Finite Element Modeling of Three-Dimensional Milling Process of Ti–6Al–4V

Bajpai, Vivek K.; Lee, Ineon; Park, Hyung Wook

doi:10.1080/10426914.2014.892618

Cited by 29 publications

(15 citation statements)

References 12 publications

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“…The J-C flow stress is calculated considering the coupled effects of the material strain hardening (Ludwick hardening term), the strain-rate dependency and the material thermal softening (equation ( 1)). The J-C model was adopted by authors 36,47,48,50,53,55,56 to simulate Ti6Al4V machining. To obtain the model parameters, mechanical tests, such as Hopkins pressure bar testing are required to empirically fit the stress-strain curves into the model.…”

Section: Phenomenological Constitutive Models For Ti6al4v Machining Modelingmentioning

confidence: 99%

Intelligent machining methods for Ti6Al4V: A review

Carvalho

Pereira

Horovistiz

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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Digital manufacturing is a necessity to establishing a roadmap for the future manufacturing systems projected for the fourth industrial revolution. Intelligent features such as behavior prediction, decision-making abilities, and failure detection can be integrated into machining systems with computational methods and intelligent algorithms. This review reports on techniques for Ti6Al4V machining process modeling, among them numerical modeling with finite element method (FEM) and artificial intelligence-based models using artificial neural networks (ANN) and fuzzy logic (FL). These methods are intrinsically intelligent due to their ability to predict machining response variables. In the context of this review, digital image processing (DIP) emerges as a technique to analyze and quantify the machining response (digitization) in the real machining process, often used to validate and (or) introduce data in the modeling techniques enumerated above. The widespread use of these techniques in the future will be crucial for the development of the forthcoming machining systems as they provide data about the machining process, allow its interpretation and quantification in terms of useful information for process modelling and optimization, which will create machining systems less dependent on direct human intervention.

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Section: Phenomenological Constitutive Models For Ti6al4v Machining Modelingmentioning

confidence: 99%

Intelligent machining methods for Ti6Al4V: A review

Carvalho

Pereira

Horovistiz

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…Thermal softening nature dominated at higher cutting speed moreover reduced the thrust force and torque. Bajpai et al [6] conducted threedimensional milling process on Ti-6Al-4V and observed the primary chip formation mechanisms along with the vonmises stress distribution. The maximum stress was 1518 MPa found near to rubbing area of tool rake face and the curl type of chip formation was formed during simulation.…”

Section: Introductionmentioning

confidence: 99%

Impact of cutting parameters on machining of Ti-6Al-4V alloy: an experimental and FEM approach

Gobivel¹,

K.S.Vijaysekar²,

Prabhakaran³

2021

Int. J. Simul. Multidisci. Des. Optim.

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Titanium alloys are used as an aerospace material due to their inherent properties such as high strength to weight ratio, corrosion, and fracture resistance. However, the low conductivity and reactivity towards plastic deformation causes these materials to be difficult to cut category. The prediction of various parameters like chip formation and actual cutting forces are important factors for better machinability which involves lot of resources. To overcome such issues, this work proposes three-dimensional FE approach to simulate the machinability behavior of Ti-6Al-4V especially on conventional turning. The impact of cutting speed and feed rate on the cutting force, thrust force, feed force and surface roughness were analyzed experimentally for various conditions. The predicted machining forces showed strong correlation with the experimental results and the effective von mises stress were examined.

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“…Although 2D FE simulations offer some distinct advantages when studying the orthogonal cutting process, 3D FE models provide more realistic oblique configurations, mainly in milling processes with complex cutting tool geometries [ 16 , 17 ]. 3D FE simulations present supplementary analysis capabilities to investigate the effect of the helix angle and tool edge radius on chip flow and burr formation, which are almost impossible to be considered by 2D FE models [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…In order to better understand the cutting mechanism and the effects of run-out in micro milling, 3D simulations are required, but a very limited number of studies in the literature deal with micro milling 3D simulations. However, at a macro scale, a few examples are available [ 16 , 17 , 21 , 24 ] that can be used as a starting point to develop a 3D micro milling model. In the literature, there is still a significant gap in our understanding of the effect of run-out in micro milling with 3D integrated finite element modeling techniques validated with accurate experimental campaigns.…”

Section: Introductionmentioning

confidence: 99%

3D Finite Element Simulation of Micro End-Milling by Considering the Effect of Tool Run-Out

et al. 2017

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Understanding the micro milling phenomena involved in the process is critical and difficult through physical experiments. This study presents a 3D finite element modeling (3D FEM) approach for the micro end-milling process on Al6082-T6. The proposed model employs a Lagrangian explicit finite element formulation to perform coupled thermo-mechanical transient analyses. FE simulations were performed at different cutting conditions to obtain realistic numerical predictions of chip formation, temperature distribution, and cutting forces by considering the effect of tool run-out in the model. The radial run-out is a significant issue in micro milling processes and influences the cutting stability due to chip load and force variations. The Johnson–Cook (JC) material constitutive model was applied and its constants were determined by an inverse method based on the experimental cutting forces acquired during the micro end-milling tests. The FE model prediction capability was validated by comparing the numerical model results with experimental tests. The maximum tool temperature was predicted in a different angular position of the cutter which is difficult or impossible to obtain in experiments. The predicted results of the model, involving the run-out influence, showed a good correlation with experimental chip formation and the signal shape of cutting forces.

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Finite Element Modeling of Three-Dimensional Milling Process of Ti–6Al–4V

Cited by 29 publications

References 12 publications

Intelligent machining methods for Ti6Al4V: A review

Intelligent machining methods for Ti6Al4V: A review

Impact of cutting parameters on machining of Ti-6Al-4V alloy: an experimental and FEM approach

3D Finite Element Simulation of Micro End-Milling by Considering the Effect of Tool Run-Out

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