Investigating the Effect of Tic Coating Layer on the Surface Roughness of Tungsten Carbide Cutting Tools Using Taguchi Design

In this study, external longitudinal turning operation was performed on (AISI 1020) steel to examine the influences of coating of the cutting tool on the machined surface roughness. The cutting tools used were coated and uncoated cemented carbide inserts. The tests are performed at four spindle speeds (80, 315, 500, and 800) rpm, at each of which two feed rates (0.2 and 0.5mm/rev) and two depth of cut (0.5 and 0.7mm) were used. Taguchi design of experiments (DOE) with a designed mathematical predictive model was used to investigate the effect of the coating layer and determine the machining conditions for minimum surface roughness. Accordingly, a suitable mixed orthogonal array L16 (3*4) was selected. The results showed that the surface roughness produced by using TiC coated inserts for identical machining conditions was lower than that produced due to uncoated tool by 41% to 53%. Regression analysis showed that the non-linear quadratic polynomial equation appears to be more suitable for representing the relation of spindle speed, feed rate, and depth of cut with the surface roughness. Taguchi method and the designed mathematical model had been used to predict the optimal cutting conditions. A confirmation test for the obtained results verified that the designed Taguchi experiments and the designed model successfully investigated the effect of the coating on the surface roughness. Data fit ver.9 and Mtb14 software had been employed to achieve the object of the presented work.

Analysis and prediction of the impact of technological parameters on cutting force components in rough milling of AZ31 magnesium alloy

This paper presents the results of experimental study of the AZ31 magnesium alloy milling process. Dry milling was carried out under high-speed machining conditions. First, a stability lobe diagram was determined using CutPro software. Next, experimental studies were carried out to verify the stability lobe diagram. The tests were carried out for different feed per tooth and cutting speed values using two types of tools. During the experimental investigations, cutting forces in three directions were recorded. The obtained time series were subjected to general analysis and analysis using composite multiscale entropy. Modelling and prediction were performed using Statistica Neural Network software, in which two types of neural networks were applied: multi-layered perceptron and radial basis function. It was observed that milling with high cutting speed values allows for component values of cutting force to be lowered as a result of the transition into the high-speed machining conditions range. In most cases, the highest values for the analysed parameters were recorded for the component Fx, whereas the lowest were recorded for Fy. Additionally, the paper shows that a prediction (with the use of artificial neural networks) of the components of cutting force can be made, both for the amplitudes of components of cutting force Famp and for root mean square Frms.

Mapping Ductile-To-Fragile Transition And The Effect of Tool Nose Radius In Diamond Turning of Single-Crystal Silicon

Although it has long been known that tools with more negative rake angles allow the ductile regime to be achieved when machining monocrystalline silicon; little has been discussed about the tool-material interaction in terms of the microgeometric contact of the tool tip at this interface. In this paper, the tool rake angle was varied in order to change the undeformed chip thickness value once the tool cutting radius, formed in front of the tool rake face, changes when the tool rake angle becomes more negative. Based on the statistical design of the experiment applied to cutting tests, a map relating values of transition pressure and different crystallographic directions is built to assist in determining machining conditions with a ductile response within a wider spectrum based on tool rake angle under different machining conditions. The results obtained allowed to answer questions under which machining conditions and tool geometry account for better surface finishes, lower machining forces, and lower residual stresses. The response surfaces generated provided answers capable of establishing under which cutting radii yielded more ductile mode material removal and avoided a brittle response, related to anisotropic response due to change in the crystallographic direction. Finally, we used the brittle-to-ductile transition map to determine a more suitable machining condition to diamond turn Fresnel lenses in single crystal silicon.

Multi-Objective Optimization of WEDM of Aluminum Hybrid Composites Using AHP and Genetic Algorithm

Aluminum hybrid composites have the potential to satisfy emerging demands of lightweight materials with enhanced mechanical properties and lower manufacturing costs. There is an inclusion of reinforcing materials with variable concentrations for the preparation of hybrid metal matrix composites to attain customized properties. Hence, it is obligatory to investigate the impact of different machining conditions for the selection of optimum parameter settings for aluminum-based hybrid metal matrix composite material. The present study aims to identify the optimum machining parameters during wire electrical discharge machining of samples prepared with graphite, ferrous oxide, and silicon carbide. In the present research work, five different process parameters and three response parameters such as material removal rate, surface roughness, and spark Gap are considered for process optimization. Energy-dispersive spectroscopy and scanning electron microscopy analysis reported the manifestation of the recast layer. Analytical hierarchy process and genetic algorithm have been successfully implemented to identify the best machining conditions for hybrid composites.

AN INVERSE OPTIMIZATION PROBLEM OF HEAT TRANSFER IN THE MACHINING PROCESS – A REVIEW

This paper reports on the results of research on thermal aspects in the process of material removal by inverse heat transfer problem. The research focuses on the identification, modeling and optimization of machining process based on the measured temperature at a particular point of the workpiece. The inverse approach determines the overall temperature distribution of the workpiece and the unknown heat flux at the tool/workpiece interface in machining. By introducing and minimizing an objective function based on the heat flux function, relationship of the heating power and duration on the surface layer of the workpiece is optimized. In this way, the most favourable machining conditions are determined in order to achieve high productivity and quality levels. The inverse optimization problem is solved by using the analytical, numerical and regularization methods. Formulation, application and analysis of the inverse optimization problem of heat transfer are shown on the example of creep-feed grinding. The creep-feed grinding process is a widely used abrasive machining process that is characterized by high thermal load of the workpiece. The results of the inverse optimization problem were verified by a series of experiments under different machining conditions.

CNN-GRU Network-based Force Prediction Approach for Variable Working Condition Milling Clamping Points of Deformable Parts

Improper clamping is one of the major causes of part deformation. Improving the fixture arrangement through force analysis of clamping points is an effective means to suppress or improve machining deformation. However, the existing research focuses on the monitoring and off-line optimization of the clamping point force, which has a certain lag on the machining deformation control, and it is difficult to predict the clamping point force due to the time-varying coupling effect of multiple factors such as process parameters, cutting force and clamping point force in the machining process. Inspired by the excellent performance of convolutional neural networks and gated recurrent networks in feature extraction and learning of temporal association laws, this paper proposes a CNN-GRU-based method for predicting the force state of clamping points under variable working conditions. Firstly, a force prediction model of clamping point during milling process with variable working conditions is established. Secondly, a convolutional neural network is designed to extract the features of dynamic coupled machining conditions. Then, a network of gated recurrent units is constructed to learn the temporal correlation law between the machining conditions and the forces on the clamping points to achieve force prediction of the clamping points during machining. Finally, it was verified by the milling process of the piston skirt. The results show that CNN-GRU can effectively predict the clamping force. In addition, CNN-GRU has higher computational efficiency and accuracy compared with CNN-LSTM, CNN-RNN and CNN-BP.