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

Author:

Pedroso André F. V.1ORCID,Sebbe Naiara P. V.12,Costa Rúben D. F. S.23,Barbosa Marta L. S.2ORCID,Sales-Contini Rita C. M.14ORCID,Silva Francisco J. G.13ORCID,Campilho Raul D. S. G.13ORCID,de Jesus Abílio M. P.23ORCID

Affiliation:

1. CIDEM, ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, Portugal

2. Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal

3. LAETA-INEGI, Associate Laboratory for Energy, Transports and Aerospace, Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal

4. Technological College of São José dos Campos, Centro Paula Souza, Avenida Cesare Mansueto Giulio Lattes, 1350 Distrito Eugênio de Melo, São José dos Campos 12247-014, Brazil

Abstract

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.

Funder

European Structural and Investments Funds

Publisher

MDPI AG

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