Authors:
Jemal Ebrahim Dessie Faculty of Mechanical Engineering and Informatics, Institute of Materials Science and Technology, University of Miskolc, Miskolc-Egyetemváros, Hungary

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Zsolt Lukacs Faculty of Mechanical Engineering and Informatics, Institute of Materials Science and Technology, University of Miskolc, Miskolc-Egyetemváros, Hungary

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Abstract

U-bending tests are the most common method to predict springback and are influenced by the process and geometrical variables in addition to material behaviour. It needs a numerical study at a high level with many variables to reduce try-out time and loop. In this study, the U-bending test of DC01 steel has been researched numerically and experimentally to govern the influential parameters. The numerical analysis was conducted using AutoForm-Sigma code. The die radius has an excessive influence on the change of flange angle than the punch radius, but the punch radius has the greatest influence on the variation of the sidewall angle. The coefficient of friction played a great impact on both flange and sidewall angle deviation and its influence grows stronger as the blank holding force increases.

Abstract

U-bending tests are the most common method to predict springback and are influenced by the process and geometrical variables in addition to material behaviour. It needs a numerical study at a high level with many variables to reduce try-out time and loop. In this study, the U-bending test of DC01 steel has been researched numerically and experimentally to govern the influential parameters. The numerical analysis was conducted using AutoForm-Sigma code. The die radius has an excessive influence on the change of flange angle than the punch radius, but the punch radius has the greatest influence on the variation of the sidewall angle. The coefficient of friction played a great impact on both flange and sidewall angle deviation and its influence grows stronger as the blank holding force increases.

1 Introduction

Sheet metal bending is one of the most popular manufacturing techniques in which a homogenous material deforms around an axis that is orthogonal to the specimen's length direction and situated in the neutral plane [1]. Even though metal bending is a straightforward procedure, springback frequently interferes with it and is challenging to account for. The change in geometry of a stamped item must be compensated for, which raises the cost of manufacture [1]. After the tool is removed from the part, the component's dimensions change, and the stresses and strains in the material that underwent deformation change also [2]. The stamped portion is typically overbent to counteract this effect. One of the most crucial elements affecting contacting surface characteristics of the tool and sheet is the Blank Holding Force (BHF) [3]. Shape variation after sheet forming is mostly influenced by contact surface pressure and friction, and tool geometries [4–7].

By regulating a few process variables, Wang et al. [7] focused on analyzing springback that occurs during the stretch bending of sheet metal. It was found that a smaller die corner radius led to a smaller amount of sidewall springback due to the smaller required bending moment. Jiang et al. [1] studied the friction coefficient and blank holder force that affect the springback and have a reverse relationship. Angsuseranee et al. [8] explored springback and sidewall curl prediction in the U-bending process through the three-level tool geometry parameters and BHF at the constant coefficient of friction. The study confirmed that the springback decreases as increasing the BHF at a smaller punch radius. Tong et al. [9] numerically proposed a simplified method for obtaining material characteristics related to springback in addition to geometrical and process parameters. The investigation reached that the springback angle on the side wall is almost independent of radius of die. A detailed material model that accurately captures the Bauschinger effect is also necessary for accurate springback predictions. The springback was also impacted by the sheet thickness [10–12]. It is crucial to conduct a thorough optimization analysis of how changes to all examined parameters affect the springback. Optimization of design variables is very critical and it needs a systematic approach [13].

In the ongoing research, the impact of frictional coefficient, BHF, and punch and die profile radius on springback prediction of the U-bending test for cold-rolled DC01 steel was investigated both numerically and experimentally. A commercial code AutoForm-Sigma was used for numerical investigation. AutoForm-Sigma enables a systematic improvement of the forming process. This is accomplished by varying the process and design parameters within a range that enables the development of safe processes [14]. High-level with multiple variables of Design Of Experiment (DOE) matrix has been simulated using Systematic Process Improvement (SPI). SPI increases transparency in the forming process by demonstrating, which design parameters influence the forming process and to what extent and it also helps to reduce the invested time and tryout loops, which is common in a conventional trial-and-error iterative approach to get the most efficient result. It is particularly true to estimate the coefficient of friction in a U-bending test because of difficult to calculate or measure in the physical experiment. As a result, it is possible to carry out experimental research at a low level of the design of experiment matrix to confirm some results of numerical simulation.

2 Numerical simulation of U-bending test

U-bending of 250 × 20 × 0.45 mm rectangular cold rolled DC01 steel sheet was performed numerically using AutoForm commercial code. A uniaxial tensile test was used to determine the mechanical characteristic of the material as it is shown in Table 1.

Table 1.

Mechanical and formability behavior of DC01

Elastic Modulus, E0, (GPa)Yield Stress, σ0, (MPa)Ultimate Tensile Strength (UTS), (MPa)Total Elongation, Ag, (%)
20619232224.5
Accurate springback prediction needs a material model which precisely describes the complex material behavior at loading-unloading conditions. In AutoForm commercial code, a novel approach has been developed and implemented to model the kinematic hardening behavior of the material [15, 16] as Eq. (1),
El=E0(1γ(1eχp)),
where El is the tangent modulus and which ordinarily falls off exponentially as comparable plastic strain p builds up; E0 is the tangent modulus at zero plastic strain; χ is the saturation constant; γ is the Young's reduction factor formulated as Eq. (2),
γ=(E0Ea)/E0,
where Ea is the Young's modulus at infinite plastic strain.
Transient softening rate K is expressed by the summation of linear and non-linear reverse strain as Eq. (3) [17],
εr=εrl+εrn=σrE1(p)+K×arctanh2(σr2σh(p))2,
where σh(p) is the reverse plastic strain dependent isotropic stress; σr is the reverse stress curve; εr is the total reverse strain; εrl is the linear reverse strain; and εrn is the nonlinear reverse strain. This study considered a pre-defined kinematic hardening behavior in the material card of AuthoForm as it is shown in Table 2.
Table 2.

Kinematic hardening behavior of DC01

γχK
0.24400.003

Multiple U-bending tests were numerically carried out automatically using SPI in a single simulation. Table 3 lists the design factors and their levels that were taken into consideration for the numerical analysis. The maximum BHF was decided because of the maximum compression force generated by the spring in the real physical experiment apparatus. Figure 1 shows the initial and the final geometrical setup before the removal of the punch.

Table 3.

Design variables and their level for the simulation

ParametersLevel
Radius of die profile, Rd, (mm)3–10
Radius of punch profile, Rp, (mm)3–10
BHF, (kN)3–9
Coefficient of friction, μ0.01–0.15
Fig. 1.
Fig. 1.

Geometrical setup, a) before forming, b) after forming

Citation: Pollack Periodica 18, 2; 10.1556/606.2023.00799

3 Experimental procedures

Some samples of the U-bending test experiment were conducted in dry and lubrication conditions at a constant 5 mm tool radius profiles with the intention of validating the influence of friction and BHF on springback parameters, which was achieved in the numerical investigation. Figure 2 shows the experimental setup of U-bending die equipment, which is installed on a 20 kN hydraulic press machine. Three-level spring-loaded BHF (at 3, 5, and 7 kN) was applied. The width of the punch was 40 mm and its half position matched the half position of the width of the sheet. The die and punch clearance on both sides was equal to double sheet thickness and the punch velocity was 5 mm s−1.

Fig. 2.
Fig. 2.

U-bending die apparatus

Citation: Pollack Periodica 18, 2; 10.1556/606.2023.00799

4 Results and discussion

After removing of the tool, the 3D profiles in the simulation at μ = 0.05 and BHF = 3 kN are shown in Fig. 3a and all 3D profiles in the physical experiments are provided in Fig. 3b. The amount of springback of each experiment and simulation was measured using NUMISHEET ’93 benchmark standard [18] as it is shown in Fig. 4. The angle θ1′ and θ2′ were calculated using a simple relationship in Eq. (4),
θ1=90θ1,θ2=θ290.
Fig. 3.
Fig. 3.

Springback profiles, a) simulation, b) experimental

Citation: Pollack Periodica 18, 2; 10.1556/606.2023.00799

Fig. 4.
Fig. 4.

Springback parameters in NUMISHEET ’93 benchmark [18]

Citation: Pollack Periodica 18, 2; 10.1556/606.2023.00799

The 2D picture of each sample in numerical and experimental results is exported to AutoCAD commercial drafting software to measure the springback parameters. Figure 5 shows the 2D profiles of all physical experiment samples after removing the tool. Numerical simulation at Rd = 3 and 4 mm and at Rp = 10 mm revealed distortion and excessive thinning. But in all other causes of process parameters, there were free from any distortion and excessive thinning.

Fig. 5.
Fig. 5.

2D profiles of physical experiment samples after removal of the tool

Citation: Pollack Periodica 18, 2; 10.1556/606.2023.00799

4.1 Effect of die radius

In the numerical simulation, Fig. 6a and b show the effect of the Rd on the springback angle on the flange θ1′ and side wall θ2′ at a different constant value of punch radius respectively. BHF was 3 kN and the coefficient of friction was 0.15 as constant in all causes. It can be seen in Fig. 6a that in the numerical simulation the springback angle θ1′ decrease with the increase of Rd because when Rd increases by a specific amount, the elastic strain created is no longer sufficient to cause any springback. It is interesting that the behavior of the angle θ1′ becomes negative when Rd > 8 mm at a constant value of Rp > 6 mm. This might be brought on by a further reduction in elastic strain close to the die corner. On the other hand, θ2′ has no significant effect on the Rd in the numerical simulation as it is shown in Fig. 6b. But, the value of constant Rp has a great impact on both θ1′ and θ2′, θ1′ decreases as increasing constant Rp but θ2′ increases with increasing constant Rp.

Fig. 6.
Fig. 6.

Effect of the die radius in the numerical simulation, a) on the flange angle, b) on the sidewall angle

Citation: Pollack Periodica 18, 2; 10.1556/606.2023.00799

4.2 Effect of punch radius

Figures 7a and b show the effect of the Rp on the springback angle θ1′ and θ2′ respectively, in the numerical simulation. BHF was 3 kN and the coefficient of friction was 0.15 as constant in all causes. θ1′ is not affected by Rp but is highly affected by the constant value of Rd as it is shown in Fig. 7a. Angle θ1′ decreases as increasing of constant value of Rd. It is clearly shown in Fig. 7b that θ2′ is directly proportional to Rp. It increases as increasing Rp due to a reduction in the required contact pressure. The constant value of Rd has no discernible effect for θ2′.

Fig. 7.
Fig. 7.

Effect of the punch radius in the numerical simulation, a) on the flange angle, b) on the sidewall angle

Citation: Pollack Periodica 18, 2; 10.1556/606.2023.00799

The change in Δθ1′ and Δθ2′ at different level constant variables of Rp and Rd is shown in Fig. 8 using Eq. (5),
Δθ1=θ1maxθ1min,Δθ2=θ2maxθ2min
Fig. 8.
Fig. 8.

Springback angle change, a) at constant Rp but variable Rd, b) at constant Rd but variable Rp

Citation: Pollack Periodica 18, 2; 10.1556/606.2023.00799

In Fig. 7a, Δθ1′ and Δθ2′ was calculated from the maximum and minimum value found from variable Rd at a different constant level of Rp. But in Fig. 8b, Δθ1′ and Δθ2′ were calculated from variable Rp at a different constant level of Rd. The angle Δθ1′ is higher at every constant Rp but smaller at constant Rd. However, there was an overall decreasing tendency with the increase of constant Rp and fluctuating in the cause of constant Rd but the opposite relationship was found for Δθ2′. After all, Rd is the most influential parameter for θ1′ and Rp is the most influential for θ2′. To generalize, the result clearly shows the smaller value of θ1′ and θ2′ was found from Rd = 10 mm and Rp = 3 mm.

4.3 Effect of BHF and coefficient of friction

Controlling the BHF and coefficient of friction is one of the traditional methods to compensate springback. In order to study the relationship of BHF on springback, different BHF levels were selected from 3 to 9 kN. The advantage of finite element simulations is that easy to study the influences of coefficients of friction on the deformation process. In this study, the friction coefficient between tools and workpieces in the numerical analysis and it was varied from 0.01 to 0.15. The Rd and Rp were set at 5 mm as a constant value. Figure 9 shows the springback parameters as a function of BHF at different levels of constant coefficient of friction. The angle θ1′ and θ2′ have inverse relationships with BHF in all simulations. Springback decreases as increasing BHF because of a more uniform distribution of strains through the sheet thickness. It is clearly seen from Fig. 9 that springback parameters were almost closer amount in dry condition forming at μ = 0.15 in the numerical simulation, on the other hand, the experimental test with lubrication condition forming was closer at between μ = 0.05–0.10.

Fig. 9.
Fig. 9.

Effect of BHF and coefficient of friction: a) on the flange angle; b) on the sidewall angle

Citation: Pollack Periodica 18, 2; 10.1556/606.2023.00799

In Fig. 10, Δθ1′ and Δθ2′ was also calculated from the maximum and minimum value found from variable BHF at a different constant level of coefficient of friction. Δθ2′ is higher at every constant coefficient of friction and it increases as the increasing coefficient of friction but Δθ1′ has the opposite relationship. Therefore, the coefficient of friction is most influential for θ2′ than θ1′ and the influence becomes higher as increasing BHF.

Fig. 10.
Fig. 10.

Springback angle change at different level of constant coefficient of friction but variable BHF

Citation: Pollack Periodica 18, 2; 10.1556/606.2023.00799

5 Conclusion

The effects of process and geometrical parameters on springback phenomenon in the U-bending process have been explored numerically and experimentally. The numerical investigation with experimental verification is important and capable of predicting springback very accurately. A numerical investigation of the high-level with multiple variables design of the experiment matrix has been conducted using SPI and it is important to reduce the real physical design of the experimental study. Based on this study, the following remarks are drawn:

  • The results produced by the analysis of the springback phenomenon using the finite element approach can be regarded as being sufficiently precise and valid;

  • Die radius was the most influential geometrical parameter at the flange springback but punch radius is the most influential at the sidewall springback;

  • The friction coefficient is one of the process variables that have a considerable impact on springback prediction, especially on the sidewall deviations. The influence becomes higher as increasing BHF.

References

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    H. J. Jiang and H. L. Dai, “A novel model to predict U-bending springback and time-dependent springback for a HSLA steel plate,” Int. J. Adv. Manuf. Technol., vol. 81, no. 5, pp. 10551066, 2015.

    • Search Google Scholar
    • Export Citation
  • [2]

    R. Srinivasan, D. Vasudevan, and P. Padmanabhan, “Influence of friction parameters on springback and bend force in air bending of electrogalvanized steel sheet: an experimental study,” J. Braz. Soc. Mech. Sci. Eng., vol. 36, no. 2, pp. 371776, 2014.

    • Search Google Scholar
    • Export Citation
  • [3]

    L. A. de Carvalho and Z. Lukács, “Application of enhanced coulomb models and virtual tribology in a practical study,” Pollack Period., vol. 17, no. 3, pp. 1923, 2022.

    • Search Google Scholar
    • Export Citation
  • [4]

    A. Ghaei, D. E. Green, and A. Aryanpour, “Springback simulation of advanced high strength steels considering nonlinear elastic unloading–reloading behavior,” Mater. Des., vol. 88, pp. 461470, 2015.

    • Search Google Scholar
    • Export Citation
  • [5]

    H. L. Dai, H. J. Jiang, T. Dai, W. L. Xu, and A. H. Luo, “Investigation on the influence of damage to springback of U-shape HSLA steel plates,” J. Alloys Compd., vol. 708, pp. 575586, 2017.

    • Search Google Scholar
    • Export Citation
  • [6]

    H. Li, G. Sun, G. Li, Z. Gong, D. Liu, and Q. Li, “On twist springback in advanced high-strength steels,” Mater. Des., vol. 32, no. 6, pp. 32723279, 2011.

    • Search Google Scholar
    • Export Citation
  • [7]

    A. Wang, K. Zhong, O. El Fakir, J. Liu, C. Sun, L. L. Wang, J. Lin, and T. A. Dean, “Springback analysis of AA5754 after hot stamping: experiments and FE modeling,” Int. J. Adv. Manuf. Technol., vol. 89, no. 5, pp. 13391352, 2017.

    • Search Google Scholar
    • Export Citation
  • [8]

    N. Angsuseranee, G. Pluphrach, B. Watcharasresomroeng, and A. Songkroh, “Springback and sidewall curl prediction in U-bending process of AHSS through finite element method and artificial neural network approach,” Songklanakarin J. Sci. Technol., vol. 40, no. 3, pp. 534539, 2018.

    • Search Google Scholar
    • Export Citation
  • [9]

    V. C. Tong and D. T. Nguyen, “A study on spring-back in U-draw bending of DP350 high-strength steel sheets based on combined isotropic and kinematic hardening laws, “Adv. Mech. Eng., vol. 10, no. 9, pages 113, 2018.

    • Search Google Scholar
    • Export Citation
  • [10]

    B. Chongthairungruang, V. Uthaisangsuk, S. Suranuntchai, and S. Jirathearanat, “Springback prediction in sheet metal forming of high strength steels,” Mater. Des., vol. 50, pp. 253266, 2013.

    • Search Google Scholar
    • Export Citation
  • [11]

    X. Yang, C. Choi, N. K. Sever, and T. Altan, “Prediction of springback in air-bending of advanced high strength steel (DP780) considering Young′ s modulus variation and with a piecewise hardening function,” Int. J. Mech. Sci., vol. 105, pp. 266272, 2016.

    • Search Google Scholar
    • Export Citation
  • [12]

    A. J. Aday, “Analysis of springback behavior in steel and aluminum sheets using FEM,” Ann. de Chim. Sci. des Materiaux, vol. 43, no. 2, pp. 9598, 2019.

    • Search Google Scholar
    • Export Citation
  • [13]

    K. Jármai and M. Petrik, “Optimization of asymmetric I-beams for minimum welding shrinkage,” Pollack Period., vol. 16, no. 3, pp. 3944, 2021.

    • Search Google Scholar
    • Export Citation
  • [14]

    AutoForm Engineering GmbH. [Online]. Available: https://www.autoform.com/. Accessed: Oct. 10, 2022.

  • [15]

    W. Kubli, A. Krasovskyy, and M. Sester, “Modeling of reverse loading effects including workhardening stagnation and early re-plastification,” Int. J. Mater. Forming, vol. 1, no. 1, pp. 145148, 2008.

    • Search Google Scholar
    • Export Citation
  • [16]

    L. Wagner, M. Wallner, P. Larour, K. Steineder, and R. Schneider, “Reduction of Young’s modulus for a wide range of steel sheet materials and its effect during springback simulation,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1157, 2021, Paper no. 012031.

    • Search Google Scholar
    • Export Citation
  • [17]

    M. Tisza and Z. Lukács, “Formability investigations of high-strength dual-phase steels,” Acta Metallurgica Sinica, vol. 28, no. 12, pp. 14711481, 2015.

    • Search Google Scholar
    • Export Citation
  • [18]

    NUMISHEET'93, Proceedings of the 2nd International Conference Numerical Simulation of 3-D Sheet Metal Forming Processes; Verification of Simulation with Experiment, Isehara, Japan, August 31–September 2, 1993.

  • [1]

    H. J. Jiang and H. L. Dai, “A novel model to predict U-bending springback and time-dependent springback for a HSLA steel plate,” Int. J. Adv. Manuf. Technol., vol. 81, no. 5, pp. 10551066, 2015.

    • Search Google Scholar
    • Export Citation
  • [2]

    R. Srinivasan, D. Vasudevan, and P. Padmanabhan, “Influence of friction parameters on springback and bend force in air bending of electrogalvanized steel sheet: an experimental study,” J. Braz. Soc. Mech. Sci. Eng., vol. 36, no. 2, pp. 371776, 2014.

    • Search Google Scholar
    • Export Citation
  • [3]

    L. A. de Carvalho and Z. Lukács, “Application of enhanced coulomb models and virtual tribology in a practical study,” Pollack Period., vol. 17, no. 3, pp. 1923, 2022.

    • Search Google Scholar
    • Export Citation
  • [4]

    A. Ghaei, D. E. Green, and A. Aryanpour, “Springback simulation of advanced high strength steels considering nonlinear elastic unloading–reloading behavior,” Mater. Des., vol. 88, pp. 461470, 2015.

    • Search Google Scholar
    • Export Citation
  • [5]

    H. L. Dai, H. J. Jiang, T. Dai, W. L. Xu, and A. H. Luo, “Investigation on the influence of damage to springback of U-shape HSLA steel plates,” J. Alloys Compd., vol. 708, pp. 575586, 2017.

    • Search Google Scholar
    • Export Citation
  • [6]

    H. Li, G. Sun, G. Li, Z. Gong, D. Liu, and Q. Li, “On twist springback in advanced high-strength steels,” Mater. Des., vol. 32, no. 6, pp. 32723279, 2011.

    • Search Google Scholar
    • Export Citation
  • [7]

    A. Wang, K. Zhong, O. El Fakir, J. Liu, C. Sun, L. L. Wang, J. Lin, and T. A. Dean, “Springback analysis of AA5754 after hot stamping: experiments and FE modeling,” Int. J. Adv. Manuf. Technol., vol. 89, no. 5, pp. 13391352, 2017.

    • Search Google Scholar
    • Export Citation
  • [8]

    N. Angsuseranee, G. Pluphrach, B. Watcharasresomroeng, and A. Songkroh, “Springback and sidewall curl prediction in U-bending process of AHSS through finite element method and artificial neural network approach,” Songklanakarin J. Sci. Technol., vol. 40, no. 3, pp. 534539, 2018.

    • Search Google Scholar
    • Export Citation
  • [9]

    V. C. Tong and D. T. Nguyen, “A study on spring-back in U-draw bending of DP350 high-strength steel sheets based on combined isotropic and kinematic hardening laws, “Adv. Mech. Eng., vol. 10, no. 9, pages 113, 2018.

    • Search Google Scholar
    • Export Citation
  • [10]

    B. Chongthairungruang, V. Uthaisangsuk, S. Suranuntchai, and S. Jirathearanat, “Springback prediction in sheet metal forming of high strength steels,” Mater. Des., vol. 50, pp. 253266, 2013.

    • Search Google Scholar
    • Export Citation
  • [11]

    X. Yang, C. Choi, N. K. Sever, and T. Altan, “Prediction of springback in air-bending of advanced high strength steel (DP780) considering Young′ s modulus variation and with a piecewise hardening function,” Int. J. Mech. Sci., vol. 105, pp. 266272, 2016.

    • Search Google Scholar
    • Export Citation
  • [12]

    A. J. Aday, “Analysis of springback behavior in steel and aluminum sheets using FEM,” Ann. de Chim. Sci. des Materiaux, vol. 43, no. 2, pp. 9598, 2019.

    • Search Google Scholar
    • Export Citation
  • [13]

    K. Jármai and M. Petrik, “Optimization of asymmetric I-beams for minimum welding shrinkage,” Pollack Period., vol. 16, no. 3, pp. 3944, 2021.

    • Search Google Scholar
    • Export Citation
  • [14]

    AutoForm Engineering GmbH. [Online]. Available: https://www.autoform.com/. Accessed: Oct. 10, 2022.

  • [15]

    W. Kubli, A. Krasovskyy, and M. Sester, “Modeling of reverse loading effects including workhardening stagnation and early re-plastification,” Int. J. Mater. Forming, vol. 1, no. 1, pp. 145148, 2008.

    • Search Google Scholar
    • Export Citation
  • [16]

    L. Wagner, M. Wallner, P. Larour, K. Steineder, and R. Schneider, “Reduction of Young’s modulus for a wide range of steel sheet materials and its effect during springback simulation,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1157, 2021, Paper no. 012031.

    • Search Google Scholar
    • Export Citation
  • [17]

    M. Tisza and Z. Lukács, “Formability investigations of high-strength dual-phase steels,” Acta Metallurgica Sinica, vol. 28, no. 12, pp. 14711481, 2015.

    • Search Google Scholar
    • Export Citation
  • [18]

    NUMISHEET'93, Proceedings of the 2nd International Conference Numerical Simulation of 3-D Sheet Metal Forming Processes; Verification of Simulation with Experiment, Isehara, Japan, August 31–September 2, 1993.

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  • Dražan Kozak (Faculty of Mechanical Engineering, Josip Juraj Strossmayer University of Osijek, Croatia)
  • György L. Kovács (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Balázs Géza Kövesdi (Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Engineering and Economics, Budapest, Hungary)
  • Tomáš Krejčí (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Jaroslav Kruis (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Miklós Kuczmann (Department of Automations, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary)
  • Tibor Kukai (Department of Engineering Studies, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Maria Jesus Lamela-Rey (Departamento de Construcción e Ingeniería de Fabricación, University of Oviedo, Spain)
  • János Lógó  (Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Hungary)
  • Carmen Mihaela Lungoci (Faculty of Electrical Engineering and Computer Science, Universitatea Transilvania Brasov, Romania)
  • Frédéric Magoulés (Department of Mathematics and Informatics for Complex Systems, Centrale Supélec, Université Paris Saclay, France)
  • Gabriella Medvegy (Department of Interior, Applied and Creative Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Tamás Molnár (Department of Visual Studies, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Ferenc Orbán (Department of Mechanical Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Zoltán Orbán (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Dmitrii Rachinskii (Department of Mathematical Sciences, The University of Texas at Dallas, Texas, USA)
  • Chro Radha (Chro Ali Hamaradha) (Sulaimani Polytechnic University, Technical College of Engineering, Department of City Planning, Kurdistan Region, Iraq)
  • Maurizio Repetto (Department of Energy “Galileo Ferraris”, Politecnico di Torino, Italy)
  • Zoltán Sári (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Grzegorz Sierpiński (Department of Transport Systems and Traffic Engineering, Faculty of Transport, Silesian University of Technology, Katowice, Poland)
  • Zoltán Siménfalvi (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Andrej Šoltész (Department of Hydrology, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • Zsolt Szabó (Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Hungary)
  • Mykola Sysyn (Chair of Planning and Design of Railway Infrastructure, Institute of Railway Systems and Public Transport, Technical University of Dresden, Germany)
  • András Timár (Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Barry H. V. Topping (Heriot-Watt University, UK, Faculty of Engineering and Information Technology, University of Pécs, Hungary)

POLLACK PERIODICA
Pollack Mihály Faculty of Engineering
Institute: University of Pécs
Address: Boszorkány utca 2. H–7624 Pécs, Hungary
Phone/Fax: (36 72) 503 650

E-mail: peter.ivanyi@mik.pte.hu 

or amalia.ivanyi@mik.pte.hu

Indexing and Abstracting Services:

  • SCOPUS
  • CABELLS Journalytics

 

2022  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
14
Scimago
Journal Rank
0.298
Scimago Quartile Score

Civil and Structural Engineering (Q3)
Computer Science Applications (Q3)
Materials Science (miscellaneous) (Q3)
Modeling and Simulation (Q3)
Software (Q3)

Scopus  
Scopus
Cite Score
1.4
Scopus
CIte Score Rank
Civil and Structural Engineering 256/350 (27th PCTL)
Modeling and Simulation 244/316 (22nd PCTL)
General Materials Science 351/453 (22nd PCTL)
Computer Science Applications 616/792 (22nd PCTL)
Software 344/404 (14th PCTL)
Scopus
SNIP
0.861

2021  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
12
Scimago
Journal Rank
0,26
Scimago Quartile Score Civil and Structural Engineering (Q3)
Materials Science (miscellaneous) (Q3)
Computer Science Applications (Q4)
Modeling and Simulation (Q4)
Software (Q4)
Scopus  
Scopus
Cite Score
1,5
Scopus
CIte Score Rank
Civil and Structural Engineering 232/326 (Q3)
Computer Science Applications 536/747 (Q3)
General Materials Science 329/455 (Q3)
Modeling and Simulation 228/303 (Q4)
Software 326/398 (Q4)
Scopus
SNIP
0,613

2020  
Scimago
H-index
11
Scimago
Journal Rank
0,257
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
340/243=1,4
Scopus
Cite Score Rank
Civil and Structural Engineering 219/318 (Q3)
Computer Science Applications 487/693 (Q3)
General Materials Science 316/455 (Q3)
Modeling and Simulation 217/290 (Q4)
Software 307/389 (Q4)
Scopus
SNIP
1,09
Scopus
Cites
321
Scopus
Documents
67
Days from submission to acceptance 136
Days from acceptance to publication 239
Acceptance
Rate
48%

 

2019  
Scimago
H-index
10
Scimago
Journal Rank
0,262
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
269/220=1,2
Scopus
Cite Score Rank
Civil and Structural Engineering 206/310 (Q3)
Computer Science Applications 445/636 (Q3)
General Materials Science 295/460 (Q3)
Modeling and Simulation 212/274 (Q4)
Software 304/373 (Q4)
Scopus
SNIP
0,933
Scopus
Cites
290
Scopus
Documents
68
Acceptance
Rate
67%

 

Pollack Periodica
Publication Model Hybrid
Submission Fee none
Article Processing Charge 900 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription fee 2023 Online subsscription: 336 EUR / 411 USD
Print + online subscription: 405 EUR / 492 USD
Subscription Information Online subscribers are entitled access to all back issues published by Akadémiai Kiadó for each title for the duration of the subscription, as well as Online First content for the subscribed content.
Purchase per Title Individual articles are sold on the displayed price.

 

Pollack Periodica
Language English
Size A4
Year of
Foundation
2006
Volumes
per Year
1
Issues
per Year
3
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1788-1994 (Print)
ISSN 1788-3911 (Online)

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Nov 2023 0 64 24
Dec 2023 0 190 16
Jan 2024 0 223 11
Feb 2024 0 222 16
Mar 2024 0 129 14
Apr 2024 0 24 11
May 2024 0 0 0