Numerical investigations of optimum turning parameters—AA2024-T351 aluminum alloy

Saleem, Waqas; Asad, Muhammad; Zain-ul-abdein, Muhammad; Ijaz, Hassan; Mabrouki, Tarek

doi:10.1080/10910344.2016.1224019

Cited by 19 publications

(15 citation statements)

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“…It was analyzed that all the deformed chips show a maximum stress concentration at the chip edge tip and shear zone. This effect was also published by Asad et al [52] and Waqas et al [21]. The deviation of maximum von Mises stress for μ = 0.2 and μ = 0.3 is shown in Fig.…”

Section: Analysis Of Chip Stresssupporting

confidence: 84%

“…The selection of optimum cutting parameters (cutting speed, feed rate, tool geometry, etc.) is also essential to control the tool-chip interaction consistently with a high material strain rate [20,21]. Different constitutive material damage models have been reported in the literature, such as Oxley's constitutive model [22], the power law model [23], the strain pathdependent model [24], and the Johnson-Cook (JC) model [25].…”

Section: Introductionmentioning

confidence: 99%

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Numerical modeling and simulation of macro- to microscale chip considering size effect for optimum milling characteristics of AA2024T351

Saleem

Ijaz

Al‐Zahrani

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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This research work deals with the numerical modeling and analysis of macro-to microscaled milling of a strain rate-sensitive alloy AA2024T351. The milling computations of a semicircular slot are performed in Abaqus/Explicit by employing the Johnson-Cook thermo-elasto viscoplastic material damage model. The Coulomb friction model is applied at the contact interfaces of work piece and cutting tool. Due to the simultaneous effect of cutting feed and angular speed (ω r), the geometry of uncut chip is modeled with variable section thickness to adapt the trochoidal path. The configuration of the uncut chip undertakes the macro-to micromilling effect. This research effort uniquely explains a comprehensive modeling and milling computations at macro-to microscales to identify the imperative milling characteristics. The presented formulation in form of the modified Johnson-Cook constitutive equation explains the size effect during micromilling. A parametric sensitivity analysis is performed with four different cutting speeds (500, 600, 700, and 800 m/min) and two contact friction coefficients (0.2 and 0.3) while keeping the feed rate constant (0.2 mm/teeth). It was observed that cutting speed and contract friction coefficient play a pivotal role in cutting reaction force and chip-tool interface temperature. A temperature in the range of 141-167 °C is predicted with different cutting speeds. The percentage deviations of the simulated cutting forces from the published experimental results are found promising. The estimated deviation may be because of compromising some imperative factors in numerical model, such as cutter vibrations, tool wear effect, tool run-out, and limitations, to develop an exact trochoidal trajectory.

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Section: Analysis Of Chip Stresssupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Numerical modeling and simulation of macro- to microscale chip considering size effect for optimum milling characteristics of AA2024T351

Saleem

Ijaz

Al‐Zahrani

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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“…Parametric sensitivity analysis (cutting reaction force (N), chip stress (MPa), and tool-chip interface temperate ( • C))[53].…”

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confidence: 99%

Computational Analysis and Artificial Neural Network Optimization of Dry Turning Parameters—AA2024-T351

et al. 2017

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In dry turning operation, various parameters influence the cutting force and contribute in machining precision. Generally, the numerical cutting models are adopted to establish the optimum cutting parameters and results are substantiated with the experimental findings. In this paper, the optimal turning parameters of AA2024-T351 alloy are determined through Abaqus/Explicit numerical cutting simulations by employing the Johnson-Cook thermo-viscoplastic-damage material model. Turning simulations were verified with published experimental data. Considering the constrained and nonlinear optimization problem, the artificial neural networks (ANN) were executed for training, testing, and performance evaluation of the numerical simulations data. Two feedforward backpropagation neural networks were developed with ten hidden neutrons in each hidden layer. The Log-Sigmoid transfer function and the Levenberg-Marquardt algorithm were applied in the model. The ANN models were studied with four input parameters: the cutting speed (200, 400, and 800 m/min), tool rake angle (5 • , 10 • , 14.8 • , and 17.5 •), cutting feed (0.3 and 0.4 mm), and the contact friction coefficients (0.1 and 0.15).The two target parameters include the tool-chip interface temperature and the cutting reaction force. The performance of the trained data was evaluated using root-mean-square error and correlation coefficients. The ANN predicted values were compared both with the Abaqus simulations and the published experimental findings. All of the results are found in good approximation to each other. The performance of the ANN models demonstrated the fidelity of solving and predicting the optimum process parameters.

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“…An increase in depth of cut increases the shear area, so higher tangential forces followed by feed force and radial force are required to cut the material [ 19 ]. Saleem et al [ 47 ] investigated that an increase in the positive rake angle decreases the cutting forces. This is attributed to an increase in the tool and chip contact area resulting in the easy formation of chips.…”

Section: Resultsmentioning

confidence: 99%

Experimental Investigation and Statistical Evaluation of Optimized Cutting Process Parameters and Cutting Conditions to Minimize Cutting Forces and Shape Deviations in Al6026-T9

et al. 2020

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Precise, economical and sustainable cutting operations are highly desirable in the advanced manufacturing environment. For this aim, the present study investigated the influence of cutting parameters (i.e., the cutting speed (c), feed rate (f), depth of cut (d) and positive rake angle (p)) and sustainable cutting conditions (dry and minimum quantity lubricant (MQL)) on cutting forces (i.e., feed force (Ff), tangential forces (Ft), radial force (Fr) and resultant cutting forces (Fc) and shape deviations (i.e., circularity and cylindricity) of a 6026-T9 aluminum alloy. The type of lubricant and insert used are virgin olive oil and uncoated tungsten carbide tool. Turning experiments were performed on a TAKISAWA TC-1 CNC lathe machine and cutting forces were measured with the help of a Kistler 9257B dynamometer. Shape deviations were evaluated by means of a Tesa Micro-Hite 3D DCC 474 coordinate measuring machine (CMM). Experimental runs were planned based on Taguchi mixture orthogonal array design L16. Analysis of variance (ANOVA) was performed to study the statistical significance of cutting parameters. Taguchi based signal to noise (S/N) ratios are applied for optimization of single response, while for optimization of multiple responses Taguchi based signal to noise (S/N) ratios coupled with multi-objective optimization on the basis of ratio analysis (MOORA) and criteria importance through inter-criteria correlation (CRITIC) are employed. ANOVA results revealed that feed rate, followed by a depth of cut, are the most influencing and contributing factors for all components of cutting forces (Ff, Ft, Fr, and Fc) and shape deviations (circularity and cylindricity). The optimized cutting parameters obtained for multi responses are c = 600 m/min, f = 0.1 mm/rev, d = 1 mm and p = 25°, while for cutting conditions, MQL is optimal.

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Numerical investigations of optimum turning parameters—AA2024-T351 aluminum alloy

Cited by 19 publications

References 1 publication

Numerical modeling and simulation of macro- to microscale chip considering size effect for optimum milling characteristics of AA2024T351

Numerical modeling and simulation of macro- to microscale chip considering size effect for optimum milling characteristics of AA2024T351

Computational Analysis and Artificial Neural Network Optimization of Dry Turning Parameters—AA2024-T351

Experimental Investigation and Statistical Evaluation of Optimized Cutting Process Parameters and Cutting Conditions to Minimize Cutting Forces and Shape Deviations in Al6026-T9

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