Orthogonal cutting simulation of EN AW 6082 T6 alloy using a coupled Eulerian-Lagrangian approach

Dumanić, Ivana; Jozić, Sonja; Bagavac, Petra; Bajić, Dražen

doi:10.1016/j.heliyon.2023.e14821

Cited by 4 publications

(1 citation statement)

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“…ε 0 p is the reference strain rate; T 0 is the ambient temperature; and T m is the melting temperature. The yield stress (A), strain-hardening constant (B), modulus of strain rate hardening (C), strain-hardening coefficient (n), and thermal softening coefficient (m) [29,30] are material constants and exponents derived by fitting the data obtained from the tensile test, Split Hopkinson Pressure Bar (SHPB) test [30,31] (not following any agreed-upon standards regarding the operation or design of the apparatus), and dilatometer test under various T and . ε.…”

Section: Model Equationmentioning

confidence: 99%

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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