Reverse identification of constitutive parameters of Inconel 718 alloy based on analytical model and thermo-mechanical loads analysis of machined surface

Tian, Pengfei; Lin, He; Zhou, Tao; Du, Feilong; Zou, Zichuan; Zhou, Xiaorong; Jiang, Hongwan

doi:10.1016/j.jmrt.2021.11.164

Cited by 12 publications

(2 citation statements)

References 37 publications

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“…ε, and T [23]. The prevalent analytical model used to reproduce the elastoplastic behaviour of INCONEL ® alloys is the JC constitutive model [14][15][16][17][18]. The JC model has some derivations: (1) the Baumann-Chiesa-Johnson (BCJ) model [24], which incorporates .…”

Section: Model Equationmentioning

confidence: 99%

“…The experimental results validated the numerical model, and the CEL model was able to cope with reality regarding the T distribution, F c, and residual stress profiles. Figure 22 Tian et al [18] conducted a reverse identification of JC parameters for INCONEL ® 718, considering specimens in both the solution-annealed and precipitation-hardened states. Experimental orthogonal cutting was employed to obtain the average F c and h ch , validating the FEA model produced in DEFORM ® 2D v12.0.…”

Section: Turningmentioning

confidence: 99%

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INCONEL® Alloy Machining and Tool Wear Finite Element Analysis Assessment: An Extended Review

Pedroso,

Sebbe,

Costa

et al. 2024

JMMP

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Machining INCONEL® presents significant challenges in predicting its behaviour, and a comprehensive experimental assessment of its machinability is costly and unsustainable. Design of Experiments (DOE) can be conducted non-destructively through Finite Element Analysis (FEA). However, it is crucial to ascertain whether numerical and constitutive models can accurately predict INCONEL® machining. Therefore, a comprehensive review of FEA machining strategies is presented to systematically summarise and analyse the advancements in INCONEL® milling, turning, and drilling simulations through FEA from 2013 to 2023. Additionally, non-conventional manufacturing simulations are addressed. This review highlights the most recent modelling digital solutions, prospects, and limitations that researchers have proposed when tackling INCONEL® FEA machining. The genesis of this paper is owed to articles and books from diverse sources. Conducting simulations of INCONEL® machining through FEA can significantly enhance experimental analyses with the proper choice of damage and failure criteria. This approach not only enables a more precise calibration of parameters but also improves temperature (T) prediction during the machining process, accurate Tool Wear (TW) quantity and typology forecasts, and accurate surface quality assessment by evaluating Surface Roughness (SR) and the surface stress state. Additionally, it aids in making informed choices regarding the potential use of tool coatings.

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