Micro-turning is a micro-mechanical cutting method used to produce small diameter cylindrical parts. Since the diameter of the part is usually small, it may be a little difficult to improve the surface quality by a second operation, such as grinding. Therefore, it is important to obtain the good surface finish in micro turning process using the ideal cutting parameters. Here, the multi-objective optimization of micro-turning process parameters such as cutting speed, feed rate and depth of cut were performed by response surface method (RSM). Two important machining indices, such as surface roughness and material removal rate, were simultaneously optimized in the micro-turning of a Ti6Al4V alloy. Further, the scanning electron microscope (SEM) analysis was done on the cutting tools. The overall results depict that the feed rate is the prominent factor that significantly affects the responses in micro-turning operation. Moreover, the SEM results confirmed that abrasion and crater wear mechanism were observed during the micro-turning of a Ti6Al4V alloy.

Cost and recyclability are among the primary factors on exploiting the engineering materials for their new applications. In this context, glass/pp-based sandwich panel has been studied experimentally and numerically with the aims of its potential applications in the automotive structures. The first part of this work presents the experimental results achieved for the load-carrying capacity of panels using three-point bend tests for its static flexural behaviour. Static behaviour is studied to compare the top-roller diameter effect on the flexural behaviour of the panels and shows a significant difference in the results. Impact behaviour of the panels is explored using three different types of impactor end-shapes that generate different levels of damage in the material with the same level of impact energy. The second part of this paper deals with the development of numerical models for the three-point bend and impact behaviour of the panels using a commercial finite element code of Abaqus. Strain energy-based homogenisation technique is employed to determine the equivalent orthotropic properties of complex circular honeycomb core material. The finite element models predict to a good level of the static and impact behaviour of the material when compared with the experiments.

Mechanical properties of the metals and their alloys are influenced by the material grain size at microscale. In the present study, the Johnson-Cook (JC) material model is modified to incorporate the effect of material's grain size along with the plasticity coupled damage model. 2D finite element (FE) simulations of turning process of an aerospace grade aluminium alloy 2024 (AA2024) were performed with different grain sizes using a commercial FE software, ABAQUS/Explicit. FE simulation results were compared with the published experimental data on turning process of AA2024. The proposed modified JC material model successfully simulated the increase in cutting force as a function of grain size refinement.

Aerospace aluminum alloys have gained the prime significance due to their excellent machining characteristics. Numerous experimental and numerical studies have been conducted to establish the optimum cutting parameters of these alloys. In the numerical cutting models, the authenticity of computational results is suspected particularly because of the complex interaction at tool–chip interface, which involves a high material strain rate and thermal processes. The fidelity of cutting simulation results is appraised by a parametric sensitivity analysis and actual experimentation. In this research, the orthogonal turning of AA2024-T351 aluminum is simulated in Abaqus/Explicit by using a thermoviscoplastic damage model and Coulomb friction model for the contact interfaces. A parametric sensitivity analysis is performed to comprehend the chip morphology, tool–chip interface temperature, reaction force, and strain. Different simulations are performed with varied cutting speeds (200, 400, 600, and 800 m/min), rake angles (5°, 10°, 14.8°, 17.5°), feeds (0.3, 0.4 mm), and friction coefficients (0.1, 0.15). It is observed that an increased rake angle decreases the cutting force and increases tool–chip interface temperature. Similarly, the cutting depth has prominent effect on chip–tool interface temperature as compared to the friction. The computational results are found in close approximation with the published experimental data of AA2024-T351.

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.