Investigating the influence of friction conditions on finite element simulation of microscale machining with the presence of built-up edge

Oliaei, Samad Nadimi Bavil

doi:10.1007/s00170-016-9456-9

Cited by 13 publications

(8 citation statements)

References 37 publications

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“…Furthermore, F f was the same in the Vc range from 90 to 120 m/min. A similar thermal softening of a workpiece was previously observed by Samad Nadimi Bavil Oliaei [ 37 ], where they found that the presence of a built-up edge affects the thrust forces more than the cutting forces.…”

Section: Resultssupporting

confidence: 76%

Machinability of INCONEL718 Alloy with a Porous Microstructure Produced by Laser Melting Powder Bed Fusion at Higher Energy Densities

Wood

Díaz-Álvarez

et al. 2020

Materials

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Products produced by additive manufacturing (AM) seek to exploit net shape manufacturing by eliminating or minimizing post-process stages such as machining. However, many applications which include turbo machinery components with tight dimensional tolerances and a smooth surface finish will require at least a light machine finishing stage. This paper investigates the machinability of the additively fabricated INCONEL718 (IN718) alloy produced by laser melting powder bed fusion (LM-PBF) with different levels of spherical porosity in the microstructure. The literature suggests that the band width for laser energy density, which combines the various scan process parameters to obtain a low spherical type porosity in the LM-PBF IN718 alloy (~1%), has wide breadth. With the increasing laser energy density and above a threshold, there is a rapid increase in the spherical pore size. In this paper, three tube samples each with different levels of spherical porosity were fabricated by varying the laser energy density for LM-PBF of the IN718 alloy within the stable and higher energy density range and the porosity measured. A low laser energy density was avoided due to balling up, which promotes highly irregular lack of fusion defects and poor consolidation within the alloy microstructure. An orthogonal turning test instrumented, with a three-component dynamometer to measure the cutting forces, was performed on AM produced IN718 tube samples under light cut conditions to simulate a finish machining process. The orthogonal turning tests were also performed on a tube sample obtained from the wrought extruded stock. The machining process parameters, which were studied include varying the cutting speed at three levels, at a fixed feed and under dry cut conditions for a short duration to avoid the tool wear. The results obtained were discussed and a notable finding was the higher rate of built-up-edge formation on the tool tip from the AM samples with a higher porosity and especially at a higher cutting speed. The paper also discusses the mechanisms that underpin the findings.

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Section: Resultssupporting

confidence: 76%

Machinability of INCONEL718 Alloy with a Porous Microstructure Produced by Laser Melting Powder Bed Fusion at Higher Energy Densities

Wood

Díaz-Álvarez

et al. 2020

Materials

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“…It is worth mentioning that different heat transfer coefficients are used in the literature to simulate orthogonal cutting processes. For instance, Calamaz et al (2008) used a value of 20,000 (W/m 2 °C), Karpat (2010) used a value of 10,000 (W/m 2 °C), Sima and Özel (2010) have used 1000 (kW/m 2 K), while Oliaei and Karpat (2017 b ) have used 5000 (W/m 2 °C). This is mainly because the simulation time for the machining is very short.…”

Section: Methodsmentioning

confidence: 99%

Prediction of the onset of shear localization based on machine learning

Akar

Ayli

Ulucak

et al. 2023

AIEDAM

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Predicting the onset of shear localization is among the most challenging problems in machining. This phenomenon affects the process outputs, such as machining forces, surface quality, and machined part tolerances. To predict this phenomenon, analytical, experimental, and numerical methods (especially finite element analysis) are widely used. However, the limitations of each method hinder their industrial applications, demanding a reliable and time-saving approach to predict shear localization onset. Additionally, since this phenomenon largely depends on the type and parameters of the constitutive material model, any change in these parameters requires a new set of simulations, which puts further restrictions on the application of finite element modeling. This study aims to overcome the computational efficiency of the finite element method to predict the onset of shear localization when machining Ti6Al4V using machine learning methods. The obtained results demonstrate that the FCM (fuzzy c-means) clustering ANFIS (adaptive network-based fuzzy inference system) has given better results in both training and testing when it is compared to the ANN (artificial neural network) architecture with an R2 of 0.9981. Regarding this, the FCM-ANFIS is a good candidate to calculate the critical cutting speed. To the best of the authors’ knowledge, this is the first study in the literature that uses a machine learning tool to predict shear localization.

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“…Considering the above point, in addition to tool life, the surface finish of the machined part is an equally important requirement. During dry machining of an aluminium alloy, the tool wear is mainly caused due to the formation of an adhesive aluminium layer and built-up *For correspondence edge [3][4][5]. This also greatly affects the surface finish of the machined surface.…”

Section: Literature Reviewmentioning

confidence: 99%

Influence of cutting parameters on cutting forces and surface roughness in dry turning of Al using PCD and different coated tools

et al. 2019

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Aluminium (Al) is the suitable material for aerospace and automotive industries due its light weight, corrosion resistance, weldability, non-magnetic and mechanical properties. But, machining of Al and its alloy and finding the suitable tool is really a big challenge because of its formation of BUE (Built-up Edge) and BUL (Built-up Layer). This paper presents the influence of cutting parameters (speed, feed and depth of cut) and its effect on the cutting force and the surface finish. Five different advanced cutting tool inserts (SPUN WC, SPGN WC, PCD, WC ? TiN and WC ? Ti(C, N) TiN ? Al 2 O 3 ) at different cutting speed (V c ) ranging between 300 m/min and 700 m/min and feed rate (f) of 0.045, 0.06, 0.09 and 0.125 mm/rev at a depth of cut of 0.2 mm (constant throughout the experiment) were taken for the experiment. Tool inserts were characterized by Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) analysis. The cutting forces were measured using Kistler force dynamometer. Amongst all tools, PCD provided a better result in all aspects but surprisingly WC tool provided a better surface finish with lesser tool wear. For all cutting conditions, high speed (670 m/min) and low feed rate (0.045 mm/rev) were recommended. adhana(0123456789().,-volV)FT3 ](0123456789().,-volV)

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Investigating the influence of friction conditions on finite element simulation of microscale machining with the presence of built-up edge

Cited by 13 publications

References 37 publications

Machinability of INCONEL718 Alloy with a Porous Microstructure Produced by Laser Melting Powder Bed Fusion at Higher Energy Densities

Machinability of INCONEL718 Alloy with a Porous Microstructure Produced by Laser Melting Powder Bed Fusion at Higher Energy Densities

Prediction of the onset of shear localization based on machine learning

Influence of cutting parameters on cutting forces and surface roughness in dry turning of Al using PCD and different coated tools

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