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Home All issues Volume 8 (2021) Manufacturing Rev., 8 (2021) 7 References
Open Access
Issue
Manufacturing Rev.
Volume 8, 2021
Article Number 7
Number of page(s) 10
DOI https://doi.org/10.1051/mfreview/2021005
Published online 02 March 2021
  1. J. Cao, E. Brinksmeier, M.W. Fu, R. Gao, B. Liang, M. Merklein, M. Schmidt, J. Yanagimoto, Manufacturing of advanced smart tooling for metal forming, CIRP Ann. Manuf. Technol. 68 (2019) 605–628 [Google Scholar]
  2. Y. Qin, Micromanufacturing Engineering and Technology, 2nd edition (Elsevier, Oxford, 2015) [Google Scholar]
  3. F. Vollertsen, D. Biermann, H.N. Hansen, I.S. Jawahir, K. Kuzman, Size effects in manufacturing of metallic components, CIRP Ann. Manuf. Technol. 58 (2009) 566–587 [Google Scholar]
  4. M.W. Fu, J.L. Wang, A.M. Korsunsky, A review of geometrical and microstructural size effects in micro-scale deformation processing of metallic alloy components, Int. J. Mach. Tool Manu. 109 (2016) 94–125 [Google Scholar]
  5. S.B. Chikalthankar, G.D. Belurkar, V.M. Nandedkar, Factors affecting on springback in sheet metal bending: a review, Int. J. Eng. Adv. Technol. 3 (2014) 247–251 [Google Scholar]
  6. H.T. Le, D.T. Vu, P.T. Doan, K.T. Le, Effect of springback in DP980 advanced high strength steel on product precision in bending process, Acta Metal. Slovaca 25 (2019) 150–157 [Google Scholar]
  7. J. Wang, M.W. Fu, J. Ran, Analysis of the size effect on springback behavior in micro‐scaled U‐bending process of sheet metals, Adv. Eng. Mater. 16 (2014) 421–432 [CrossRef] [Google Scholar]
  8. Z. Xu, L. Peng, E. Bao, Size effect affected springback in micro/meso scale bending process: experiments and numerical modeling, J. Mater. Process. Tech. 252 (2018) 407–420 [Google Scholar]
  9. I.A. Choudhury, V. Ghomi, Springback reduction of aluminum sheet in V-bending dies, P. I. Mech. Eng. B-J. Eng. 228 (2014) 917–926 [Google Scholar]
  10. J.T. Gau, C. Principe, M. Yu, Springback behavior of brass in micro sheet forming, J. Mater. Process. Tech. 191 (2007) 7–10 [Google Scholar]
  11. D. Pritima, P. Padmanabhan, Investigation of sheet bending parameters on springback in nickel coated mild steel sheets using response surface methodology, Mech. Ind. 19 (2018) 206 [CrossRef] [Google Scholar]
  12. S.K. Panthi, M.S. Hora, M. Ahmed, Artificial neural network and experimental study of effect of velocity on springback in straight flanging process, Indian. J. Eng. Mater. Sci. 23 (2016) 159–164 [Google Scholar]
  13. A.A. Shah, T.Q. Bui, A.V. Tran. Improved knowledge-based neural network (KBNN) model for predicting spring-back angles in metal sheet bending, Int. J. Model. Simul. Sci. Comput. 5 (2014) 1350026 [Google Scholar]
  14. W. Liu, Q. Liu, F. Ruan, Z. Liang, H. Qiu, Springback prediction for sheet metal forming based on GA-ANN technology, J. Mater. Process. Tech. 187 (2007) 227–231 [Google Scholar]
  15. R. Teimouri, H. Baseri, B. Rahmani, M. Bakhshi-Jooybari, Modeling and optimization of spring-back in bending process using multiple regression analysis and neural computation, Int. J. Mater. Form. 7 (2014) 167–178 [Google Scholar]
  16. M.A. Dib, N.J. Oliveira, A.E. Marques, M.C. Oliveira, J.V. Fernandes, B.M. Ribeiro, P.A. Prates, Single and ensemble classifiers for defect prediction in sheet metal forming under variability, Neural Comput. Appl. 32 (2020) 12335–12349 [Google Scholar]
  17. M.E. Khamneh, M. Askari-Paykani, H. Shahverdi, Optimization of spring-back in creep age forming process of 7075 Al-Alclad alloy using D-optimal design of experiment method, Measurement 88 (2016) 278–286 [Google Scholar]
  18. D.C. Montgomery, L. Custer, D.R. Mccarville, Design and Analysis of Experiments Student Solutions Manual, 8th edition (John Wiley & Sons, New York, 2012) [Google Scholar]
  19. İ. Asiltürk, S. Neşeli, Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis, Measurement 45 (2012) 785–794 [CrossRef] [Google Scholar]
  20. M.A. Bezerra, R.E. Santelli, E.P. Oliveira, L.S. Villar, L.A. Escaleira, Response surface methodology (RSM) as a tool for optimization in analytical chemistry, Talanta 76 (2008) 965–977 [CrossRef] [PubMed] [Google Scholar]
  21. R.H. Myers, D.C. Montgomery, C. Anderson-Cook, Response Surface Methodology: Product and Process Optimization Using Designed Experiments (John Wiley & Sons, New York, 2009) [Google Scholar]
  22. X. Liu, S. Zhao, Y. Qin, J. Zhao, W.A. Wan-Nawang, A parametric study on the bending accuracy in micro W-bending using Taguchi method, Measurement 100 (2017) 233–242 [Google Scholar]
  23. X. Liu, S. Zhao, Y. Qin, C. Wang, W.A. Wan-Nawang, Size effects on the springback of CuZn37 brass foils in tension and micro W-bending, P. I. Mech. Eng. B-J. Eng. 232 (2018) 2439–2448 [Google Scholar]
  24. O.A. Mohamed, S.H. Masood, J.L. Bhowmik, Optimization of fused deposition modeling process parameters for dimensional accuracy using I-optimality criterion, Measurement 81 (2016) 174–196 [Google Scholar]
  25. A. Ozdemir, An I-optimal experimental design-embedded nonlinear lexicographic goal programming model for optimization of controllable design factors, Optim. Eng. 3 (2020) 1–16 [Google Scholar]
  26. P. Goos, B. Jones, U. Syafitri, I-optimal design of mixture experiments, J. Am. Stat. Assoc. 111 (2016) 899–911 [Google Scholar]

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