Deep Drawing Steel Research Articles

Lightweighting an automotive fuel tank through formability analysis

For lightweighting automotive sheet metal parts, it is important to use the right grade/material with the minimum possible initial blank thickness in the forming processes. Fuel tank is one of the most critical sheet metal parts in automobiles due to its complex shape and large depth. In this work, a simulation-based approach has been employed to study the feasibility of lightweighting an automotive fuel tank (for two wheelers) by reducing blank thickness and changing steel grade. Formability analysis has been carried out using numerical simulations of a 0.8 mm thick sheet of Extra Deep Drawing (EDD) steel which is being used to manufacture the fuel tanks. Simulations have been performed to find out the minimum initial sheet thickness which can be formed successfully without failure or excessive thinning. From the predictions of formability, thinning, and strain distributions in the formed parts, it has been found that the weight of the part can be reduced by 12.5% by reducing the initial thickness from 0.80 to 0.70 mm. To reduce the thickness further, simulations have also been carried out by changing the grade from EDD steel to Interstitial Free (IF) steel which has superior drawability than EDD steel. It has been found that the blank thickness can be further reduced to 0.65 mm in the case of IF steel which is expected to result in 19.0% reduction in the weight of the component. The predictions have been validated by the actual press trials in the industry with reduced sheet thickness. The predicted strain distribution and thinning in the formed parts have also been validated with the experimental press trails. In addition to the weight reduction, the proposed change in thickness and grade would lead to a reduction of 16.5% and 4.0%, respectively, in the raw material cost of the fuel tank.

Influence of material in sheet-bulk metal forming from coil

AbstractSheet-bulk metal forming (SBMF) represents an innovative approach for the efficient manufacturing of functional integrated parts from sheet metal by applying bulk forming processes to sheet metal. It combines the advantages of part weight optimization and process chain shortening. In view of the increasing demand for functionally integrated components, the forming from coil offers an economical possibility to produce high output rates. Nevertheless, an underfilling of functional elements as well as an asymmetric part forming are current challenges. In order to apply SBMF from coil in industrial contexts, it is necessary to understand the causes of these process limits and to develop measures for improving the forming of functional elements and thereby push existing forming limits. In this paper, both a lateral and a backward extrusion process from coil for the forming of internal and external geared SBMF parts are investigated to develop a fundamental process understanding. In a combined numerical-experimental approach, the influence of the flow behavior of different materials as well as the impact of the main material flow direction of the process on the dimensional part accuracy is analyzed. This enables the evaluation of fundamental material-related dependencies and influences regarding the geometrical part accuracy, the mechanical component properties and the tool load for forming of the conventional deep-drawing steel DC04 (t0 = 2 mm), a high-strength steel material (HC260LA) and an aluminum alloy (AA6082 T6). Based on these findings, measures for material flow control are identified and investigated to counteract the challenges of SBMF from coil. These findings permit the identification of potential transferability of the identified cause-effect mechanisms to different processes, component geometries and materials. Thereby, in particular, an increase in coil width as well as lower a strain hardening behavior of the material offers the possibility to improve the geometrical accuracy of the components.

Effect of Countersample Coatings on the Friction Behaviour of DC01 Steel Sheets in Bending-under-Tension Friction Tests.

The aim of this article is to provide an analysis of the influence of the type of hard anti-wear coatings on the friction behaviour of DC01 deep-drawing steel sheets. DC01 steel sheets exhibit high formability, and they are widely used in sheet metal forming operations. The tribological properties of the tool surface, especially the coating used, determine the friction conditions in sheet metal forming. In order to carry out the research, this study developed and manufactured a special bending-under-tension (BUT) friction tribometer that models the friction phenomenon on the rounded edges of tools in the deep-drawing process. The rationale for building the tribotester was that there are no commercial tribotesters available that can be used to model the phenomenon of friction on the rounded edges of tools in sheet forming processes. The influence of the type of coating and sheet deformation on the coefficient of friction (CoF) and the change in the topography of the sheet surface were analysed. Countersamples with surfaces prepared using titanium + nitrogen ion implantation, nitrogen ion implantation and electron beam remelting were tested. The tests were carried out in conditions of dry friction and lubrication with oils with different kinematic viscosities. Under dry friction conditions, a clear increase in the CoF value, with the elongation of the samples for all analysed types of countersamples, was observed. Under lubricated conditions, the uncoated countersample showed the most favourable friction conditions. Furthermore, oil with a lower viscosity provided more favourable conditions for reducing the coefficient of friction. Within the entire range of sample elongation, the most favourable conditions for reducing the CoF were provided by uncoated samples and lubrication with S100+ oil. During the friction process, the average roughness decreased as a result of flattening the phenomenon. Under dry friction conditions, the value of the Sa parameter during the BUT test decreased by 20.3-30.2%, depending on the type of countersample. As a result of the friction process, the kurtosis and skewness increased and decreased, respectively, compared to as-received sheet metal.

Influence of boriding on the tribological behavior of AISI D2 tool steel for dry deep drawing of stainless steel and aluminum

Deep drawing of stainless steel and aluminum is a widely adopted method for manufacturing various industrial goods. However, the wear damage produced to die tools is a recurring problem in these processes. While lubricants are often employed to lubricate and reduce die wear, dry deep drawing is the current best alternative for meeting green manufacturing demands. Hence, enhanced solid lubricants or surface treatments for dies are highly required. This study aims to evaluate and compare the tribological behavior of a heat-treated AISI D2 steel and a boriding AISI D2 tool steel against stainless steel (AISI 304) and aluminum alloy (AA 6061 T6) in the absence of lubricant. The boride coating was formed on the AISI D2 steel by the closed-pack boriding technique, resulting in a tooth-saw morphology with an overall thickness of 33.6 μm. X-ray diffraction analysis confirmed the presence of FeB, Fe2B, and Cr2B3 phases on the surface of the borided AISI D2 tool steel. Notably, the boriding treatment led to a significant increase in the surface hardness of the tool steel, reaching a value of 20.7 GPa. The coefficient of friction (CoF) behavior and wear were evaluated by pin-on-disk tests, simulating conditions similar to those found in cold deep drawing operations. The wear surfaces were analyzed by optical profilometry and scanning electron microscopy (SEM). The results suggest boriding as a suitable treatment to reduce wear damage on the surface of deep drawing dies used for stainless steel and aluminum forming processes in the absence of lubricant. Two-body abrasion and adhesion were identified as the main wear mechanisms in AISI D2 steel against stainless steel and aluminum, with a significant amount of oxidation observed when aluminum was tested. Additionally, a reduction of approximately 20 % in the CoF was observed when employing borided tool steel against stainless steel. However, there was an observed increase of around 75 % in the CoF when the borided tool steel was tested against aluminum.

Machine learning-based sampling of virtual experiments within the full stress state

This paper presents a new machine learning-based approach to investigate anisotropic yield surfaces of sheet metals by means of virtual experiments. The new sampling approach is based on the machine learning technique known as active learning, which has been adapted to efficiently sample virtual experiments with respect to the full stress state in order to identify parameters of anisotropic yield functions. The approach was employed to sample virtual experiments based on the crystal plasticity finite element method (CPFEM) for a DX56D deep drawing steel and compared with two state-of-the-art sampling methods taken from the literature. The resulting points on the initial yield surface for all three sampling methods were used to identify parameters of the anisotropic yield functions Hill48, Yld91, Yld2004–18p and Yld2004–27p. These parameters were then applied to a cylindrical cup drawing simulation to analyse the effect of the three sampling methods on a typical sheet forming simulation. The results show that the new machine learning-based sampling approach has a higher sampling efficiency than the two state-of-the-art sampling methods. Consequently, fewer computationally expensive crystal plasticity simulations are required. By comparing different variants of the Hill48, Yld91, Yld2004–18p and Yld2004–27p yield surfaces, it was also found that identifying parameters of anisotropic yield functions based on virtual experiments sampled within the full stress state can lead to a degraded representation of the in-plane anisotropy. With respect to DX56D deep drawing steel, this degradation was observed for the Yld2004–18p yield function. The negative implications following from this degraded in-plane representation were further demonstrated by the results of the cylindrical cup drawing process. As a consequence, the representation of the in-plane anisotropy must be carefully reviewed when taking the full stress state into account. In this context, Yld2004–27p was identified as being sufficiently flexible to simultaneously represent the plastic anisotropy of DX56D with respect to the in-plane and out-of-plane behaviour with high accuracy.

Analysis of the friction performance of deep-drawing steel sheets using network models

AbstractThis article presents the results of pilot studies on the lubrication of the blankholder zone in sheet metal forming using a pressurised lubricant. The authors invented a method and built a special tribometer for pressure-assisted lubrication. This approach reduces friction in sheet metal forming processes compared to conventional lubrication. Moreover, the artificial neural network approach combined with a force-directed Fruchterman-Reingold graph algorithm and Spearman’s correlation was used for the first time to analyse the relationships between the friction process parameters and the output parameters (the coefficient of friction and the resulting surface roughness of the sheet metal). The experimental tests were conducted utilising strip drawing on four grades of steel sheets known to be outstanding for deep-drawing quality. Different oils, oil pressures and contact pressures were used. Artificial neural network models were used for the first time to determine these relationships in a strip drawing test where every parameter is represented by one node, and all nodes are connected by edges with each other. R Software version 4.2.3 was used to construct the network using the ‘qgraph’ and ‘networktools’ packages. It was found that friction conditions had a highly significant negative correlation with coefficient of friction (COF) and a moderately significant negative correlation with the final surface roughness. However, the initial surface roughness of the as-received sheets had a negative correlation with the COF and a positive one with the resulting surface roughness of the sheet metal. The parameters most related to the COF are the strength coefficient, the ultimate tensile strength and the friction conditions (dry friction or pressurised lubrication). Spearman’s correlation coefficients showed a strong correlation between the kinematic viscosity and the friction conditions.

Application of categorical boosting to modelling the friction behaviour of DC05 steel sheets in strip drawing test

It is challenging to model the coefficient of friction, surface roughness, and related tribological processes during metal contact because of flattening, ploughing, and adhesion. It is important to choose the appropriate process parameters carefully when creating analytical models to overcome the challenges posed by complexity. This will ensure the production of sheet metal formed components that meets the required quality standards and is free from faults. This research analyses the impacts of nominal pressure, kinematic viscosity of lubricant, and lubricant pressure on the coefficient of friction and average roughness of DC05 deep-drawing steel sheets. The strip drawing test was used to determine the coefficient of friction. This work utilises the Categoric Boosting (CatBoost) machine learning algorithm created by Yandex to estimate the COF and surface roughness, intending to conduct a comprehensive investigation of process parameters. A Shapley decision plot exhibits the coefficient of friction prediction models via cumulative SHapley Additive exPlanations (SHAP) data. CatBoost has outstanding prediction accuracy, as seen by R2 values ranging from 0.955 to 0.894 for both the training and testing datasets for the COF, as well as 0.992 to 0.885 for surface roughness.

Design, Numerical and Experimental Testing of a Flexible Test Bench for High-Speed Impact Shear-Cutting with Linear Motors

Given the use of high-strength steels to achieve lightweight construction goals, conventional shear-cutting processes are reaching their limits. Therefore, so-called high-speed impact cutting (HSIC) is used to achieve the required cut surface qualities. A new machine concept consisting of linear motors and an impact mass is presented to investigate HSIC. It allows all relevant parameters to be flexibly adjusted and measured. The design and construction of the test bench, as well as the mechanism for coupling the impact mass, are described. To validate the theoretically determined process speeds, the cutting process was recorded with high-speed cameras, and HSIC with a mild deep-drawing steel sheet was performed. It was discovered that very good cutting edges could be produced, which showed a significantly lower hardening depth than slowly cut reference samples. In addition, HSIC was numerically modelled in LS-DYNA, and the calculated cutting edges were compared with the real ones. With the help of adaptive meshing, a very good agreement for the cutting edges could be achieved. The results show the great potential of using a linear motor in HSIC.

Influence of pre-strain on tensile response of extra deep drawing (EDD) steel under varying strain rates and crash performance

This study explores the impact of pre-strain on the uniaxial tensile stress-strain behavior of extra deep drawing (EDD) steel at varying strain rates. The EDD steel blanks were subjected to a uniaxial pre-strain of 15 % in either the transverse or rolling direction. Subsequently, uniaxial tensile tests were conducted at different strain rates, both orthogonal and parallel to the initial pre-straining direction. The study also investigates the alterations in yield stress (YS), ultimate tensile strength (UTS), uniform elongation (UEL), total elongation (TEL), and strain energy absorption (SED, represented by the area under the tensile stress-strain curve) at diverse strain rates after pre-straining. The experimental findings indicate that pre-straining is advantageous for enhancing YS, UTS, and SED. However, this improvement comes at the expense of UEL or TEL. A square crush tube, which shares similarities with automotive components, was examined to assess the crash performance of several automotive structural parts during crash events. A validated constitutive model was used to predict the square tube's energy absorption and crush performance under high-velocity conditions for different pre-straining scenarios.

An Investigation into the Friction of Cold-Rolled Low-Carbon DC06 Steel Sheets in Sheet Metal Forming Using Radial Basis Function Neural Networks

This article presents the friction test results for cold-rolled low-carbon DC06 steel sheets, which are commonly processed into finished products using sheet metal forming methods. A strip drawing test with flat dies was used in the experimental tests. The strip-drawing test is used to model the friction phenomena in the flange area of the drawpiece. The tests were carried out using a tester that enabled lubrication with a pressurised lubricant. The friction tests were carried out at different nominal pressures, oil pressures, and friction conditions (dry friction and oil lubrication). Oils destined for deep-drawing operations were used as lubricants. Neural networks with radial base functions (RBFs) were used to explore the influence of individual friction parameters on the value of the coefficient of friction (COF). Under lubrication with both oils considered, the value of the COF increased with decreasing oil pressure. This confirms the correctness of the concept of the device for reducing friction in the flange area of the drawpiece. The developed concept of pressurised lubrication is most effective at relatively small nominal pressures of 2–4 MPa. This range of nominal pressures corresponds to the actual nip pressures when forming deep-drawing steel sheets. Under conditions of dry friction, the values obtained for the COF rise above 0.3, while under lubrication conditions, even without pressure-assisted lubrication, the COF does not exceed 0.2. As the nominal pressure increases, the effectiveness of the lubrication exponentially decreases. It was found that the Sq parameter carries the most information regarding the value of the COF. The RBF neural network with nine neurons in the hidden layer (RBF-8-9-1) and containing the Sq parameter as the input was characterised by an R2 of 0.989 and an error of 0.000292 for the testing set.

Optimisation of laser welding of deep drawing steel for automotive applications by Machine Learning: A comparison of different techniques

AbstractLaser welding is particularly relevant in the industry thanks to its simplicity, flexibility and final quality. The industry 4.0 and sustainable manufacturing framework gives massive attention to in situ and non‐destructive inspection methods to predict laser weld final quality. Literature often resorts to supervised Machine Learning approaches. However, selecting the ApTest method is non‐trivial and often decision making relies on diverse and unclearly defined criteria. This work addresses this task by proposing a statistical comparison method based on nonparametric tests. The method is applied to the most relevant supervised Machine Learning approaches exploited in literature to predict laser weld quality, specifically, considering the optimisation of a new production line, hence focussing on supervised Machine Learning methods that do not require massive data set, that is, Generalized Linear Model (GLM), Gaussian Process Regression, Support Vector Machine, Classification and Regression Tree, and Genetic Algorithms. The statistical comparison is carried out to select the best‐performing model, which is then exploited to optimise the production process. Additionally, an automatic process to optimise Machine Learning models and process parameters is resorted to, basing on Bayesian approaches, to reduce operator effect. This work provides quality and process engineers with a simple framework to compare Machine Learning approaches performances and select the most suitable process modelling technique.

RELATION BETWEEN DISCONTINUOUS PLASTIC FLOW AND STRAIN HARDENING OF THE LOW-CARBON STEEL

Purpose. Determination of the conditions for the disappearance of a section of intermittent flow and its effect on the strain hardening of low-carbon steel.
 Actuality. The use of low carbon steels with intermittent flow for stamping has a significant limitation. Based on this, the issues of the effect of ferrite grain size on the occurrence of intermittent flow are relevant for determining the optimal structural state of steels intended for deep drawing.
 Research methods. The structure of the samples was studied under a light microscope, the grain size of the ferrite was determined by the methods of quantitative metallography. The mechanical properties of steel were determined in tension at room temperature and a strain rate of 10-3 s-1. The characteristics of the initiation of plastic flow and the parameters of strain hardening were determined from the analysis of tension curves in logarithmic coordinates. In the region of uniform strain hardening, the deformation of the violation of the directly proportional relation lgσ-lgε was determined as the moment of formation of dislocation structures with a certain periodicity.
 Results. An increase in the grain size of ferrite is accompanied by a decrease in the region of intermittent flow and a shift in the moment of formation of a dislocation cellular structure towards small plastic deformations. The strain hardening rate in the region of uniform work hardening and the Lüders strain are related by an inversely proportional relationship. With excessively large grain sizes of ferrite, the difficulty of maintaining conditions for a uniform distribution of slip lines is one of the reasons for the disappearance of the intermittent flow region on the deformation curves of low-carbon steel.
 Scientific novelty. With an increase in the grain size of ferrite, the deformation of the beginning of the decay of a uniform distribution of dislocations into periodic structures is shifted to a decrease. At the same time, an increase in the ability of the metal to work hardening during the formation of a deformation band contributes to a decrease in the length of the intermittent flow section.
 Practical value. Determining the nature of the influence of the grain size of low-carbon steel ferrite on the characteristics of work hardening and the formation of dislocation periodic structures can be useful in determining the optimal structural state of steel for deep drawing.

Non-destructive evaluation of magnetic anisotropy associated with crystallographic texture of interstitial free steels

Interstitial free (IF) steels, as third generation of deep drawing steel, are used in the automotive industry for applications requiring good formability, strength and a superior surface quality. The formability is achieved by having a strong gamma fibre (γ-fibre) crystallographic texture and is quantified by measuring the r-value from tensile tests. Whereas the crystallographic texture is typically measured using EBSD or XRD on samples after careful surface preparation, it is desirable to be able to evaluate it non-destructively on production material. To do this aim, a magnetic anisotropy measurement is an attractive approach as the magnetic behaviour of steel is known to be affected by its crystallographic texture. A set of interstitial free steel samples, at different stages of recrystallisation process (i.e., commercially cold rolled and annealed to achieve fully and partially recrystallised microstructures) and consequently different texture components have been employed to study the measurement and possibility of model-wise predicting magnetic anisotropy in IF steels. In order to compare the predicted magnetic anisotropy to the measured values, a finite element (FE) microstructure model that takes into account crystallographic texture was applied.The findings indicate that the non-destructive technique deployed in this study - a U-shaped electromagnetic (EM) sensor that can be placed onto a sheet specimen - is promising for a rapid assessment of the magnetic anisotropy in IF steels.

Prediction of mechanical properties for deep drawing steel by deep learning

Prediction of mechanical properties for deep drawing steel by deep learning

Investigation of the Plane Strain Behaviour of a Laser-Heat Treated Aluminium Alloy

The necessity of complex-shaped components characterized by superior mechanical properties and limited weight is moving the attention to the Aluminium (Al) alloys. Deep Drawing Steel grades possess superior stamping characteristics and formability with respect to Al alloys. But the need of light-weighting pushes towards the adoption of materials with optimal strenght-to-weight ratio, like Al alloys. Todays Al alloys are certainly used in the transport sector but their formability (at room temperature) is poorer than Deep Drawing Steel grades, which still hinders their massive implementation in the forming processes and drives the research toward innovative manufacturing solutions. One of the most promising approach to overcome such a limitation and, thus, manufacture complex component using cold forming processes, is the adoption of local heat treatments to obtain a suitable distribution of material properties able to enhance the formability at room temperature.The design of cold forming using locally modified blanks needs: (i) an extensive investigation of the material behaviour at room temperature after the local heating and (ii) the adoption of a Finite Element approach. As for the former aspect, the authors proposed a fast and comprehensive methodology to investigate the hardening behaviour of an Al alloy (AA5754-H32) locally annealed by laser heat treatment. Using a similar approach, the hardening model was then enriched by considering the normal anisotropy, evaluating the correlation between the Lankford parameter and the material condition reached at the end of the local treatment. To improve the knowledge on the plastic response of the material, the present work focusses on the characterization of the plane strain behavior of the AA5754, initially in wrought condition (H32) and subsequently modified by laser heating. In particular, the study proposes a new quasi-homogeneous specimen which combines the local heating profile with an optimized geometry to produce a prevailing plane strain condition in the heat-treated zone. In such a way, data about the material response in the plane strain condition could be obtained for a large range of material conditions determined by the preliminary heat treatment.

Characterization and Modelling of Sheet Material with Graded Strength for more Accurate Finite Element Analysis

Many sheet materials do not exhibit constant properties over the sheet thickness. On the one hand, this can be caused by a rolling process with a pass reduction of 1-2%, the so-called skin pass rolling. This is mainly used for deep drawing steel grades to eliminate the pronounced yield strength of the base material. Another possibility is abrasive blasting or shot peening of sheet metal. This causes plastic deformation of the sheet surface and material strengthening. The grading can be used to locally strengthen components or locally adapt the roughness. Since many production processes today are designed numerically, the mapping of such a grading is necessary but currently not implemented in FE code. Aim of this research is, to correlate the material hardness to a plastic pre-strain with a newly developed characterization method. For this purpose, graded material properties are generated by means of abrasive blasting as an example process. The resulitng locally varying work hardening over the sheet thickness is further integrated in finite element analysis by the assignment of a starting condition of a 2d-shell element formulation. With this approach, it is possible to map the graded material properties easily in FE simulations with a very good accuracy.

Material and Damage Characterization of the Elastoplastic Response of the EK4 Deep Drawing Steel

Although EK4 drawing steel is nowadays widely used to manufacture a great variety of parts, it exhibits a marked normal and planar anisotropy that can make it difficult to control the process during its forming. In order to achieve an accurate description of the elastoplastic material response in sheet forming operations, this work presents a detailed material and damage characterization of EK4 deep drawing steel through a two-step methodology involving both experiments and finite element simulations. Firstly, tensile tests on sheet samples cut along the rolling, diagonal and transverse directions were carried out. The corresponding measurements were used to calibrate the material parameters related to the following modeling approaches adopted in the present study: the Hollomon hardening law, the non-associated Hill-48 phenomenological constitutive model and the anisotropic Hosford-Coulomb ductile fracture criterion. Secondly, this characterization was assessed and validated in the numerical simulation of the technological Erichsen test in which the material is mainly subjected to a biaxial stress state. The obtained predictions show a good agreement when compared with the corresponding experimental measurements of the punch load–displacement curve and thickness radial profile at the final fracture stage of the sample.

Adapting the Surface Integrity of High-Speed Steel Tools for Sheet-Bulk Metal Forming

New manufacturing technologies, such as Sheet-Bulk Metal Forming, are facing the challenges of highly stressed tool surfaces which are limiting their service life. For this reason, the load-adapted design of surfaces and the subsurface region as well as the application of wear-resistant coatings for forming dies and molds made of high-speed steel has been subject to many research activities. Existing approaches in the form of grinding and conventional milling processes do not achieve the surface quality desired for the forming operations and therefore often require manual polishing strategies afterward. This might lead to an unfavorable constitution for subsequent PVD coating processes causing delamination effects or poor adhesion of the wear-resistant coatings. To overcome these restrictions, meso- and micromilling are presented as promising approaches to polishing strategies with varying grain sizes. The processed topographies are correlated with the tribological properties determined in an adapted ring compression test using the deep drawing steel DC04. Additionally, the influence of the roughness profile as well as the induced residual stresses in the subsurface region are examined with respect to their influence on the adhesion of a wear-resistant CrAlN PVD coating. The results prove the benefits of micromilling in terms of a reduced friction factor in the load spectrum of Sheet-Bulk Metal Forming as well as an improved coating adhesion in comparison to metallographic finishing strategies, which can be correlated to the processed roughness profile and induced compressive residual stresses in the subsurface region.

Laser-TIG welding of galvanized steel – numerical and experimental assessment of the effect of arc in various setups

The technology of laser-TIG welding utilizes the arc as a secondary heat source during laser welding. In TIG-leading configuration, the low-current arc precedes the beam to preheat the material. The numerical simulations representing various setups combining laser and arc were performed to study the changes of thermal cycles on the interface of thin metal sheets of overlap joint. The relations between the position of the arc towards the beam, additional heat input, and temperature gradients are discussed. The technology of laser-TIG welding of zinc-coated deep-drawing steel was experimentally applied in the same joint configuration. A good agreement between the calculated and experimental welds was achieved. The arc current less than 40 A did not cause the vaporization, neither oxidation of zinc coating on the interface surface of metal sheets. Nevertheless, the quality of laser-TIG welds was better compared to laser welds. The 40A arc current increased the heat input by about 50% and led to an almost 60% decrease in cooling rate compared to autonomous laser welding. Prolonged heating and cooling time are the key factors of improving the weld quality.

Analysis of forming capacity of HCT490X and DX57D depending on strain rate

To evaluate the forming capacity in sheet metal forming, experimentally determined Forming Limit Curves (FLC) are often used. These curves represent the forming limits of a material. The most widely used approach to experimentally determine the onset of necking of sheet metals is formulated in EN ISO 12004-2. The first goal of this work is to investigate the limit strains for dual-phase steel HCT490X (commonly referred to as DP500) with a nominal thickness of 0.6 mm and deep drawing steel DX57D with a nominal thickness of 0.7 mm. The second goal is to evaluate the influence of punch speed (resulting in strain rate) on the position of forming limit curve for materials HCT490X and DX57D. The FLC curve is measured with the Nakajima test on a universal testing machine BUP 600. The test setup and the shape of the specimens follow EN ISO 12004-2. The stochastic, isotropic, and high contrast pattern was applied on the specimens and was measured using a 3D photogrammetric system ARAMIS by GOM. The punch movement velocity was gradually changed, and the experimentally measured results are compared with numerical simulation in AutoForm Forming R8.