Multifield finite strain plasticity: Theory and numerics

Lewandowski, Karol; Barbera, Daniele; Blackwell, Paul; Roohi, Amir H.; Athanasiadis, Ignatios; McBride, Andrew; Steinmann, Paul; Pearce, CJ; Kaczmarczyk, Łukasz

doi:10.1016/j.cma.2023.116101

Cited by 3 publications

(1 citation statement)

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“…While the disparity in errors between the Eulerian and LAG approaches varies based on the specific problem, errors within the Eulerian framework generally tend to be more significant than those within the LAG framework [110]. The ALE method [111,112] finds extensive use among researchers, as it aids in overcoming certain limitations inherent in the LAG method [108]. Integrating the Eulerian and LAG approaches offers a viable solution to address the distortion of elements and effectively model the extensive plastic deformation characteristic of machining [100].…”

Section: Assembly Numerical Modellingmentioning

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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