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This paper presents the experimental and numerical results of a study on producing axisymmetric parts made of the C60-grade steel by skew rolling. The experimental part of this study involved conducting the skew rolling process with varying parameters, including the forming angle α, tool angle θ, chuck velocity Vu, and reduction ratio δ. Their effect on the quality of produced parts was examined and described by the roughness parameter Ra. Numerical calculations involved the use of machine learning models to predict the quality class of produced parts. The highest prediction accuracy of the results was obtained with the random forest and logistic regression models. Metrics such as precision, recall and accuracy were used to evaluate the performance of individual models. Confusion matrices and ROC curves were also employed to illustrate the performance of the classification models. The results of this study will make it possible to prevent the formation of spiral grooves on the circumference of steel parts during the rolling process. [ABSTRACT FROM AUTHOR]

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In this article, electric vehicles (EVs) are generally introduced, and the benefits of shifting from internal combustion engine (ICE) automobiles toward EVs are discussed. Batteries, as the core technology of EVs, are then addressed, and various battery types are reviewed. Next, the critical role of copper as one of the primary materials essential for various EV components, as well as the expected growth in its demand in the near future, are discussed. Following that, the significance of using advanced high-strength steels (AHSSs) in the body structure of EVs to reduce weight while maintaining safety requirements is emphasized, and different types of AHSSs are generally mentioned. Subsequently, common metal forming and joining methods for manufacturing and assembling EV components are reviewed. After that, the importance of proper EV constructional design is highlighted, and the strategic battery pack placement for safety and effective weight distribution is highlighted. The importance of using lightweight materials that offer high strength-to-weight ratios in the body structure of EVs and their benefits for improving the efficiency of EVs through increasing the driving range are also outlined. In addition, the importance of responsible end-of-life actions, including recycling or responsible disposal of EV components after their functional lifespan, is also overviewed. Finally, useful strategies for facilitating wider adoption of EVs are discussed, and alternative options for addressing air pollution and global warming are mentioned. [ABSTRACT FROM AUTHOR]

Correct design of the sheet metal forming process requires knowledge of the friction phenomenon occurring in various areas of the drawpiece. Additionally, the friction at the drawbead is decisive to ensure that the sheet flows in the desired direction. This article presents the results of experimental tests enabling the determination of the coefficient of friction at the drawbead and using a specially designed tribometer. The test material was a DC04 carbon steel sheet. The tests were carried out for different orientations of the samples in relation to the sheet rolling direction, different drawbead heights, different lubrication conditions and different average roughnesses of the countersamples. According to the aim of this work, the Features Importance analysis, conducted using the Gradient-Boosted Regression Trees algorithm, was used to find the influence of several parameter features on the coefficient of friction. The advantage of gradient-boosted decision trees is their ability to analyze complex relationships in the data and protect against overfitting. Another advantage is that there is no need for prior data processing. According to the best of the authors' knowledge, the effectiveness of gradient-boosted decision trees in analyzing the friction occurring in the drawbead in sheet metal forming has not been previously studied. To improve the accuracy of the model, five MinLeafs were applied to the regression tree, together with 500 ensembles utilized for learning the previously learned nodes, noting that the MinLeaf indicates the minimum number of leaf node observations. The least-squares-boosting technique, often known as LSBoost, is used to train a group of regression trees. Features Importance analysis has shown that the friction conditions (dry friction of lubricated conditions) had the most significant influence on the coefficient of friction, at 56.98%, followed by the drawbead height, at 23.41%, and the sample width, at 11.95%. The average surface roughness of rollers and sample orientation have the smallest impact on the value of the coefficient of friction at 6.09% and 1.57%, respectively. The dispersion and deviation observed for the testing dataset from the experimental data indicate the model's ability to predict the values of the coefficient of friction at a coefficient of determination of R2 = 0.972 and a mean-squared error of MSE = 0.000048. It was qualitatively found that in order to ensure the optimal (the lowest) coefficient of friction, it is necessary to control the friction conditions (use of lubricant) and the drawbead height. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the influence of different constitutive models on the accuracy of predicting spring-back in cold roll forming for pre-punched profiles. Finite element analysis was conducted using Abaqus, employing eight distinct constitutive models that varied in terms of yield criteria, yield function calibration, and elastic modulus degradation. To evaluate the impact of holes and their positions on spring-back, three samples made of St12 with a 1 mm thickness were used: a sample without holes, a profile with holes in the bending zone (on the bend), and a profile with holes close to the bending zone (near the bend). The results show that ignoring variation in elastic modulus has less influence on the accuracy of spring-back prediction for "near the bend" than the two other profiles. This case is explained by less change in elastic modulus during roll forming of the "near the bend" profile. Additionally, calibrating yield criteria based on R-values, such as Hill48_R and Yld89_R, and yield stress values result in more precise spring-back estimations for the "on the bend" and "near the bend" profiles, respectively. This approach proves superior when contrasted with the alternative calibration methods. Moreover, neglecting the effect of Young's modulus variation in Yld2000-2d and Hill48_S results in the lowest MAPE (mean absolute percentage error) of approximately 20% compared to other models. However, it is worth noting that Yld2000-2d underestimates the experimental values while Hill48_S tends to overestimate them. As a result, the most suitable constitutive model, considering elastic variation, is Yld2000-2d, with an average MAPE of 8% across all three samples. [ABSTRACT FROM AUTHOR]

This study explores the feasibility of using planar shock waves generated by vaporizing foil actuators (VFA) in microfabrication technologies. We conducted experiments to investigate the generation and propagation of these shock waves by detonating actuators at relatively low current densities. The measurements of discharge voltage and current, coupled with high-speed imaging, indicated that planar shock waves result from the convergence of multiple shock waves due to the non-uniform heating and vaporization of the actuator. These experiments also demonstrated that the pressure within the shock waves can be precisely characterized. In microscale embossing experiments, metal workpieces exhibited uniform forming, whereas polymeric workpieces demonstrated non-uniform forming. This distinction underscores the utility of polymer workpieces in elucidating the heterogeneity of explosions in VFA applications. The successful deformation of metal workpieces at the nanoscale further confirmed the potential of VFA for precise microfabrication. This research highlights the critical influence of material type on the outcomes of VFA-induced forming and offers significant insights into the dynamics of shock waves necessary for optimizing microfabrication processes. [ABSTRACT FROM AUTHOR]

Conventional rolling is a plastic deformation process that uses compression between two rolls to reduce material thickness and produce sheet/plane geometries. This deformation process modifies the material structure by generating texture, reducing the grain size, and strengthening the material. The rolling process can enhance the strength and hardness of lightweight materials while still preserving their inherent lightness. Lightweight metals like magnesium alloys tend to lack mechanical strength and hardness in load-bearing applications. The general rolling process is controlled by the thickness reduction, velocity of the rolls, and temperature. When held at a constant thickness reduction, each pass through the rolls introduces an increase in strain hardening, which could ultimately result in cracking, spallation, and other defects. This study is designed to optimize the rolling process by evaluating the effects of the strain rate, rather than the thickness reduction, as a process control parameter. [ABSTRACT FROM AUTHOR]

The Metal Die Support Incremental Sheet Forming (MDS-ISF) enhances the dimensional accuracy of produced items in many industrial applications. However, the procedure limits metal sheet formability due to the strain between the tool and mandrel. As a result, selecting the best MDS-ISF technique to improve formability is a critical problem in the manufacture of complicated components. This study aims at optimising the forming parameters of MDS-ISF by forming single-walled cones of fixed wall angle and forming height by using Taguchi Grey Relational Analysis. The forming parameters selected for experimentation are step depth, spindle speed, and feed rate at three different levels. The responses, such as average surface roughness and average thickness, were selected for multi-objective optimisation and ANOVA was also conducted to study the influence of each forming parameter on the output responses. Additionally, the wall angle, and thickness of the components were also measured. From ANOVA, step depth had the uppermost influence on the surface finish and thickness trailed by feed rate and spindle speed.

Unified constitutive equations have been developed to model the behaviour of metallic materials under various processing conditions. These constitutive equations usually take the form of a set of ordinary differential equations (ODEs), which must be solved thousands of times in a finite element (FE) process simulation. Thus, an efficient and reliable numerical integration method for large systems is crucial for solving this problem. However, in many constitutive equations, numerical stiffness is often present. This means that the stability requirements, rather than the accuracy, constrain the step size. Therefore, certain numerical methods become unsuitable when the required step size becomes unacceptably small. In this study, a series of mathematical analyses was performed to investigate the difficulties in the numerical integration of three sets of unified viscoplastic/creep constitutive equations. Based on an analysis of the current stiffness assessment methods, a novel index was introduced, that enables an accurate assessment of the stiffness of the ODE-type unified constitutive equations. A computational study was also conducted to benchmark several promising implicit numerical integration methods for viscoplastic/creep constitutive equations. This study can assist researchers in metal forming and other fields in choosing appropriate numerical methods when dealing with stiff ODEs.

Open die forging is one of the oldest manufacturing methods known to remove defects in the ingot resulting from the casting process. The improved properties of the final component are highly dependent on the strain distribution. Although sinusoidal equations and empirical formulations have been already used to estimate the strain, they have been applied only to the core of the workpiece. In this work, a novel approach is presented to model the equivalent strain distribution in 2D cross-sections, in the direction of the press, of open die forged components using neural networks. The proposed method efficiently combines a parametric sinusoidal function with a neural network to learn the complex relationships between the process parameters and the resulting local strain. The neural network is trained on a dataset of finite element (FE) simulations of rectangular geometries that cover a wide range of aspect ratios, bite ratios, and height reductions. The presented methodology with near real-time prediction capabilities shows good agreement with FE results. Moreover, the parametric function captures the characteristic pattern of the strain distribution and reveals certain physical relationships affecting the deformation of the material. These patterns are later examined by analyzing the parameters identified in the parametric sinusoidal function.

This work is carried out to investigate the anisotropic hardening behavior of MS1180 steel through experiment and analytical modeling. Dogbone and hydraulic bulging specimens were machined to test the mechanical behavior of MS1180 steel plate under different loading directions and stress states. The loading process was recorded by the three‐dimensional digital image correction system. The result uncovers that the strength under equi‐biaxial tension is larger than that under uniaxial tension. Mechanical behavior under different loading directions presents the obvious difference related to the plastic strain. Hardening behavior under uniaxial tension and equi‐biaxial tension is characterized by the Swift‐Voce and polynomial equations, respectively. Three yield functions are adopted to describe the anisotropic behavior, including Yld2000‐2d, S‐Y2009, and CQN‐Chen. A convexity analysis method based on geometric definition is used to determine the convex region of the CQN‐Chen yield surface under large strain. By comparing the yield surfaces of the three yield functions under different plastic strain, the CQN‐Chen yield function possesses a more flexible and reasonable characterization ability than the Yld2000‐2d and S‐Y2009 yield functions. The anisotropic hardening behavior of MS1180 steel plate is modeled with an error of less than 0.03 by the CQN‐Chen yield function, showing high prediction accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a new method for forming flanges in hollow parts. The process consists of an extrusion with two dies that move in an opposite direction to that of the punches. This particular kinematics of the tools makes it possible to form two flanges simultaneously in a single tool pass. The proposed method was investigated using a tube made of steel 42CrMo4. It was assumed that the extrusion would be conducted as a cold forming process at ambient temperature. Different diameters and heights of the impression made in the top and bottom dies were used. It was demonstrated that the main failure mode of the proposed technique was an unintended increase in the inside the diameter of the workpiece in the flange zone. The results showed that the above parameters had a key impact on the achievable maximum flange diameters and heights. [ABSTRACT FROM AUTHOR]

The issues of import substitution of the working tool of foreign press brakes were considered using reverse engineering. The main cause of failure is the deformation of the tool's working edges, which cannot be restored, so the only solution is to replace it. The complexity of manufacturing the working tool lies in the absence of design documentation for punches and dies, which requires creating it independently through reverse engineering, including the technology of creating a product model through direct measurements. Laser 3D scanning was used to create digital models, based on which drawings of the punch and die were developed, as well as the technology for their manufacture. Experimental-industrial tests of the manufactured bending tool showed that its operational durability practically does not differ from imported counterparts. [ABSTRACT FROM AUTHOR]

For isotropic materials, the von Mises yield criterion is generally used to interpret bulge test data and assess formability. In this paper, we investigate the role played by the J 3 dependence of the plastic response on the behavior during stretch forming under pressure. To this end, we consider the isotropic yield criterion of Drucker, which involves a unique parameter c expressible solely in terms of the ratio between the yield stresses in shear and uniaxial tension, τ Y / σ T . In the case when τ Y / σ T = 3 , the parameter c = 0 and the von Mises yield criterion is recovered, otherwise Drucker’s criterion accounts for dependence on both J 2 and J 3 . First, an analytical estimate of the ratio of the principal stresses at the apex of the dome is deduced. It is demonstrated that the stress ratio depends on the parameter c, the deviation from an equibiaxial stress state induced by changing the die aspect ratio is more pronounced for materials with higher τ Y / σ Y ratio. Finite element predictions using the yield criterion and isotropic hardening confirm the trends put into evidence theoretically. Moreover, the F.E. simulations show that there is a correlation between the value of the parameter c that describes the dependence on J 3 in the model and the strain paths that can be achieved in any given test, the level of plastic strains that develop in the dome, and the thickness reduction. Specifically, for a material characterized by c > 0 ( τ Y / σ T < 1 / 3 ) under elliptical bulging, at the apex the plastic strain ratio is greater than in the case of a von Mises material, while the stress ratio is less. On the other hand, for a material characterized by c < 0 ( τ Y / σ T > 1 / 3 ), the reverse holds true. The FE results also suggest that for certain isotropic materials neglecting the dependence of their plastic behavior on J 3 would lead to an underestimation of the thickness reduction. [ABSTRACT FROM AUTHOR]

A mathematical modeling of friction is essential for reducing the experimental efforts in order to analyze the friction in manufacturing process. In the literature a number of theoretical and experimental investigations have been carried out. However, their reliability is highly dependent on the assumptions to be considered in modelling. In this work, an asperity-based deformation is taken into account to estimate the coefficient of friction strain hardening and junction growth. To assess the accuracy of the asperity-based friction model, a detailed experimental study is carried by performing the ring compression test under different lubricants between the die and workpiece. It is observed that the material parameters of the workpiece and friction factor are most influencing parameters during ring compression test. Further, an inverse methodology is demonstrated to find the friction factor by minimizing the error between the calculated and measured friction coefficient. The proposed inverse method can be alternative approach to estimate the friction factor as a function of material, chemical and thermal properties of the workpiece and die in metal forming industries. [ABSTRACT FROM AUTHOR]

Springback is one of the factors that causes decreased product quality in tube bending; 2D and 3D stretch bending can be used to manufacture a complex geometry of a tube with springback reduction. For a nonlinear springback problem in tube bending, an artificial neural network (ANN) is an attractive data-driven approach to achieving springback prediction and control. The main objective of this paper is to control springback and improve geometrical quality with an ANN in 2D and 3D stretch bending of a rectangular tube. In general, an ANN is trained with collected data sets from a large number of experiments, causing expensive costs and time-consuming work. In the present work, the training data sets for the proposed ANN are obtained from both experiments and an analytical springback model. As the analytical model can adopt different bending angles, material properties, and geometries, supplementary data by the analytical model significantly reduced the number of experiments needed for ANN training. Contrary to the typical springback predictions, the proposed ANN synthesizes the machine settings based on the desired dimensions as the inputs. It is shown that springback can be controlled by specifying the bend angles provided by the ANN prediction. The proposed ANN method was validated in 2D and 3D stretch bending of a rectangular tube, and its prediction and control performance are favorably compared to an ANN trained with only experimental data sets. [ABSTRACT FROM AUTHOR]

Technological metal forming processes involving hot and superplastic deformation are sensitive to temperature and strain rate. This is because inelasticity mechanisms operate in different ways under different conditions, leading to the formation of different structures and therefore different effective physical and mechanical properties of the material. Optimal temperature and strain rate conditions for the forming process which provide improved performance of the resulting products with acceptable energy consumption (or, conversely, minimum energy consumption with acceptable performance characteristics) can be most effectively determined by mathematical modeling. The key elements of the latter are constitutive models for describing the behavior of the material (physical equations), which account for the influence of temperature and strain rate on various mechanisms of inelastic deformation. Such constitutive models can be most effectively developed using a multilevel approach based on the introduction of internal variables, crystal plasticity, and an explicit description of the material structure and physical deformation mechanisms. There are many works that propose multilevel mathematical models of metals that somehow explicitly account for the temperature and strain rate effects on inelastic deformation. Based on physical considerations, this analytical review defines the most promising approach to constructing multilevel constitutive models with comprehensive consideration of the temperature and strain rate effects. [ABSTRACT FROM AUTHOR]

Heavy plates are indispensable semi-finished products. Quality is strongly linked with production, so the rolling process must be performed within well-defined narrow tolerances. To meet this challenge, adequate modeling has become a necessity. In contrast to continuous strip rolling, where the workpiece can be modeled as a semi-infinite strip and 2D modeling can be argued quite well, this strategy is insufficient for the comprehensive modeling of heavy plate rolling. The geometry of the heavy plate favors an inhomogeneous distribution of relevant state variables, such as temperature. In addition, if the process involves longitudinal and spreading passes, the required plate rotation spoils the assumption of a symmetric arrangement that might have been acceptable before rotation. Consequently, the derivation of suitably reduced models is not trivial, and modeling tailored to the specific objective of investigation is of utmost importance. Models intended to resolve the evolution of inhomogeneities in the field variables are demanding and computationally expensive. An effective modular modeling strategy was developed for such models to be used offline. Mutually complementing and interchangeable modules may constitute an efficient modeling strategy valid for the specific subject of interest. The presented approach reduces the enormous cost of complete 3D simulation as much as the model purpose allows for. [ABSTRACT FROM AUTHOR]

This research investigates the benefits of different Machine Learning (ML) approaches in production systems, with respect to the given use case of considering the forming process and different friction conditions on hydraulic press response in between the phases of the sheet metal bending cycle, i.e. bending, levelling and movement. A framework for enhancing production systems with ML facilitates the transition to smarter processes and enables fast, accurate predictions integrated into decision-making and adaptive control. Comparative ML analysis provides insights into predictive regression models for hydraulic press condition recognition, enhancing process improvement. Our results are supported by performance evaluation metrics of predictive accuracy RMSE, MAE, MSE and R2 for Linear Regression (LR), Decision Trees (DT), Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Neural Network (NN) models. Given the remarkable predictive accuracy of the regression models with R2 values between 0.9483 and 0.9995, it is noteworthy that less complex models exhibit significantly shorter training times, up to 437 times shorter than more complex models. In addition, simpler models have up to 36 times better prediction rates, compared to more complex models. The fundamentals illustrate the trade-offs between model complexity, accuracy and computational training and prediction rate. [ABSTRACT FROM AUTHOR]

The machines of WF Maschinenbau process metal blanks into various workpieces using so-called flow-forming processes. The quality of these workpieces depends largely on the quality of the blanks and the condition of the machine. This creates an urgent need for automated monitoring of the forming processes and the condition of the machine. Since the complexity of the flow-forming processes makes physical modeling impossible, the present work deals with data-driven modeling using machine learning algorithms. The main contributions of this work lie in showcasing the feasibility of utilizing machine learning and sensor data to monitor flow-forming processes, along with developing a practical approach for this purpose. The approach includes an experimental design capable of providing the necessary data, as well as a procedure for preprocessing the data and extracting features that capture the information needed by the machine learning models to detect defects in the blank and the machine. To make efficient use of the small number of experiments available, the experimental design is generated using Design of Experiments methods. They consist of two parts. In the first part, a pre-selection of influencing variables relevant to the forming process is performed. In the second part of the design, the selected variables are investigated in more detail. The preprocessing procedure consists of feature engineering, feature extraction and feature selection. In the feature engineering step, the data set is augmented with time series variables that are meaningful in the domain. For feature extraction, an algorithm was developed based on the mechanisms of the r-STSF, a state-of-the-art algorithm for time series classification, extending them for multivariate time series and metric target variables. This feature extraction algorithm itself can be seen as an additional contribution of this work, because it is not tied to the application domain of monitoring flow-forming processes, but can be used as a feature extraction algorithm for multivariate time series classification in general. For feature selection, a Recursive Feature Elimination is employed. With the resulting features, random forests are trained to detect several quality features of the blank and defects of the machine. The trained models achieve good prediction accuracy for most of the target variables. This shows that the application of machine learning is a promising approach for the monitoring of flow-forming processes, which requires further investigation for confirmation. [ABSTRACT FROM AUTHOR]

The accurate representation of tribological boundary conditions at the tool-workpiece interface is crucial for analysis and optimization of formability, material flow, and surface quality of components during metal forming processes. It has been found that these tribological conditions vary spatially and historically with process parameters and contact conditions. These time-dependent tribological behaviours are also known as transient tribological phenomena, which are widely observed during forming processes and many other manufacturing application scenarios. However, constant friction values are usually assigned to represent complex and dynamic interfacial conditions, which would introduce deviations in the relevant predictions. In this paper, transient tribological phenomena and the contemporary understanding of the interaction between friction and wear are reviewed, and it has been found that these phenomena are induced by the transitions of friction mechanisms and highly dependent on complex loading conditions at the interface. Friction modelling techniques for transient behaviours for metal forming applications are also reviewed. To accurately describe the evolutionary friction values and corresponding wear during forming, the advanced interactive friction modelling has been established for different application scenarios, including lubricated condition, dry sliding condition (metal-on-metal contact), and coated system. [ABSTRACT FROM AUTHOR]

Friction is one of the main phenomena accompanying sheet metal forming methods, affecting the surface quality of products and the formability of the sheet metal. The most basic and cheapest way to reduce friction is to use lubricants, which should ensure the highest lubrication efficiency and at the same time be environmentally friendly. Due to the trend towards sustainable production, vegetable oils have been used in research as an alternative to petroleum-based lubricants. The analysis of friction in sheet metal forming requires an appropriate tribotester simulating the friction conditions in a specific area of the sheet metal being formed. Research has used a special strip drawing tribometer, enabling the determination the value of the coefficient of friction in the blankholder zone in the deep drawing process. Quantitative analysis of the friction phenomenon is necessary at the stage of designing the technological process and selecting technological parameters, including blankholder pressure. This article presents the results of friction testing of 1.0338 (DC04) steel sheets using a strip drawing test. The experimental tests involved pulling a strip of sheet metal between two countersamples with a rounded surface. The tests were carried out on countersamples with different levels of roughness for the range of contact pressures occurring in the blankholder zone in the deep drawing process (1.7–5 MPa). The values of the coefficient of friction determined under dry friction conditions were compared with the results for edible (corn, sunflower and rapeseed) and non-edible (Moringa, Karanja) vegetable lubricants. The tested oils are the most commonly used vegetable-based biolubricants in metal forming operations. Multi-layer artificial neural networks were used to determine the relationship between the value of the contact pressure, the roughness of the countersamples, the oil viscosity and density, and the value of the coefficient of friction. Rapeseed oil provided the best lubrication efficiency during friction testing for all of the tested samples, with an average surface roughness of Sa 0.44–1.34 μm. At the same time, as the roughness of the countersamples increased, a decrease in lubrication efficiency was observed. The lowest root mean squared error value was observed for the MLP-4-8-1 network trained with the quasi-Newton algorithm. Most of the analysed networks with different architectures trained using the various algorithms showed that the kinematic viscosity of the oil was the most important aspect in assessing the friction of the sheets tested. The influence of kinematic viscosity on the value of the coefficient of friction is strongly dependent on the surface roughness of the countersamples. [ABSTRACT FROM AUTHOR]

Pressure-carrying properties of traditional flexible dies can hardly be controlled during the forming process of shell components. In this work, a forming method based on the subregional and dynamic pressure response of flexible die was proposed, aiming at achieving the flexible control of shell deformation in different forming regions and stages. Elastic composites with iron particles selectively dispersed in pre-designed location, i.e., heterogeneous magneto-rheological elastomers (MREs), were fabricated and employed as the flexible dies in the bulge tests of Al1060 sheet in the presence of external magnetic field. Shell deformation were dynamically measured, pressure field and stress state were numerically studied. The results showed that local shell deformation could be controlled in a nearly monotonic way by adjusting the distribution pattern and content (φ) of iron particles. In the case that heterogeneous MRE with φ = 0/30% was employed and magnetic field with flux density (B) of 1950 Gs was applied, difference of bulge height and strain on both sides of the deformed shell increased by 6.5% and 16%, respectively. The maximum difference of bulge height on both sides of the sheet metal appeared in the case that B = 1240 Gs, which showed an increase of 9% compared with the condition that no magnetic field is applied. But, the influence of magnetic field is not monotonous. Changed stress paths of sheet metal, which was induced by non-uniformly distributed and dynamically controllable pressure field of heterogeneous MREs, was used to clarify the forming mechanism. [ABSTRACT FROM AUTHOR]

This article provides a detailed review of the types of magnesium wheels available in the industry and the current methods of the wheels' production. The past several years have seen a significant development of magnesium-based lightweight alloys employed as a structural material for modern light vehicles. Magnesium alloys are characterized by their low density while maintaining good mechanical properties. The use of these alloys in the industry enables vehicles' weight reduction while increasing their technical parameters. The first part of the article presents the unique properties of magnesium alloys that determine the application of this material for lightweight vehicle wheels. The advantages of using magnesium wheels over aluminum wheels are also presented. Next, a classification of the types of magnesium wheels was made in regard to their construction, applications, and manufacturing methods. At present, magnesium wheels by construction can be classified according to their geometry as single parts or assembled parts. In reference to geometry, wheels can have different shapes: classic, multi-spoke, with holes, or with frames. Depending on the geometry used, magnesium wheels can have different parameters, such as their mounting hole spacing, wheel diameters, or rim width. Considering the applications in various industries, main distinctions can be made between magnesium wheels for automobiles, motorcycles, bicycles, and wheelchairs. Magnesium wheels can also be categorized in regards to the manufacturing methods: casting, machining, forging, and hybrid manufacturing. The second part of the article focuses on the analysis of magnesium alloy wheel-manufacturing technologies used in the industry and developed by research centers. This article discusses these manufacturing technologies in detail and indicates prospective directions for further development. [ABSTRACT FROM AUTHOR]

This article presents the results of an analysis of the influence of friction process parameters on the coefficient of friction of steel sheets 1.0347 (DC03), 1.0338 (DC04) and 1.0312 (DC05). A special tribometer was designed and manufactured in order to simulate the friction phenomenon occurring in the blankholder area in deep drawing operations. Lubricant was supplied to the contact zone under pressure. The value of the coefficient of friction was determined under various contact pressures and lubrication conditions. Multi-layer artificial neural networks (ANNs) were used to predict the value of the coefficient of friction. The input parameters considered were the kinematic viscosity of lubricants, contact pressure, lubricant pressure, selected mechanical properties and basic surface roughness parameters of sheet metals. The value of the coefficient of friction of 1.0312 steel sheets was predicted based on the results of friction tests on 1.0347 and 1.0338 steel sheets. Many ANN models were built to find a neural network that will provide the best prediction performance. It was found that to ensure a high performance of ANN prediction, it is necessary to simultaneously take into account all the considered roughness parameters (Sa, Ssk and Sku). The predictive performance of the 'best' network was greater than R2 = 0.98. The lubricant pressure had the greatest impact on the coefficient of friction. Increasing the value of this parameter reduces the value of the coefficient of friction. However, the greater the contact pressure, the smaller the beneficial effect of pressure-assisted lubrication. The third parameter of the friction process, the kinematic viscosity of the oil, exhibited the smallest impact on the coefficient of friction. [ABSTRACT FROM AUTHOR]

Unified constitutive equations have been developed to model the behaviour of metallic materials under various processing conditions. These constitutive equations usually take the form of a set of ordinary differential equations (ODEs), which must be solved thousands of times in a finite element (FE) process simulation. Thus, an efficient and reliable numerical integration method for large systems is crucial for solving this problem. However, in many constitutive equations, numerical stiffness is often present. This means that the stability requirements, rather than the accuracy, constrain the step size. Therefore, certain numerical methods become unsuitable when the required step size becomes unacceptably small. In this study, a series of mathematical analyses was performed to investigate the difficulties in the numerical integration of three sets of unified viscoplastic/creep constitutive equations. Based on an analysis of the current stiffness assessment methods, a novel index was introduced, that enables an accurate assessment of the stiffness of the ODE-type unified constitutive equations. A computational study was also conducted to benchmark several promising implicit numerical integration methods for viscoplastic/creep constitutive equations. This study can assist researchers in metal forming and other fields in choosing appropriate numerical methods when dealing with stiff ODEs. [ABSTRACT FROM AUTHOR]

The transfer mechanism of a toner pattern printed on a soft film using a laser printer to a metal surface in compression was investigated by experiment and computer simulations. The toner pattern can be transferred successfully to the workpiece surface under the conditions that the entire soft film continues to be squeezed out smoothly from the workpiece edge until the transfer process is completed. The results indicate that the average tool pressure needed for the complete transfer of the toner pattern to the workpiece surface can be predicted by the average compression pressure calculated with slab method [ABSTRACT FROM AUTHOR]

The force effect on the guide elements of the forging and pressing equipment of transverse forces is considered, taking into account the accompanying factors—gaps in the guides of the equipment and the eccentricity of the applied load that occur when performing metal forming operations. The method of modeling of the system, the stages of creating of a model are presented, the results of research are given, the main conclusions and recommendations are given, taking into account which will improve the quality of products obtained by forming operations and improve the performance of forging and pressing equipment and the tools used on it. [ABSTRACT FROM AUTHOR]

This study presents information regarding the development of a localized laser-induced heat treatment for aluminum alloys. Such a process is intended to improve the forming behavior of aluminum parts in challenging metal-forming conditions. This study details information on material, heat treatment parameters as well as results for strength, hardness, and elongation properties. It was concluded that it is possible to locally modify yield strength and hardness using laser control parameters and process duration suitable for industrial applications. [ABSTRACT FROM AUTHOR]

Meso-and microforming is a technology to shape parts from extremely small metal billets. Parts with geometric dimensions are a few millimetres to a few micrometres. With the rapid development of the electrical-electronics industry and biomedical engineering, the technology of forming microscopic parts has been researched and applied because of its efficiency, accuracy, and high productivity. Deep drawing is an operation that turns flat sheet metal blanks into hollow, open-mouth parts. It is an essential process in sheet metal stamping. Micro deep drawing is one of the micro-shaping technologies that has been widely studied and applied in recent years. However, the bases for calculating technological and geometrical parameters in the micro-deep drawing have not yet been analyzed and evaluated in detail. Therefore, this paper has proposed a theoretical basis combined with simulation applied to the design of technology to manufacture a connector head part drawing die with a diameter of 300µm and height of 1500µm using materials SUS304 material. Numerical simulation also allows evaluation of the stamping part's internal stress state, the ability to pull the workpiece into the die, and the thickness distribution on the product wall. Experimental research has also verified that, with the determined parameters, the stamping parts meet the quality requirements. This indicates the proposed calculation methods for the tiny deep drawing operation are entirely suitable. The results of this research can be wholly applied to the production of micro-sized cylindrical cup parts using the deep drawing method. [ABSTRACT FROM AUTHOR]

Abstract The accurate representation of tribological boundary conditions at the tool-workpiece interface is crucial for analysis and optimization of formability, material flow, and surface quality of components during metal forming processes. It has been found that these tribological conditions vary spatially and historically with process parameters and contact conditions. These time-dependent tribological behaviours are also known as transient tribological phenomena, which are widely observed during forming processes and many other manufacturing application scenarios. However, constant friction values are usually assigned to represent complex and dynamic interfacial conditions, which would introduce deviations in the relevant predictions. In this paper, transient tribological phenomena and the contemporary understanding of the interaction between friction and wear are reviewed, and it has been found that these phenomena are induced by the transitions of friction mechanisms and highly dependent on complex loading conditions at the interface. Friction modelling techniques for transient behaviours for metal forming applications are also reviewed. To accurately describe the evolutionary friction values and corresponding wear during forming, the advanced interactive friction modelling has been established for different application scenarios, including lubricated condition, dry sliding condition (metal-on-metal contact), and coated system.

This paper presents results from experimental and numerical studies of the skew rolling process used to shape axisymmetric products made of C60-grade steel. An experimental study was carried out to investigate the effect of process parameters described by the forming angle α, the skew angle θ, the reduction ratio δ, and the jaw chuck velocity Vu on the surface roughness Ra of the forgings. Stepped forgings made of C60-grade steel were rolled. Based on numerical calculations, a machine learning regression model was developed that uses process parameters to predict the surface roughness of produced parts. The random forest model was found to be the most effective based on the determined metrics (MAE, RMSE, R2). A more detailed analysis of this model was performed using the SHAP library. The application of ML methods will enable optimization of skew rolling through appropriate selection of process parameters affecting improvement in product quality. [ABSTRACT FROM AUTHOR]

This paper addresses the prediction of ductile fracture and hardening behavior of AA7075-T651 sheets under different stress states. Tests from shear to balanced biaxial tension are carried out up to fracture to determine ductile fracture. Considering the effect of different loading conditions on the plastic behavior of the material, stress triaxiality T, representing different stress states, and Lode parameter L are introduced to the Voce hardening model. After calibration by an inverse engineering approach, the improved hardening model is analyzed numerically, and an accurate description of the material strength under different stress states is verified. Based on the hardening model, four uncoupled ductile fracture criteria (Oh, Rice-Tracy, DF2012 and DF2015) are calibrated and utilized to predict the onset of fracture for the specimens. All fracture strains are obtained by a hybrid experimental-numerical method. A comparison of the predicted fracture strokes indicates that the DF2015 criterion predicts the fracture behavior in better agreement with the experimental results, especially for the Nakajima specimen. [ABSTRACT FROM AUTHOR]

In this study, experimental investigation and numerical simulation U-bending process are employed to construct a damage model based on ductile damage criteria. Also, an adaptive neural-fuzzy inference system (ANFIS) is extracted for the prediction of damage behavior based on the Cockroft-Latham and modified Mohr-Coulomb criteria calibrated using the proposed method. Appropriate calibration tests including uniaxial tension up to the in-plane shear tension are designed based on the bending deformation mechanics. In addition, the optimal parameters of the ANFIS system are obtained by the gray wolf optimization algorithm. The accuracy of the proposed model is investigated using experimental-numerical results. Results indicate that both the proposed damage model and ANFIS network are successful in predicting the damage behavior in the U-bending process. In addition, the maximum bending angle to achieve a damage index of less than 1 in the ratio of bending radius to sheet thicknesses of 0.5, 1, 2, and 3 is presented using the model presented in this study. According to the results, the minimum applicable ratio of bending radius to thickness is equal to 3 for when the sheet metal bends up to 90 degrees. [ABSTRACT FROM AUTHOR]

In the present study, the lubricated and non-lubricated stamping processes are used for fabrication of the metallic bipolar plates (MBPPs) with a thickness of 0.1 mm made of SS3316 with a serpentine flow field. Experimental tests together with the finite element (FE) model are utilized to define the directional thickness distribution. According to the results, considering the friction factor of 0.1 during the FE analysis is capable of providing accurate results in terms of thickness prediction under lubricated conditions. The largest thinning percentages in the modified conditions are found to be 27.02%, 30.40%, and 26.00% in the longitudinal, diagonal, and transverse directions, respectively, indicating that the diagonal direction is the most critical one. Finally, the effect of lubricating condition on wrinkling of the sample is investigated. According to the results, using the lubricated condition during the stamping process would lead to augmentation of the wrinkling of the bipolar plate and also, the wrinkling of bipolar plate’s margin was not symmetrical.

Conventional rolling is a plastic deformation process that uses compression between two rolls to reduce material thickness and produce sheet/plane geometries. This deformation process modifies the material structure by generating texture, reducing the grain size, and strengthening the material. The rolling process can enhance the strength and hardness of lightweight materials while still preserving their inherent lightness. Lightweight metals like magnesium alloys tend to lack mechanical strength and hardness in load-bearing applications. The general rolling process is controlled by the thickness reduction, velocity of the rolls, and temperature. When held at a constant thickness reduction, each pass through the rolls introduces an increase in strain hardening, which could ultimately result in cracking, spallation, and other defects. This study is designed to optimize the rolling process by evaluating the effects of the strain rate, rather than the thickness reduction, as a process control parameter.

This paper presents a new method for forming flanges in hollow parts. The process consists of an extrusion with two dies that move in an opposite direction to that of the punches. This particular kinematics of the tools makes it possible to form two flanges simultaneously in a single tool pass. The proposed method was investigated using a tube made of steel 42CrMo4. It was assumed that the extrusion would be conducted as a cold forming process at ambient temperature. Different diameters and heights of the impression made in the top and bottom dies were used. It was demonstrated that the main failure mode of the proposed technique was an unintended increase in the inside the diameter of the workpiece in the flange zone. The results showed that the above parameters had a key impact on the achievable maximum flange diameters and heights.

Heavy plates are indispensable semi-finished products. Quality is strongly linked with production, so the rolling process must be performed within well-defined narrow tolerances. To meet this challenge, adequate modeling has become a necessity. In contrast to continuous strip rolling, where the workpiece can be modeled as a semi-infinite strip and 2D modeling can be argued quite well, this strategy is insufficient for the comprehensive modeling of heavy plate rolling. The geometry of the heavy plate favors an inhomogeneous distribution of relevant state variables, such as temperature. In addition, if the process involves longitudinal and spreading passes, the required plate rotation spoils the assumption of a symmetric arrangement that might have been acceptable before rotation. Consequently, the derivation of suitably reduced models is not trivial, and modeling tailored to the specific objective of investigation is of utmost importance. Models intended to resolve the evolution of inhomogeneities in the field variables are demanding and computationally expensive. An effective modular modeling strategy was developed for such models to be used offline. Mutually complementing and interchangeable modules may constitute an efficient modeling strategy valid for the specific subject of interest. The presented approach reduces the enormous cost of complete 3D simulation as much as the model purpose allows for.

This paper gives insight into the development and utilization of a computer software that uses raw experimental data from the load cells and DIC systems to obtain the instant of time at fracture tf, the loading paths in principal strain space ε1=f(ε2), and their conversion into the space of effective strain vs. stress triaxiality ε‾=f(η). Special emphasis is given to the different assumptions and stress triaxiality measures that can be used to convert the loading paths from principal strain space into the space of effective strain vs. stress triaxiality. Results for double-action radial extrusion show the differences of treating the loading paths as linear or non-linear from beginning until the onset of failure by fracture. Results also allow concluding on the importance of accounting for the stress triaxiality derived from individual experimental measurements in an average sense over the entire loading paths, to avoid overestimation and mislocation of the fracture forming limits. The applicability of the software for education and training of students in formability is also discussed.