Predicting the Effects of Cutting Parameters and Tool Geometry on Hard Turning Process Using Finite Element Method

This study focuses on addressing the severe plastic deformation (SPD) behavior and the effects o f machining parameters on microstructure alternations in machined surface cre ated from high-speed machining. A finite element (FE) model is proposed to predict the orthogonal machining of A16061-T6 alloys at high speeds. By extracting strains, strain rates, stresses, and temperatures from this model, a dislocation density-based model is incorporated into it as a user-defined subroutine to predict dislocation densities and grain sizes in machined surface. The predicted results show that dislocation densities decrease with the depths below the machined surface, but grain sizes present an opposite tendency. Higher cutting speeds are associated with thinner plastic deformation layers. Dislocation densities decrease with cutting speeds, but grain sizes increase with cutting speeds in machined surface. Dislocation densities decrease initially and then increase with feed rates. There exists a critical feed rate to generate the maximum SPD layer in machined surface. Tool rake angle has a great impact on the depth o f plastic deformation layer. Thus, it affects the distributions of dislocation densities and grain sizes. A large negative rake angle can induce an increased dislocation density in machined surface. The predicted chip thicknesses, cutting forces, distributions of dislocation densities, and grain sizes within the range of machining parameters have good agreement with experi ments in terms of chip morphology, cutting forces, microstructure, and microhardness in chip and machined surface.

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“…Two distinct zones are assumed among these interfaces: sticking region, and sliding region, as shown in Fig. 1 [25][26][27][28][29][30][31].…”

Section: Friction Model Between Chip-tool-work Interfacesmentioning

confidence: 99%

Dislocation Density and Grain Size Evolution in the Machining of Al6061-T6 Alloys

Ding

Zhang

Liu

2014

Journal of Manufacturing Science and Engineering

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“…Generally, D 1 to D 5 values are determined through experiments [43]. The equivalent plastic strain with a scalar damage parameter ω can be expressed by [44,45]:…”

Section: Cutting Simulationsmentioning

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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“…Based on complexity of the 3D cutting edge geometry and chip formation mechanisms, the finite element method may be necessary to predict indentation depths required for cutting rate determination. Recent progress has been made on an oblique 3D finite element model, which was validated through hard turning of steel [13].…”

Section: (47)mentioning

confidence: 99%

Prediction of Cutting Time When Crosscutting Rounds, Pipe, and Rectangular Bar With a Gravity Fed Portable Bandsaw

Sagar

James

2014

Journal of Manufacturing Science and Engineering

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Portable bandsaws are gaining in popularity for their use on remote jobsites to efficiently cut structural materials such as bar, pipe, and channel. Some of their increased popularity is due to the recent introduction of high watt-hour lithium ion batteries, which has further improved the portability of bandsaws by making them cordless. However, with cordless bandsaws, knowledge of cutting rates becomes more important as battery runtime limits productivity. Unlike industrial cutoff bandsaws that typically have feed rate control, the cutting rate of portable bandsaws is determined by operator applied pressure and gravity. While some research has highlighted the cutting mechanics of bandsaws and related wear processes, there is a lack of progress in the area of predicting cutoff time as a function of sawing parameters, such as applied thrust force, blade speed, workpiece material properties, and geometry of the cross section. Research was conducted to develop and experimentally verify a mechanistic model to predict cutting rates of various cross sectional geometries with a gravity fed portable bandsaw. The analytical model relies upon experimental determination of a cutting constant equation, which was developed for a low carbon steel workpiece cut with an 18 teeth per inch (TPI) blade. The model was employed to predict crosscutting times for steel rounds, squares, and tubes for several conditions of thrust force and blade speed. Model predictions of cutting time were in close agreement with experimental results.

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Predicting the Effects of Cutting Parameters and Tool Geometry on Hard Turning Process Using Finite Element Method

Cited by 36 publications

References 24 publications

Dislocation Density and Grain Size Evolution in the Machining of Al6061-T6 Alloys

Dislocation Density and Grain Size Evolution in the Machining of Al6061-T6 Alloys

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

Prediction of Cutting Time When Crosscutting Rounds, Pipe, and Rectangular Bar With a Gravity Fed Portable Bandsaw

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