influence: maximum thinning is governed primarily by axial feed, whereas springback exhibits nonlinear multivariate behavior dominated by radial feed, followed by axial feed and tool velocity, with significant interaction effects. The quadratic models, highly predictive for springback and sufficiently accurate for thinning, enable desirability-based multi-objective optimization, identifying 650 mm/min velocity, 0.35 mm axial feed, and 0.25 mm radial feed as an optimal operating condition. This parameter set limits thinning to approximately 0.45, reduces springback in both planes to 0.69 and 0.60, and achieves an overall desirability of nearly 0.7, thereby establishing a validated process window for defect-sensitive ITF of square tubes.

Deformation-induced anisotropic hardening strongly influences springback, strain localisation, forming limits and unloading-sensitive response in metallic sheet materials, and must therefore be represented reliably in finite-element simulations of sheet-forming operations. Existing syntheses have clarified the phenomenology of load-path effects and catalogued broad classes of constitutive models, but they have generally devoted less attention to the practical questions that govern engineering adoption: which observed behaviours justify which model family, what test information is minimally required for calibration, and what implementation burden follows at the FE level. This article presents a critical narrative review of deformation-induced anisotropic hardening in metallic sheet materials from the viewpoint of model selection, calibration and finite-element deployment. Experimentally observed responses are reorganised into a three-layer framework linking observable hardening signatures, constitutive evolution modes and representative model families. Prior syntheses are positioned explicitly to define the distinct contribution of the present review in terms of calibration burden, identifiability, implementation readiness and decision-oriented model choice. Crystal-plasticity-based approaches are discussed as supporting virtual laboratories for mechanism interpretation and synthetic calibration data generation. Computational issues relevant to industrial deployment are reviewed in terms of local stress updates, consistent elastoplastic tangents and parameter-identification workflows. The scope is restricted to constitutive descriptions of anisotropy evolution in metallic sheet materials up to the onset of localised necking. The overall aim is to provide a practical map for choosing, calibrating and implementing anisotropic hardening models in sheet-forming simulations.

In this work, electromagnetic cold expansion process with high-speed loading was investigated though experiments and numerical simulations. A coupled electromagnetic–deformation numerical model was established and corresponding validation experiments were conducted with 7075-T651 aluminum alloy plates with pre-drilled holes (with interference range of 0.09375, 0.08025, 0.06707 and 0.05422, respectively) under a discharge energy of 9 kJ. A coupled electromagnetic–deformation numerical model was established and validated at the geometric level by comparison with experimental measurements. The model was then used to analyze deformation behavior and residual stress distribution under different interference levels. The results showed that the maximum relative errors between simulation and experiment were 5.74% and 0.66%, respectively, demonstrating reasonable predictive capability for geometric deformation. The peak magnetic pressure was 54.58 MPa and impact velocity 20 m/s during electromagnetic cold expansion. The equivalent plastic strain and maximum extrusion stress (1209.22 MPa) on the hole wall were higher at the inlet than outlet, decreasing with lower interference. The radius of inlet/outlet strain-defined plastic deformation zone were 4 mm and 10 mm. Inlet residual stress was smaller than that of outlet. The inlet radial residual tensile stress was 154.85 MPa, outlet compressive − 332.9 MPa, both diminishing with distance.

To address the forming–accuracy challenges of silica ceramic components fabricated via digital light processing (DLP), this study proposes a method for adjusting the absorption of incident light by silica ceramic slurries through the addition of organic dyes (curcumin, methyl chloride, and Sudan I), which significantly improves the printing accuracy. This study systematically investigates the effects of the content of different organic dyes on the rheological properties, curing behavior, and ultraviolet absorbance of silica ceramic slurries. The results show that adding organic dyes negligibly affects the rheological properties of silica ceramic slurries. For silica ceramic slurries containing curcumin and Sudan I, the organic dyes substantially increase the absorbance of the slurries. Consequently, even trace amounts of curcumin and Sudan I can significantly enhance the forming accuracy of silica ceramics. This study demonstrates that utilizing the selective light–absorption characteristics of organic dyes can suppress light scattering at its source, offering a straightforward and purity–preserving new route for fabricating high–precision, complex–structured SiO₂ ceramic components.