International Journal of Material Forming最新文献

Laser forming (LF) is an advanced non-contact manufacturing technique that utilizes laser energy to induce controlled thermal expansion and plastic deformation in metal sheets, enabling the shaping of high-strength and brittle materials with minimal residual stresses. The effectiveness of LF is governed by three primary mechanisms Temperature Gradient Mechanism (TGM), Buckling Mechanism (BM), and Upsetting Mechanism (UM)) which are influenced by process parameters such as laser power, scanning speed, beam diameter, and material properties. This review presents a comprehensive overview of recent advancements in LF, beginning with an analysis of the governing deformation mechanisms and their role in achieving precision and control. It then explores critical microstructural changes including grain refinement, phase transformations, and heat-affected zones (HAZ) that directly impact material behavior and performance. Building upon these foundational aspects, the article highlights current innovations in LF process enhancement through machine learning (ML)-based optimization, real-time thermal feedback, and adaptive control strategies. Challenges such as edge effects, residual stresses, and process repeatability are discussed, along with mitigation approaches Like forced cooling and adaptive scanning. Experimental findings show that forced cooling can increase the bending angle by up to 35.2% and improve energy efficiency by 22.14%. The review Further examines the application of computational models such as ANNs, SVMs, and GAs in predicting bend angles and optimizing process parameters. ANN-based models, for instance, have achieved prediction accuracies of up to 98.9%. The AI tools offer a holistic perspective on future research directions aimed at enhancing process sustainability and broader industrial adoption.

This study systematically investigated the mechanical hammer forming of 7075 aluminum alloy driven by a voice coil motor through experiments and simulations, focusing on the effects of hammering force, offset distance, and thickness on forming behavior and surface quality. Parameter optimization, theoretical modeling of stress–deformation, stress relaxation analysis, and multi-contour plate forming were also explored. Results showed that increasing the force from 35 N to 65 N raised the maximum arc height by 53%, extending the offset distance from 1 mm to 1.6 mm increased it by 29%, and raising thickness from 2 mm to 5 mm yielded a 110% rise, identifying thickness as the dominant factor. Surface waviness and roughness were strongly influenced by force and offset distance but only slightly by thickness, with higher force and smaller offset distance leading to poorer quality. Offset distance most affected surface hardness, while thickness had the least influence. A BP neural network optimization identified optimal parameters (55 N, 1.2 mm, 3 mm) balancing deformation and surface quality. Furthermore, an arc height model was established to correlate residual stress redistribution with deformation, and stress relaxation was described using an exponential decay model with extracted relaxation time constants (τ). Finally, multi-contour plate forming was demonstrated through trajectory design, providing a reference for correcting deformed thin-walled parts.

This study investigates the effects of laser power, welding speed, and welding current on melt pool fluid dynamics, Keyhole stability, and porosity by developing a multiphysics coupled numerical model and validating it with high-speed imaging experiments. A single-variable controlled experimental design was employed to address porosity defects encountered in the laser-TIG hybrid welding of 12 mm-thick Invar steel. The study found that increasing the laser power from 4 kW to 6 kW significantly raises the Keyhole collapse frequency and porosity. This is attributed to the increased recoil pressure and melt pool depth, which hinder bubble escape. Increasing the welding speed from 0.008 m/s to 0.05 m/s reduces porosity by enhancing the melt pool’s kinetic energy, which offsets interfacial forces, and by lowering heat input to Maintain Keyhole stability. Welding current exhibits a nonlinear effect on porosity, In the range of 100 A to 150 A, electromagnetic forces enhance melt pool stability and extend solidification time, promoting bubble escape. However, when the current increases from 150 A to 225 A, excessive heat input leads to local overheating and intensifies Keyhole instability. Finally, 1,000 frames of keyhole morphology during the stable stage were extracted, and analysis of keyhole collapse frequency was conducted to reveal the influence of welding parameters on porosity.

In this research, the simulation and experimental control of crown formation in the rolling process of ultra-thin silver strip is comprehensively investigated, which is crucial for the applications in high-voltage circuit protection, particularly in new energy vehicles. The objective was to identify key factors influencing crown formation and develop control strategies to ensure product quality and consistency. A finite element model was constructed to simulate the fourteen-stand rolling mill system, integrating static and dynamic analyses to evaluate the rolled crown value under various process parameters. The simulation results were validated against actual rolling data, confirming the model’s accuracy. Factors affecting crown formation were identified through simulation and analysis, including reduction per pass, friction coefficient, lateral displacement of intermediate rolls, strip entry position, input strip crown, material strength, strip width, and entry/exit tension. Empirical formulas were derived to predict crown values based on these parameters, providing a scientific basis for process optimization. Optimization recommendations included adjusting intermediate roller lateral displacement, annealing before the final rolling pass, selecting narrow strip, implementing progressive reduction per pass and controlling strip entry position. An experimental investigation validated the simulation findings, Maintaining thickness tolerance within a stringent standard of less than 1 micron, demonstrating the feasibility of ultra-high precision rolling for silver strips. In conclusion, this study significantly contributes to the understanding and control of crown formation in ultra-thin silver strip rolling, offering a scientific basis for optimizing the rolling process and improving product quality. The research outcomes are expected to influence the development of advanced rolling technologies and the manufacturing of high-performance strips for various industries.

The paper derives from a simple question that the authors have asked themselves when attending conferences and reading articles on modelling of metal forming processes: is numerical modelling based on FEA still innovative? Are the proposed results able to provide a further effective enhancement to scientific knowledge? And how huge was the effort to obtain such an eventual enhancement? Starting with these questions, the authors applied some basic concepts of Innovation Theory to the last forty years of numerical modelling of forming processes and understood that this technology has reached its natural limit: only small enhancements of modelling performances are obtained despite quite big efforts. Also, research topic trends analysis was performed within ESAFORM community through text mining approaches. Now, to answer the research questions still open, a disruptive discontinuity is necessary, aimed at assessing a new master modelling technology.

This study presents a surrogate model based on the convolutional U-Net architecture to predict the thermal field in a carbon fibre-reinforced thermoplastic tape at the microscale during brief and localized heating. Leveraging microstructure data within a machine learning framework, the proposed model aims to enhance the accuracy of temperature field predictions at a low computational cost. The incorporation of a co-attention mechanism to handle image channels of different nature significantly improves precision, resulting in a strong correlation between the model’s predictions and the ground truth obtained from the numerical solution of the heat equation. This capability enables rapid assessment of diverse microstructures, facilitating optimization and real-time applications in manufacturing settings.

Hydrogen-powered vehicles are set to become a viable alternative for many of the cars currently on the roads. However, even if hydrogen offers a promising eco-friendly solution for the energy transition, several issues related to its storage and delivery need to be resolved in order to predict its wide use in both stationary and automotive applications. Hydrogen has the lowest volumetric energy density of all commonly used fuels (0.01079 MJ/L at atmospheric pressure). However, compression emerges as a direct and effective solution to this issue, with high pressures capable of significantly enhancing hydrogen's energy density, thereby augmenting its practicality. The energy densities achievable under high pressure are indeed impressive, making hydrogen highly practical. In mobile applications, hydrogen is typically stored as a gas in high-pressure composite overwrapped pressure vessels (COPVs). To achieve optimal functionality for high-pressure applications, two fundamental objectives must be met: ensuring exceptional structural integrity and maximizing gas impermeability. The commercialization of these vessels therefore presents a range of engineering challenges, including the development of advanced manufacturing techniques, the enhancement of structural properties, and the selection of appropriate materials, among others. The trend towards high-pressure hydrogen storage tanks is characterized by low cost, lightweight, and favorable safety performance. Consequently, the development of an efficient, sustainable, and safe high-pressure hydrogen storage method is a crucial focus of recent research, aiming to optimize hydrogen's utility in various applications. This review summarizes the latest developments in the most established hydrogen compression technologies.

Most research objects of ring rolling process are axisymmetric ring forgings. With the increasing requirements for energy efficiencies and material savings in the industry, the demand for the type of non-axisymmetric rings is becoming increasingly urgent. In response to this demand, the research proposed a new rolling process by adding a constraint die set in conventional ring rolling machine for the Rings with Island Bosses on Outer Surface. This process includes two stages: 1) the diameter growth stage, ring blank is first rolled between die and mandrel until the outer surface of ring contact around at the inner surface of die; 2) the boss growth stage, the material is gradually extruded into the holes on the inner surface of die. Finite element simulation and physical experiments were carried out to confirm that the proposed rolling process was practicable on conventional rolling machine. By design of experiment of this process, folding and shrinkage defects were studied to determine the range of rolling process parameters. The factors, such as shape, area and fillet radius of boss, ring wall thickness and friction, which affect height of boss in boss growth stage, were investigated. The proposed rolling process is practicable for the Rings with Island Bosses on Outer Surface.

Dissimilar metal laminated composite plates are highly valuable in various applications due to the excellent properties of their constituent materials. However, composite plates produced through roll bonding often exhibit bending, challenging their practical use. Analyzing the underlying causes of bending in roll-bonded composite plates and developing optimized processes to mitigate this issue hold significant theoretical and practical importance. In this study, the commercial finite element simulation software ABAQUS was utilized to simulate the deformation behavior of 6061 aluminum alloy and pure titanium TA1 during symmetric rolling bonding. The effects of plastic deformation differences between dissimilar metals, uneven stress distribution across the thickness, and elastic recovery after rolling on the bending of composite plates were systematically investigated. Finite element models were established for titanium/aluminum composite plates under asymmetric rolling conditions, including identical-diameter differential-speed rolling and differential-diameter identical-speed rolling. The findings reveal that both differential diameters and differential speeds effectively mitigate the bending phenomena caused by deformation incompatibility between dissimilar materials and the uneven stress distribution. Based on these findings, an optimized differential-diameter and differential-speed rolling model was developed. With a low roll diameter of 210 mm and a speed ratio of 1.09, the flattest roll-bonded titanium/aluminum composite plates were achieved compared to other conditions. Additionally, the results of rolling experiments confirmed the high accuracy of the finite element simulations. This study provides valuable guidance for improving the bending behavior of composite plates made from metals with significant performance differences.

This study explores predictive modeling of mechanical properties tensile strength, flexural strength, impact strength, and hardness of natural fiber and filler cashew nutshell waste, sugarcane waste, and polyethylene terephthalate (PET) waste was used as fillers composite materials based on advanced machine learning algorithms. The experiment composition weigth percentages (0%, 5%, 10%, and 15%) were obtained through the literature and intermediate and longer compositions (1%–16%) were approximated using Artificial Neural Network (ANN) and Support Vector Regression (SVR) models. The performance of every algorithm was compared based on statistical measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The ANN model exhibited better prediction performance with R² values greater than 0.99 in every property, with the lowest error rates, representing high reliability in interpolation as well as extrapolation. SVR also worked satisfactorily, albeit with marginally increased deviations in calculated values at some composition ranges. The work establishes machine learning models specifically ANN as an effective means of simulating composite materials’ mechanical behavior, and an effective method of material design optimization that can be done with less experimental labor.