2022
DOI: 10.1016/j.simpat.2022.102656
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Multiphysics numerical modelling of backward flow forming process of AISI 5140 steel

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Cited by 8 publications
(1 citation statement)
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“…The simulation or methods represented by SVM model [17] , neural network model [18] and thermodynamic model [19] can achieve efficient acquisition of microstructure and properties under different components, and provide an early basis for reducing the screening scope for test verification. For instance, the JMatPro program can not only directly obtain the hot-working microstructure phase diagram, CCT and TTT diagram [20] of different alloys, but also indirectly serve as the initial parameters [21] or comparison verification items of other numerical calculation programs [22] . Besides, it can also be used to optimize the alloy processing technology including heat treatment [23] .…”
Section: Introductionmentioning
confidence: 99%
“…The simulation or methods represented by SVM model [17] , neural network model [18] and thermodynamic model [19] can achieve efficient acquisition of microstructure and properties under different components, and provide an early basis for reducing the screening scope for test verification. For instance, the JMatPro program can not only directly obtain the hot-working microstructure phase diagram, CCT and TTT diagram [20] of different alloys, but also indirectly serve as the initial parameters [21] or comparison verification items of other numerical calculation programs [22] . Besides, it can also be used to optimize the alloy processing technology including heat treatment [23] .…”
Section: Introductionmentioning
confidence: 99%