Experimental-numerical analysis of ductile damage modeling of aluminum alloy using a hybrid approach: ductile fracture criteria and adaptive neural-fuzzy system (ANFIS)

References

• Kulkarni SS, Gupta V, Ortiz A, et al. Determining cohesive parameters for modeling interfacial fracture in dissimilar-metal friction stir welded joints. Int J Solids Struct. 2021;216:200–210.
   Web of Science ®Google Scholar
• Stoughton TB, Yoon JW. A new approach for failure criterion for sheet metals. Int J Plast. 2011;27(3):440–459.
   Web of Science ®Google Scholar
• Maleki A, Ahmadi A, Talebi-Ghadikolaei H. Numerical investigation of bending angle and entropy generation in laser forming of high strength steel. J Fluid Mech. 2019;9:151–166.
   Google Scholar
• Zhan M, Gu C, Jiang Z, et al. Application of ductile fracture criteria in spin-forming and tube-bending processes. Comput Mater Sci. 2009;47:353–365.
   Web of Science ®Google Scholar
• Linardon C, Favier D, Chagnon G, et al. A conical mandrel tube drawing test designed to assess failure criteria. J Mater Process Technol. 2014;214:347–357.
   Web of Science ®Google Scholar
• Takuda H, Mori K, Hatta N. The application of some criteria for ductile fracture to the prediction of the forming limit of sheet metals. J Mater Process Technol. 1999;95:116–121.
   Web of Science ®Google Scholar
• Song X, Leotoing L, Guines D, et al. Investigation of the forming limit strains at fracture of AA5086 sheets using an in-plane biaxial tensile test. Eng Fract Mech. 2016;163:130–140.
   Web of Science ®Google Scholar
• Mirnia MJ, Shamsari M. Numerical prediction of failure in single point incremental forming using a phenomenological ductile fracture criterion. J Mater Process Technol. 2017;244:17–43.
   Web of Science ®Google Scholar
• Mirnia MJ, Vahdani M. Calibration of ductile fracture criterion from shear to equibiaxial tension using hydraulic bulge test. J Mater Process Technol. 2020;280:116589.
   Web of Science ®Google Scholar
• Zahedi A, Dariani BM, Mirnia MJ. Experimental determination and numerical prediction of necking and fracture forming limit curves of laminated Al/Cu sheets using a damage plasticity model. Int J Mech Sci. 2019;153:341–358.
   Web of Science ®Google Scholar
• Talebi-Ghadikolaee H, Naeini HM, Mirnia MJ, et al. Fracture analysis on U-bending of AA6061 aluminum alloy sheet using phenomenological ductile fracture criteria. Thin Walled Struct. 2020;148:106566.
   Web of Science ®Google Scholar
• Talebi-Ghadikolaee H, Naeini HM, Mirnia MJ, et al. Experimental and numerical investigation of failure during bending of AA6061 aluminum alloy sheet using the modified Mohr-Coulomb fracture criterion. Int J Adv Manuf Technol. 2019;105:5217–5237.
   Web of Science ®Google Scholar
• Zhan X, Hu Q, Wang Z, et al. The numerical method for predicting failure in single point incremental forming using a new anisotropic ductile fracture model. Procedia Manuf. 2019;29:45–52.
   Google Scholar
• Talebi-Ghadikolaee H, Naeini HM, Mirnia MJ, et al. Ductile fracture prediction of AA6061-T6 in roll forming process. Mech Mater. 2020;148:103498.
   Web of Science ®Google Scholar
• Talebi-Ghadikolaee H, Naeini HM, Mirnia MJ, et al. Modeling of ductile damage evolution in roll forming of U-channel sections. J Mater Process Technol. 2020;283:116690.
   Web of Science ®Google Scholar
• Othmen KB, Haddar N, Jegat A, et al. Ductile fracture of AISI 304L stainless steel sheet in stretching. Int J Mech Sci. 2020;172:105404.
   Web of Science ®Google Scholar
• Modanloo V, Talebi-Ghadikolaee H, Alimirzaloo V, et al. Fracture prediction in the stamping of titanium bipolar plate for PEM fuel cells. Int J Hydrogen Energy. 2021;46:5729–5739.
   Web of Science ®Google Scholar
• Deole AD, Barnett MR, Weiss M. The numerical prediction of ductile fracture of martensitic steel in roll forming. Int J Solids Struct. 2018;144:20–31.
   Web of Science ®Google Scholar
• Talebi-Ghadikolaee H, Elyasi M, Mirnia MJ. Investigation of failure during rubber pad forming of metallic bipolar plates. Thin Walled Struct. 2020;150:106671.
   Web of Science ®Google Scholar
• Zurada JM. Introduction to artificial neural systems. St. Paul, Minnesota: West Publishing Company; 1992.
   Google Scholar
• Nauck D, Klawonn F, Kruse R. Foundations of neuro-fuzzy systems. John Wiley & Sons, Inc; 1997.
   Google Scholar
• Jang J-S. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern. 1993;23:665–685.
   Web of Science ®Google Scholar
• Maher I, Eltaib M, Sarhan AA, et al. Investigation of the effect of machining parameters on the surface quality of machined brass (60/40) in CNC end milling—ANFIS modeling. Int J Adv Manuf Technol. 2014;74:531–537.
   Web of Science ®Google Scholar
• Sherkatghanad E, Moslemi-Naeini H, Rabiee AH, et al. Modeling and predicting the important properties of the PVC/glass fiber composite laminates in the production process by the TLBO-ANFIS approach. Iran J Mater Forming. 2021;8:63–75.
   Google Scholar
• Yaghoobi A, Bakhshi-Jooybari M, Gorji A, et al. Application of adaptive neuro fuzzy inference system and genetic algorithm for pressure path optimization in sheet hydroforming process. Int J Adv Manuf Technol. 2016;86:2667–2677.
   Web of Science ®Google Scholar
• Asl YD, Woo YY, Kim Y, et al. Non-sorting multi-objective optimization of flexible roll forming using artificial neural networks. Int J Adv Manuf Technol. 2020;107:2875–2888.
   Web of Science ®Google Scholar
• Safari M, Joudaki J. Prediction of bending angle for laser forming of tailor machined blanks by neural network. Iran J Mater Forming. 2018;5:47–57.
   Google Scholar
• Lou Y, Huh H. Extension of a shear-controlled ductile fracture model considering the stress triaxiality and the Lode parameter. Int J Solids Struct. 2013;50:447–455.
   Web of Science ®Google Scholar
• Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Software. 2014;69:46–61.
   Web of Science ®Google Scholar