Numerical simulation and optimization of fine-blanking process for copper alloy sheet

The difference between traditional blanking and the fine-blanking process is that fine-blanking is a precise manufacturing process, no post-processing, and better efficiency and cost thanks to smaller die clearance and higher blanking quality than traditional processes. Fine-blanking process is widely used in aerospace and automobile production.

Thipprakmas et al. [1] improved the fine-blanking process by predicting the V-ring indenter’s geometric parameter optimization and influence using finite element analysis, Taguchi method, and ANOVA. Yin et al. [2] created a mathematical life model for fine-blanking die using backpropagation neural network to gain the inner law and behavior between die wear and working parameters, thereby enabling wear prediction for fine-blanking die. Liu et al. [3] used finite element analysis and experiment to improve fractures that occurred to complex parts during the fine-blanking process, which mainly focused on the effects of surface quality by the angle of nearby edges, the height of ridges, and parametric designing methods for complex parts. Wang et al. [4] proposed a new blanking method by replacing V-ring indenter using slot structure, which creates high hydrostatic stress, to improve tearing; the proposed method creates a buffed surface along almost full blank thickness, eliminating most tearing effects. Liu et al. [5] proposed an optimization analysis which consists of minimizing die roll dimensions, an indenter mechanism set on cutting edge, and finite element analysis to validate the optimization; the study shows the optimized fine-blanking die can limit blanking deformation and the increase of die roll. Zheng et al. [6] discussed the effects of die roll dimension for evaluating product quality due to changes to product shape and material, the evolution of blank holder structure, and blanking press, which acts as a crucial part of fine-blanking process quality control in electric automotive industries. Karbaukh et al. [7] proposed the multiple different wedge-shaped knives for rolled-stock cutting equipment and process, which converted deformation energy from machine frame and drive into hydraulic press, namely, a stress concentrator for effective cutting work. Sahli et al. [8] proposed an optimization process for cold-rolled steel cutting, which consists of parameters such as burr condition, cutting edge appearance, and dimension, using the numerical analysis method.

In the fine-blanking process, the directions of blanking and shearing are related to the anisotropic mechanical properties of the metal blank and orientation of crystallites. According to observations of the side microstructure in fine-blanking products, the blanking and shearing directions can be divided into the deformation, shear, and failure zones. The current fine-blanking process tends to reduce the size of the deformation zone and improve the initiation and growth of microcracks in the failure zone. The literature has mostly discussed the application of FEA combined with a damage model to simulate the damage behavior of the shear plane [9] and die design to control the quality of the shear surface [10]. Zhao et al. [11] conducted microstructure observation to determine the fracture failure mode in the shear zone of the DP600 steel plate fine-blanking process. A self-adjustable triaxial stress model was adopted to simulate the damage behavior of the shear zone. Wang et al. [12] used a scanning electron microscope to observe the microstructure of the shear surface of an ultra-thin blank and employed the Abaqus software package to simulate a three-dimensional fine-blanking process on the basis of effective plastic strain and the Gurson–Tvergaard–Needleman shear correction model. In addition, Barik et al. [13] used Simulation software ABAQUS to change the forming and geometric parameters to perform FEA on the fine-blanking process of aluminum alloy AA6082-T6. An experimental design method was employed to optimize the fine-blanking process and the effect of forming and geometric parameters on burr formation on the metal blank after fine-blanking. This method can improve product quality and reduce production costs. Tanaka et al. [14] used FEA to calculate the ratio of the shear plane to the fracture surface and evaluated the ductile fracture criteria of Oyane, Cockcroft, and Latham using four punching methods. The results indicated that various types of shear surface will be affected by the clearance between the punch and die, clamping method of the blank, and selection of ductile fracture criteria.

A servo die cushion is a forming method that can self-adjust the blank holder force in the press equipment with high speed and stability. The servo press exhibits an adjustable curvilinear motion on the upper press, and the servo die cushion can adjust the blank holder force arbitrarily. Therefore, blanking processes that cannot be formed by conventional machinery or hydraulic presses can be achieved according to the forming conditions required by different blanking processes. At present, literature on servo die cushions involve servomotor-related technology and metal-forming industry applications [15], the use of high-frequency impulsive vibration to improve forming quality and efficiency [16], and the use of FEA and remeshing to define the standard in blanking formability analysis [17]. Kim et al. [18] proposed computer-aided engineering analysis combined with an experimental design method to confirm the technical methods required by the servo press in the forging process. The experimental design method could effectively optimize the pressure distribution required for the forming process of continuously variable pulleys and spur gears. Olguner and Bozdana [19] proposed a finite element model for a dual-phase steel plate to evaluate the reduction of steel plate thickness and the change of forming load in the pulse pressure distribution curve during the drawing process. They also changed the frequency and amplitude of the pulse pressure to perform elastoplastic FEA with DEFORM-3D. Fallahiarezoodar et al. [20] applied a servo die cushion to the drawing and formation of the U-channel of Al 5182-O. A blank was stretched and bent when it was near the rounded corner of the cavity; it was not bent when it passed the rounded corner. The reversing load generated by the bending process produced a residual stress distribution in the direction of the blank section. The application of the servo die cushion reduced the residual stress distribution and wall springback. Moreover, Esmaeilpour et al. [21] used a servo press and die cushion to form three aluminum alloy sheets in a cross shape. The uniaxial tensile test was used to construct the anisotropic properties of the aluminum alloy sheet, and the Argus system was used to measure the strain of the draw forming. The formed part was cut in two different directions using a water-jet machine to measure the thickness change after forming. The study used Yld2000-2D and Hill’s 48 yield function for numerical simulation analysis and comparison with experimental values.

The most common problem encountered in the process of blanking products is how to reduce costs and improve quality, which is generally related to the selection of process parameters, die geometry, and blank material. In the industry, the trial and error method is often used for tryout process, but this relies on rich experience and involves huge costs, and discovering solutions for nonlinear problems could be difficult. In the blanking process, the fracture zone and die roll zone are the surface features that must be produced on the shear surface. Both zones will affect the size and performance of the finished product and even cause problems such as secondary processing. Therefore, the ability to control the size of the fracture and die roll zones in the blanking process is crucial.

The present study used a thickness 2.5 mm C1100 copper plate central processing unit (CPU) thermal heat spreader as an example; the relevant dimensions is shown in Fig. 1, to investigate the key process parameters of fine-blanking, including the effects of blanking velocity, V-ring height of the blank holder, V-ring height of the cavity, V-ring shape angle, V-ring position, blank holder force, counter punch force, and die clearance on the fracture zone depth and die roll zone width. First, this study performed a tensile test simulation analysis on the C1100 copper plate and imported the test parameters into the DEFORM software package to obtain the critical damage value of the material. Subsequently, the Taguchi orthogonal array L₁₈ (2¹ × 3⁷) experiment combination was used to conduct a fine-blanking simulation analysis. Moreover, the fracture zone depth and die roll zone width were set as the quality objectives for process optimization analysis, where the Taguchi method’s smaller-the-better quality design was used to identify the correlation between process parameters and quality objectives through ANOVA. Furthermore, the optimal combination of process parameters was obtained through the robust multicriteria optimal approach and Pareto-optimal solutions. Finally, experiments were performed to verify the accuracy of the results of the optimal process parameter combination analysis.

The greatest differences between a fine-blanking die and a general blanking die are the die clearance, V-ring, and counter punch in the cavity. The die clearance is approximately 0.5–1.5% of the blank thickness, and the shear surface of the finished product occupies more than 80% of the shear surface. V-rings are commonly added to the blanks and cavities to increase the precompression internal stress of the blanks for reducing the generation of tensile stress, thereby improving the plasticity of the material, slowing the generation of the fracture zone, and preventing blanks in the waste area from flowing into the finished product area. The function of the counter punch in the cavity is to press the material on the back during shearing, mainly to increase the precompression internal stress inside it to increase its plasticity and restrain the material to ensure greater flatness.

The fine-blanking process is divided into three stages, as depicted in Fig. 2. Before blanking, the blank is clamped and fixed by the blank holder and the cavity, the punch descends, and the counter punch contacts the blank simultaneously. During blanking, the counter punch exerts ejection force to clamp the material and perform the blanking while pressing the punch into the cavity. After blanking, the counter punch continues to support the material and stops at the end position after the punch is pressed into the cavity and exceeds the thickness of the blank. After the punch rises and is demolded, the blank is pushed out of the cavity to complete the fine-blanking process. The aforementioned actions of the counter punch can be completed by a servo die cushion.

To meet practical requirements, a fine-blanking die was designed with the CPU thermal heat spreader parts as an example. The die structure is presented in Fig. 3. The upper die system included an upper die set, upper block, upper die base, nitrogen gas spring, punch, punch fix plate, punch back plate, blank holder plate, holder back plate, and guide post. The lower die system included a lower die set, lower die base, counter punch, servo cushion block, cavity insert, cavity plate, cavity back plate, and bushing. First, the blank was placed on the cavity plate for positioning. During blanking, the blank was clamped by the blank holder through the force exerted by the nitrogen gas spring. The blank was pressed into the cavity simultaneously by the punch, and the counter punch in the cavity was pressed against the blank by the servo die cushion to generate an ejection force. In cooperation with the servo die cushion, the die was sheared from top to bottom. Upon completion, the upper die system opened, the finished blank was ejected from the cavity by the counter punch, and the blank could be removed. The thickness 2.5 mm C1100 copper plate was 99.9% copper. The blank was 60 mm long × 70 mm wide. In addition, this study used a servo press (SEYI SD1-200, Shieh Yih Machinery Industry, Taiwan) for the fine-blanking experiments, as depicted in Fig. 4. The forming tonnage of the servo press was 200 ton, the maximum output tonnage of the servo die cushion was 15 ton, and the maximum forming speed was 50 strokes/min. The forming motion curves can be set according to different forming requirements, and the servo die cushion can be matched with said motion to directly control the stroke and speed.

In the simulation analysis, a tensile test had to be conducted to obtain the mechanical properties of the copper alloy C1100. The size of the tensile test piece conformed to ASTM E8M specifications, and the size and test piece are presented in Fig. 5. This study used a 100 kN MTS 810 dynamic tensile testing machine to measure the mechanical properties of the copper alloy C1100. The tensile speed of the tensile test was 1 mm/min. The tensile test could only obtain the load–elongation curve, whereas the true stress and true strain were based on the actual cross-sectional area and length of the test piece under the load. Therefore, the load–elongation curve of the material had to be converted into a true stress–true strain curve. The converted maximum true stress and true strain of the material are 352MPa and 0.42, respectively. The fitting curve is stretched to 1.0 in strain for illustrating the failure of high-formability material. The Ludwik hardening law is used for fitting true stress–strain curves after necking. Figure 6 depicts the true stress (σ)–true strain (ε) curve of the copper alloy C1100. The curve fitting formula is shown as follows:

To present a blank fracture situation during shearing, ductile fracture criteria must be used as the basis for the blank fracture during the simulation analysis. This study used the normalized Cockcroft–Latham fracture criterion [25] as the critical damage value in simulation analysis. The formula is shown as follows:

where C represents failure value, \({\overline{\upvarepsilon}}_{\mathrm{f}}\) represents equivalent strain when deforming, \(\overline{\upsigma}\) represents equivalent stress, and σ^∗ represents maximum primary stress. The formula above calculates the maximum failure value by integrating ratios of the material’s primary and equivalent stress with equivalent strain. Failure occurs to the material as soon as the energy function of unit volume reaches the limit. Verification of failure value is done by comparing simulation and experiment of the tensile stress test and fine-blanking process [25]. Consistency is shown in both the tensile stress test and the fine-blanking process [26, 27]. Therefore, to obtain the critical damage value of the copper alloy C1100, the thickness of the material at the necking fracture of the test piece was measured after the tensile test was completed, and then the material tensile test simulation analysis was performed. The analysis model of the tensile test is illustrated in Fig. 7. This study simulated the thickness at the necking fracture to obtain the normalized Cockcroft–Latham critical damage value required by the material, as demonstrated in Fig. 8. The critical damage value of copper alloy in the C1100 tensile test analysis simulation was 2.04. Finally, the obtained mechanical properties of the material were incorporated into DEFORM 2D and DEFORM 3D software for simulation analysis. The load–elongation figure of simulation and experiment, shown in Fig. 9, proves similar results.

This study employed the fracture zone depth and die roll zone width of the CPU thermal heat spreader as the quality objectives, as illustrated in Fig. 10. The fracture zone depth was required to be as small as possible to obtain satisfactory size tolerance and avoid secondary processing problems. In addition, to avoid the reduction of the contact area of the finished product, which leads to a poor heat dissipation effect, the die roll zone width had to be as small as possible.

This study adopted Taguchi methods as the experimental design. These are systematic and effective methods that greatly reduce the number of experiments and can determine the optimal design parameter values that meet performance and cost requirements. The first step was to determine the key parameters. The most crucial fine-blanking process parameters that affect the fracture zone depth and die roll zone width in fine-blanking include the blanking velocity, V-ring height of the blank holder, V-ring height of the cavity, V-ring shape angle, V-ring position, blank holder force, counter punch force, and die clearance, which are illustrated in Fig. 11. In this experiment, the L₁₈ (2¹ × 3⁷) orthogonal array was adopted. The process parameters and levels are explained as follows:

The aforementioned experimental parameters and levels (as presented in Table 1) were configured for the L₁₈ (2¹ × 3⁷) Taguchi orthogonal array, as presented in Table 2.

This study used DEFORM 2D to simulate the fine-blanking process and optimize the process parameters. The analysis model included the punch, blank holder, blank, counter punch, and cavity, as depicted in Fig. 12a. The CATIA software package was used to draw the geometry of the experimental die and import it into DEFORM-2D. The blank material was set as an elastoplastic body, while the punch, blank holder, cavity, and counter punch were set as a rigid body. Approximately 10,000 meshes were established on the blank, and local mesh refinement was performed in the shear and deformation zones. The size of the generated mesh was approximately 0.04 × 0.04 mm², and the friction coefficient was set to 0.12 according to T. S. Kwak et al. [23]. To prevent excessive deformation of the mesh or it penetrating into the die, the mesh was automatically refined when the interference was greater than 0.01 mm to prevent calculation divergence. The simulation process used an isotropic elastic–plastic material model to define the deformation behavior of the blank, the true stress–true strain curve obtained by the tensile test, and the critical damage value obtained by the tensile test simulation analysis, which were imported into the FEA software. The simulation was of the blanking process from the punch to the end position after the punch contacted the blank, as depicted in Fig. 12b.

To effectively determine the die roll zone width and fracture zone depth, the contour line of the shear cross-section by FEA results were converted into IGS files and imported into CATIA software. During the blanking process, the shear cross-section of the blank was divided into the fracture, shear, and die roll zones from top to bottom, as presented in Fig. 13. Therefore, the contour line of the shear cross-section above and below the shear zone was defined as the fracture zone and the die roll zone, respectively. The shearing surface of the blank was close to the cavity wall during the blanking process, and shearing zone was a vertical line \(\overline{ab}\). The vertical distance from point a to point c (top side of the blank) is the fracture zone depth \(\overline{ac}\). The vertical distance from point b to point d (bottom side of the blank) is the die roll zone depth \(\overline{bd}\). The horizontal distance from point e (end point of the horizontal line on the bottom side) to the vertical line \(\overline{ab}\) is the die roll zone width.

In this study, the fine-blanking of a CPU thermal heat spreader was used as an example to optimize the process parameters. To reduce the fracture zone depth and die roll zone width, single-objective optimization of the fine-blanking parameters was performed. Subsequently, the effect of the fine-blanking parameters on each quality objective for robust multicriteria optimal design was analyzed, and the optimal design results were verified with experiments. The research results can be summarized as follows: