Open Access
Issue
Manufacturing Rev.
Volume 12, 2025
Article Number 21
Number of page(s) 11
DOI https://doi.org/10.1051/mfreview/2025016
Published online 26 September 2025
  1. M. Dalbosco, G. da Silva Lopes, P.D. Schmitt et al., Improving fatigue life of cold forging dies by finite element analysis: a case study, J. Manuf. Process. 64 (2021) 349–355 [Google Scholar]
  2. Z. Gronostajski, M. Hawryluk, The main aspects of precision forging, Arch. Civ. Mech. Eng. 8 (2008) 39–55 [Google Scholar]
  3. D.J. Politis, N.J. Politis, J. Lin et al., A review of force reduction methods in precision forging axisymmetric shapes, Int. J. Adv. Manuf. Technol. 97 (2018) 2809–2833 [Google Scholar]
  4. A.A. Emamverdian, S. Yu, C.P. Cao et al., Current failure mechanisms and treatment methods of hot forging tools (dies) − a review, Eng. Fail. Anal. 129 (2021) 105678 [Google Scholar]
  5. A. Srikanth, N. Zabaras, Shape optimization and preform design in metal forming processes, Comput. Methods Appl. Mech. Eng. 190 (2000) 1859–901 [Google Scholar]
  6. H. Cho, J. Choi, G. Min et al., An upper-bound analysis of the closed-die forging of spur gears, J. Mater. Process. Technol. 67 (1997) 83–88 [Google Scholar]
  7. W. Yeh, M.C. Wu, A variational upper-bound method for analysis of upset forging of rings, J. Mater. Process. Technol. 170 (2005) 392–402 [Google Scholar]
  8. D.W. Zhang, H. Yang, Z.C. Sun, Analysis of local loading forming for titanium-alloy T-shaped components using slab method, J. Mater. Process. Technol. 210 (2010) 258–266 [Google Scholar]
  9. G. Samolyk, Z. Pater, Application of the slip-line field method to the analysis of die cavity filling, J. Mater. Process. Technol. 153–154 (2004) 729–735 [Google Scholar]
  10. J. Liu, Z.S. Cui, Hot forging process design and parameters determination of magnesium alloy AZ31B spur bevel gear, J. Mater. Process. Technol. 209 (2009) 5871–5880 [Google Scholar]
  11. M.Y. Cao, C.L. Hu, X.W. Zhuang et al., Study on the manufacturing process for enhancing thehigh-pressure capability of stainless-steel common rails, Int. J. Adv. Manuf. Technol. 127 (2023) 2447–2463 [Google Scholar]
  12. L.M. Alves, P.A.F. Martins, Flexible forming tool concept for producing crankshafts, J. Mater. Process. Technol. 211 (2011) 467–474 [Google Scholar]
  13. J. Cochet, S. Thuillier, P.Y. Manach et al., Thermo-mechanical forming of a large sling shackle, Int. J. Adv. Manuf. Technol. 86 (2016) 1573–1591 [Google Scholar]
  14. J. Cochet, S. Thuillier, N. Decultot et al., Investigation of the key process parameters in the hot forming of a shackle, Int. J. Adv. Manuf. Technol. 105 (2019) 3209–3219 [Google Scholar]
  15. M.H.A. Bonte, L. Fourment, T. Do et al., Optimization of forging processes using finite element simulations: a comparison of sequential approximate optimization and other algorithms, Struct. Multidisc. Optimiz. 42 (2010) 797–810 [Google Scholar]
  16. R. Hino, A. Sasaki, F. Yoshida et al., A new algorithm for reduction of number of press-forming stages in forging processes using numerical optimization and FE simulation, Int. J. Mech. Sci. 50 (2008) 974–983 [Google Scholar]
  17. Z. Kang, Y. Luo, Sensitivity analysis of viscoplastic deformation process with application to metal preform design optimization, Eng. Optimiz. 44 (2012) 1511–1523 [Google Scholar]
  18. V. Janakiraman, R. Saravanan, Concurrent optimization of machining process parameters and tolerance allocation, Int. J. Adv. Manufact. Technol. 51 (2010) 357–369 [Google Scholar]
  19. M. Ozturk, S. Kocaoglan, F.O. Sonmez, Concurrent design and process optimization of forging, Comput. Struct. 167 (2016) 24–36 [Google Scholar]
  20. D.J. Kim, B.M. Kim, J.C. Choi, Determination of the initial billet geometry for a forged product using neural networks, J. Mater. Process. Technol. 72 (1997) 86–93 [Google Scholar]
  21. H. Tumer, F.O. Sonmez, Optimum shape design of die and preform for improved hardness distribution in cold forged parts, J. Mater. Process. Technol. 209 (2009) 1538–1549 [Google Scholar]
  22. D. Vieilledent, L. Fourment, Shape optimization of axisymmetric preform tools in forging using a direct differentiation method, Int. J. Numer. Meth. Eng. 52 (2001) 1301–1321 [Google Scholar]
  23. S. Sharma, M. Sharma, V. Gupta et al., A systematic review of factors affecting the process parameters and various measurement techniques in forging processes, Steel. Res. Int. 94 (2023) n/a [Google Scholar]
  24. O. Kinzle, K.V. Spies, Die Gestahltung der Zwischenformen für Gesenkschmiedestucke, Werkstattstechnik und maschinenbau, Vol. 47, 1957, pp. 175–181 (in German) [Google Scholar]
  25. G.P. Teterin, I.J. Tarnovsky, A.A. Chechik, Criterion of complexity of the configuration of forgings, Kuznechno Shtanmpovochnoe Proizvodstvo, 7 (1966) 6–8 (in Russian) [Google Scholar]
  26. G.Q. Zhao, E. Wright, R.V. Grandhi, Forging preform design with shape complexity control in simulating backward deformation, Int. J. Mach. Tool. Manuf. 35 (1995) 1225–1239 [Google Scholar]
  27. R. Hosseini-Ara, P. Yavari, A new criterion for preform design of H-shaped hot die forging based on shape complexity factor, Int. J. Mater. Form. 11 (2018) 233–238 [Google Scholar]
  28. B. Tomov, A new shape complexity factor, J. Mater. Process. Technol. 92 (1999) 439–443 [Google Scholar]
  29. B. Tomov, R. Radev, An example of determination of preforming stages in hot die forging, J. Mater. Process. Technol. 157 (2004) 617–619 [Google Scholar]
  30. B. Tomov, R. Radev, Shape complexity factor for closed die forging, Int. J. Mater. Form. 3 (2010) 319–322 [Google Scholar]
  31. M.Y. Cao, C.L. Hu, B.X. Cai et al., Complexity index of axisymmetric parts in the forging process based on variation in geometric changes, Int. J. Adv. Manuf. Technol. 135 (2024) 203–217 [Google Scholar]
  32. B.B. Mandelbrot, D.E. Passoja, A.J. Paulley, Fractal character of fracture surfaces of metals, Nature 308 (1984) 721–724 [NASA ADS] [CrossRef] [Google Scholar]
  33. N. Sarkar, B.B. Chaudhuri, An efficient differential box-counting approach to compute fractal dimension of image, IEEE Trans. Syst. Man. Cybern. 24 (1994) 115–120 [Google Scholar]
  34. U. Freiberg, S. Kohl, Box dimension of fractal attractors and their numerical computation, Commun. Nonlinear. Sci. 95 (2021) 105615 [Google Scholar]
  35. W.H. Zhuang, X.H. Han, L. Hua et al., FE prediction method for tooth variation in hot forging of spur bevel gears, J. Manuf. Process. 38 (2019) 244–255 [Google Scholar]
  36. M.C. Chen, C.D. Zhu, Z.Q. Yu et al., A novel process for manufacturing large-diameter thin-walled metal ring: double-roll pendulum hot rotary forging technology, J. Manuf. Process. 76 (2022) 379–396 [Google Scholar]
  37. K. Reza Kashyzadeh, Effects of axial and multiaxial variable amplitude loading conditions on the fatigue life assessment of automotive steering knuckle, J. Fail. Anal. Prev. 20 (2020) 455–463 [Google Scholar]
  38. C.L. Hu, F. Zeng, Z. Zhao et al., Process optimization for design of duplex universal joint fork using unequal thickness flash, Int. J. Precis. Eng. Manuf. 16 (2015) 2517–2527 [Google Scholar]
  39. F.S. Silva, Analysis of a vehicle crankshaft failure, Eng. Fail. Anal. 10 (2003) 605–616 [Google Scholar]
  40. M. Churl Song, C.J. VanTyne, J. Rae Cho et al., Optimization of preform design in Tadeusz Rut forging of heavy crankshafts, ASME J. Manuf. Sci. Eng. 139 (2017) 091014 [Google Scholar]
  41. M. Meyer, M. Stonis, B.A. Behrens, Cross wedge rolling of preforms for crankshafts, Key. Eng. Mater. 504 (2012) 205–210 [Google Scholar]
  42. X. Wang, Z. Qi, K. Chen et al., Study on the forming accuracy of the three-cylinder crankshaft using a specific die with a preformed dressing, Int. J. Adv. Manuf. Technol. 104 (2019) 551–564 [Google Scholar]
  43. B. Jiang, W. Fang, R.M. Chen et al., Mechanical properties and microstructural characterization of medium carbon non-quenched and tempered steel: microalloying behavior, Mat. Sci. Eng. A-Struct. 748 (2019) 180–188 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.