Metamodels to describe the high temperature deformation behaviour of Al 2014+2wt%TiB₂ composite

References

• Balasundar I, Raghu T, Kashyap BP. Modeling the hot working behavior of near-α titanium alloy IMI 834. Prog. Nat. Sci. Mater. Int. 2013;23(6):598–607.
   Web of Science ®Google Scholar
• Lin YC, Wen DX, Deng J, et al. Constitutive models for high-temperature flow behaviors of a Ni-based superalloy. Mater Des. 2014;59:115–123.
   Web of Science ®Google Scholar
• Cepeda-Jiménez CM, Ruano OA, Carsí M, et al. Study of hot deformation of an Al-Cu-Mg alloy using processing maps and microstructural characterization. Mater Sci Eng A. 2012;552:530–539.
   Web of Science ®Google Scholar
• Gangolu S, Gourav Rao A, Sabirov I, et al. Development of constitutive relationship and processing map for Al-6.65Si-0.44Mg alloy and its composite with B4C particulates. Mater Sci Eng A. 2016;655:256–264.
   Web of Science ®Google Scholar
• Kanthi L, Kurra S, Singh SK. Flow stress prediction of zircaloy-4 at elevated temperatures using KHL constitutive model. Adv Mater Process Technol. 2020;6(2):362–371.
   Google Scholar
• Lin YC, Chen XM. A critical review of experimental results and constitutive descriptions for metals and alloys in hot working. Mater Des. 2011;32(4):1733–1759.
   Web of Science ®Google Scholar
• Wang Z, Qi L, Wang G, et al. Constitutive equation for the hot deformation behavior of Csf/AZ91D composites and its validity for numerical simulation. Mech Mater. 2016;102:90–96.
   Web of Science ®Google Scholar
• Saxena KK, Pancholi V. Zr–Nb Alloys and Its Hot Deformation Analysis Approaches. Met Mater Int. 2020;(April). DOI:10.1007/s12540-020-00812-8.
   Google Scholar
• Lin YC, Chen MS, Zhong J. Constitutive modeling for elevated temperature flow behavior of 42CrMo steel. Comput Mater Sci. 2008;42(3):470–477.
   Web of Science ®Google Scholar
• Venkata Ramana A, Balasundar I, Davidson MJ, et al. Constitutive Modelling of a New High-Strength Low-Alloy Steel Using Modified Zerilli–Armstrong and Arrhenius Model. Trans. Indian Inst. Met. 2019;72(10):2869–2876.
   Web of Science ®Google Scholar
• Balasundar I, Ravi KR, Raghu T. On the high temperature deformation behaviour of titanium alloy BT3-1. Mater Sci Eng A. 2017;684:135–145.
   Web of Science ®Google Scholar
• Momeni A, Ebrahimi GR, Jahazi M, et al. Microstructure evolution at the onset of discontinuous dynamic recrystallization: a physics-based model of subgrain critical size. J Alloys Compd. 2014;587:199–210.
   Web of Science ®Google Scholar
• Zhang H, Chen G, Chen Q, et al. A physically-based constitutive modelling of a high strength aluminum alloy at hot working conditions. J Alloys Compd. 2018;743(June):283–293.
   Google Scholar
• Jalham IS. Modeling capability of the artificial neural network (ANN) to predict the effect of the hot deformation parameters on the strength of Al-base metal matrix composites. Computational Science and Technology. 2003;63:63–67.
   Web of Science ®Google Scholar
• Senthilvelan LST. Hot Deformation Flow Behaviour Modelling of Cast-Extruded Al 7075 Alloy Using Arrhenius Equations and Artificial Neural Network. Trans. Indian Inst. Met. 2016; DOI:10.1007/s12666-016-0849-0
   Web of Science ®Google Scholar
• Sha W, Edwards KL. The use of artificial neural networks in materials science based research. Mater Des. 2007;28(6):1747–1752.
   Web of Science ®Google Scholar
• Shen Z, Wu R, Yuan C, et al. Comparative study of metamodeling methods for modeling the constitutive relationships of the TC6 titanium alloy. J Mater Res Technol. 2021;10:188–204.
   Google Scholar
• Xiao G, Yang Q, and Li L, et al. Modeling Material Flow Behavior during Hot Deformation Based on Metamodeling Methods. Mathematical Problems in Engineering. 2015; doi:10.1155/2015/157892
   Web of Science ®Google Scholar
• Tjong SC, and Ma ZY. Microstructural and mechanical characteristics of in situ metal matrix composites. Materials Science and Engineering : R: Reports. 2000;29(July):49–113.
   Google Scholar
• Kumar V, Shridhar VM, Jayakrishnan S. Synthesis, Characterization and Mechanical Properties of AA7075 Based MMCs Reinforced with TiB 2 Particles Processed Through Ultrasound Assisted In-Situ Casting Technique. Trans. Indian Inst. Met. 2017; DOI:10.1007/s12666-017-1216-5
   Google Scholar
• Myers RH, Montgomery DC, and Christine MA-C. Response Surface Methodology: process and Product Optimization Using Designed Experiments. New Jersey: John Wiley & Sons; 2016.
   Google Scholar
• Raghavendra S, Sathyanarayana PS, S SK, et al. High speed machining of titanium Ti 6Al4V alloy components: study and optimization of cutting parameters using RSM. Adv Mater Process Technol. 2020:1–14.
   Google Scholar
• Rawlings, John O., Pantula, Sastry G., and Dickey, David A. Applied Regression Analysis: A Research Tool II. (New York: Springer-Verlag): 1999.
   Google Scholar
• Mahender T, Mra P, Balasundar I, et al. On the optimization of temperature and cooling rate to maximize strength and ductility of near α titanium alloy IMI 834. Mater Sci Eng A. 2021;827(September):142052.
   Google Scholar
• Deb K. Optimization for engineering design:Algorithms and examples. New Delhi: Prentice Hall of India; 2005.
   Google Scholar
• Xiao YH, Guo C. Constitutive modelling for high temperature behavior of 1Cr12Ni3Mo2VNbN martensitic steel. Mater Sci Eng A. 2011;528(15):5081–5087.
   Web of Science ®Google Scholar
• Mahender T, Venkatesh B. Optimization of flow forming process parameters of al - 8014 using genetic algorithm. Int. J. Eng. Technol. 2018;7(2):868–873.
   Google Scholar
• Madadi RR, Balaji C. Optimization of the location of multiple discrete heat sources in a ventilated cavity using artificial neural networks and micro genetic algorithm. Int J Heat Mass Transfer. 2008;51(9–10):2299–2312.
   Web of Science ®Google Scholar
• Balasundar I, Raghu T, and Kashyap BP. Correlation between microstructural features and creep strain in a near alpha titanium alloy processed in the alpha + beta regime. Materials Science an Engineering A. 2016;609():241–249 doi:10.1016/j.msea.2014.04.079.
   Web of Science ®Google Scholar
• Karthikeyan R, Senthil Kumar V, Punitha A, et al. An Integrated ANN–GA Approach to Maximize the Material Removal Rate and Surface Roughness of Wire Cut EDM on Titanium Alloy. Adv Mater Process Technol. 2020:1–11.
   Google Scholar
• Kumar S, Karmakar A, Nath SK. Construction of hot deformation processing maps for 9Cr-1Mo steel through conventional and ANN approach. Mater Today Commun. 2021;26:101903. no. September 2020.
   Web of Science ®Google Scholar
• Balasundar I, Raghu T and Kashyap B P. (2016). Taguchi Based Optimisation of Artificial Neural Network to Establish a Direct Microstructure: Mechanical Property Correlation in a near-α Titanium Alloy. Trans Indian Inst Met, 69(10), 1929–1941. 10.1007/s12666-016-0852-5
   Web of Science ®Google Scholar
• Lawal AI, Aladejare AE, Onifade M, et al. Predictions of elemental composition of coal and biomass from their proximate analyses using ANFIS, ANN and MLR. Int. J. Coal Sci. Technol. 2020; 1–3. DOI:10.1007/s40789-020-00346-9.
   Google Scholar
• Prasad Y. Hot Working Guide. Ohio: Second. ASM International 2015 .
   Google Scholar
• Wahed MA, Gupta AK, Gadi VSR, et al. Parameter optimization in V-bending process at elevated temperatures to minimize spring back in Ti-6Al-4V alloy. Adv Mater Process Technol. 2020;6(2):430–444.
   Google Scholar
• Mahender T, Balasundar I, Raghu T. A parametric study on the design factors influencing the thermal performance of nickel alloy C263 sandwich panels. Aust. J. Mech. Eng. 2021:1–15.
   Web of Science ®Google Scholar
• Prasad KS, Panda SK, Kar SK, et al. Constitutive modelling and formability prediction of Inconel-718 sheet at elevated temperatures for space applications. Adv Mater Process Technol. 2020:1–24.
   Google Scholar