Prediction of cutting force, tool wear and surface roughness of Al6061/SiC composite for end milling operations using RSM

The results of mathematical modeling and the experimental investigation on the machinability of aluminium (Al6061) silicon carbide particulate (SiCp) metal matrix composite (MMC) during end milling process is analyzed. The machining was difficult to cut the material because of its hardness and wear resistance due to its abrasive nature of reinforcement element. The influence of machining parameters such as spindle speed, feed rate, depth of cut and nose radius on the cutting force has been investigated. The influence of the length of machining on the tool wear and the machining parameters on the surface finish criteria have been determined through the response surface methodology (RSM) prediction model. The prediction model is also used to determine the combined effect of machining parameters on the cutting force, tool wear and surface roughness. The results of the model were compared with the experimental results and found to be good agreement with them. The results of prediction model help in the selection of process parameters to reduce the cutting force, tool wear and surface roughness, which ensures quality of milling processes.     

Depth of cut models for multipass abrasive waterjet cutting of alumina ceramics with nozzle oscillation

An experimental study of the depth of cut in multipass abrasive waterjet (AWJ) cutting of alumina ceramics with controlled nozzle oscillation is presented. It is found that this cutting technique can significantly increase the depth of cut by an average of 50.8% as compared to single pass cutting without nozzle oscillation under the corresponding cutting conditions and within the same cutting time. Predictive models for the depth of cut are then developed. The modelling process starts with single pass cutting using a dimensional analysis technique and the particle erosion theories applied to alumina ceramics, before progressing to the development of the models for multipass cutting. The models are finally assessed both qualitatively and quantitatively with experimental data. It is shown that the model predictions are in good agreement with the experimental data with the average deviations of about 1%.     

Depth of cut models for multipass abrasive waterjet cutting of alumina ceramics with nozzle oscillation

An experimental study of the depth of cut in multipass abrasive waterjet (AWJ) cutting of alumina ceramics with controlled nozzle oscillation is presented. It is found that this cutting technique can significantly increase the depth of cut by an average of 50.8% as compared to single pass cutting without nozzle oscillation under the corresponding cutting conditions and within the same cutting time. Predictive models for the depth of cut are then developed. The modelling process starts with single pass cutting using a dimensional analysis technique and the particle erosion theories applied to alumina ceramics, before progressing to the development of the models for multipass cutting. The models are finally assessed both qualitatively and quantitatively with experimental data. It is shown that the model predictions are in good agreement with the experimental data with the average deviations of about 1%.     

Modelling and analysis of hybrid electrochemical turning-magnetic abrasive finishing of 6061 Al/Al2O3 composite

This paper integrates the electrochemical turning (ECT) process and magnetic abrasive finishing (MAF) to produce a combined process that improves the material removal rate (MRR) and reduces surface roughness (SR). The present study emphasizes the features of the development of comprehensive mathematical models based on response surface methodology (RSM) for correlating the interactive and higher-order influences of major machining parameters, i.e. magnetic flux density, applied voltage, tool feed rate and workpiece rotational speed on MRR and SR of 6061 Al/Al₂O₃ (10% wt) composite. The paper also highlights the various test results that also confirm the validity and correctness of the established mathematical models for in-depth analysis of the effects of hybrid ECT- MAF process parameters on metal removal rate and surface roughness. Further, optimal combination of these parameters has been evaluated and it can be used in order to maximize MRR and minimize SR. The results demonstrate that assisting ECT with MAF leads to an increase machining efficiency and resultant surface quality significantly, as compared to that achieved with the traditional ECT of some 147.6% and 33%, respectively.     

Effects of process parameters on surface roughness in abrasive waterjet cutting of aluminium

Abrasive waterjet cutting is a novel machining process capable of processing wide range of hard-to-cut materials. Surface roughness of machined parts is one of the major machining characteristics that play an important role in determining the quality of engineering components. This paper shows the influence of process parameters on surface roughness (R_a) which is an important cutting performance measure in abrasive waterjet cutting of aluminium. Taguchi’s design of experiments was carried out in order to collect surface roughness values. Experiments were conducted in varying water pressure, nozzle traverse speed, abrasive mass flow rate and standoff distance for cutting aluminium using abrasive waterjet cutting process. The effects of these parameters on surface roughness have been studied based on the experimental results.     

Optimizing multi-requirements of surface finishing in magnetic abrasive cutting process using grey relational analysis

Journal of Mechanical Science and Technology - Mechanical machining inevitably generates undesirable parts on the surface of workpieces. It brings adverse effects in terms of manufacturing cost,...     

Modeling surface roughness for polishing process based on abrasive cutting and probability theory

The surface roughness is a variable used to describe the quality of polished surface. This article presents a surface roughness model based on abrasive cutting and probability theory, which considers the effects of abrasive grain shape, grit and distribution feature, pressure on surface roughness. The abrasive grain protrusion heights are thought to close to Gaussian distribution, and then the relationship between the indentation depth and the pressure based on Hertz contact theory is obtained. Surface roughness prediction model is established by calculating indentation depth of the abrasive grains on workpiece surface. The maximum surface profile height (R_y) is approximately equal to the maximum indentation depth of the abrasive grain. The arithmetic average surface roughness (R_a) is equal to the average indentation depth of the abrasive grain. The effects of process parameters such as pressure and grit on R_y and R_a were simulated and analyzed in detail.     

Processing and properties of Al2O3/SiC nanocomposites

Alumina/SiC nanocomposites were produced by mechanical mixture of commercial powders. The preparation steps involved the vigorous mixing of the powders and drying under conditions where the homogeneous mixture was kept stable. Pressureless sintering of die-pressed powders achieved reasonable densities (～97% theoretical density) for 2·5wt% of SiC on sintering at 2073 K. Higher SiC contents strongly reduced the sintered density. The use of a more reactive alumina (finer matrix powder) gave similar results. Hot pressing at 1973 K/1 h/25 MPa produced high-density materials for SiC contents as high as 20 wt%. Transmission and scanning electron microscopy analysis showed that the SiC particles were well distributed and were situated both inside the grains and on the grain boundaries of the alumina matrix. The SiC strongly inhibited grain growth in the matrix in keeping with the Zener model. The bend strength increased as the SiC content increased, a result partly explained by the grain size refinement. The strength improvement of 20% over monolithic was explained in terms of the change to an intergranular fracture mode.     

Optimisation of process parameters for multi-performance characteristics in EDM of Al2O3 ceramic composite

The advantages of electrical discharge machining (EDM) in machining of complex ceramic components have promoted research in the area of EDM of ceramic composites. The recent developments in ceramic composites are focused not only on the improvements of strength and toughness, but also on possibilities for difficult-to-machine shapes using EDM. One such EDM-machinable ceramic composite material (Al₂O₃–SiC_w–TiC) has been developed recently and has been selected in the present study to investigate its EDM machinability. Experiments were conducted using discharge current, pulse-on time, duty cycle and gap voltage as typical process parameters. The grey relational analysis was adopted to obtain grey relational grade for EDM process with multiple characteristics namely material removal rate and surface roughness. Analysis of variance was used to study the significance of process variables on grey relational grade which showed discharge current and duty cycle to be most significant parameters. Other than discharge current and duty cycle, pulse-on time and gap voltage have also been found to be significant. To validate the study, confirmation experiment has been carried out at optimum set of parameters and predicted results have been found to be in good agreement with experimental findings.     

Optimization of machining characteristics in electric discharge machining of 6061Al/Al2O3p/20P composites by grey relational analysis

Present investigation applied the designs of experiments and grey relational analysis (GRA) approach to optimise parameters for electrical discharge machining process of 6061Al/Al₂O₃p/20P aluminium metal matrix composites. Planning of experiments was based on an L18 (2^1?×?3^5) orthogonal array to determine an optimal setting. The process parameters included one noise factor, aspect ratio having two levels and five control factors, viz. pulse current, pulse ON time, duty cycle, gap voltage and tool electrode lift time with three levels each. The material removal rate, tool wear rate and surface roughness were selected as the evaluation criteria, in this study. Optimal combination of process parameters is determined by the grey relational grade (GRG) obtained through GRA for multiple performance characteristics. Analysis of variance for the GRG is also implemented. It is shown that through GRA, the optimization of the multiple performance characteristics can be greatly simplified.     

Artificial neural network and regression models for performance prediction of abrasive waterjet in rock cutting

An experimental study is carried out for modeling the rock cutting performance of abrasive waterjet. Kerf angle (KA) is considered as a performance criteria and modeled using artificial neural network (ANN) and regression analysis based on operating variables. Three operating variables, including traverse speed, standoff distance, and abrasive mass flow rate, are studied for obtaining different results for the KA. Data belonging to the trials are used for construction of ANN and regression models. The developed models are then tested using a test data set which is not utilized during construction of models. Additionally, the regression model is validated using various statistical approaches. The results of regression analysis are also used to determine the significant operating variables affecting the KA. Furthermore, the performances of derived models are compared for showing the accuracy levels in prediction of the KA. As a result, it is concluded that both ANN and regression models can give adequate prediction for the KA with an acceptable accuracy level. The compared results reveal also that the corresponding ANN model is more reliable than the regression model. On the other hand, the standoff distance and traverse speed are statistically determined as dominant operating variables on the KA, respectively.     

Wear properties of Saffil/Al,Saffil/Al2O3/Al and Saffil/SiC/Al hybrid metal matrix composites

The purpose of this study is to investigate the wear properties of Saffil/Al, Saffil/Al₂O₃/Al and Saffil/SiC/Al hybrid metal matrix composites (MMCs) fabricated by squeeze casting method. Wear tests were done on a pin-on-disk friction and wear tester under both dry and lubricated conditions. The wear properties of the three composites were evaluated in many respects. The effects of Saffil fibers, Al₂O₃ particles and SiC particles on the wear behavior of the composites were elucidated. Wear mechanisms were analyzed by observing the worn surfaces of the composites. The variation of coefficient of friction (COF) during the wear process was recorded by using a computer. Under dry sliding condition, Saffil/SiC/Al showed the best wear resistance under high temperature and high load, while the wear resistances of Saffil/Al and Saffil/Al₂O₃/Al were very similar. Under dry sliding condition, the dominant wear mechanism was abrasive wear under mild load and room temperature, and the dominant wear mechanism changed to adhesive wear as load or temperature increased. Molten wear occurred at high temperature. Compared with the dry sliding condition, all three composites showed excellent wear resistance when lubricated by liquid paraffin. Under lubricated condition, Saffil/Al showed the best wear resistance among them, and its COF value was the smallest. The dominant wear mechanism of the composites under lubricated condition was microploughing, but microcracking also occurred to them to different extents.     

Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method

This paper presents a new approach for the optimization of drilling parameters on drilling Al/SiC metal matrix composite with multiple responses based on orthogonal array with grey relational analysis. Experiments are conducted on LM25-based aluminium alloy reinforced with green bonded silicon carbide of size 25 μm (10% volume fraction). Drilling tests are carried out using TiN coated HSS twist drills of 10 mm diameter under dry condition. In this study, drilling parameters namely cutting speed, feed and point angle are optimized with the considerations of multi responses such as surface roughness, cutting force and torque. A grey relational grade is obtained from the grey analysis. Based on the grey relational grade, optimum levels of parameters have been identified and significant contribution of parameters is determined by ANOVA. Confirmation test is conducted to validate the test result. Experimental results have shown that the responses in drilling process can be improved effectively through the new approach.     

Three body abrasive wear characteristics of plasma sprayed conventional and nanostructured Al2O3-13%TiO2 coatings

In this paper, the conventional Metco130 coatings, and two kinds of nanostructured coatings (NP and NS coatings) were fabricated by plasma spray with different feed powders. The coatings were evaluated by indentation, scratch and three body abrasive wear tests. The NP coating sprayed with plasma densified feed powder had the highest hardness, crack growth resistance and scratch resistance. Test results exhibited that the nanostructured coatings had greatly improved three body abrasive wear resistance compared with conventional coatings. The three body abrasive wear resistance of NP coatings was about three times that of conventional coatings. The failure mode in scratch tests and wear mechanism of three coatings were also discussed.     

Prediction of surface roughness in abrasive waterjet machining of particle reinforced MMCs using genetic expression programming

Machining of particle-reinforced metal matrix composites has been considerably difficult due to the extremely abrasive nature of the reinforcements that causes rapid tool wear and high machining cost. Abrasive water jet (AWJ) machining has proven to be a viable technique to machine such materials compared to conventional machining processes. The present study is focused on the surface roughness of AWJ cut surfaces and genetic expression programming (GEP) was proposed to predict surface roughness in AWJ machining of 7075 Al alloy composites reinforced with Al₂O₃ particles. In the development predictive models, characteristics of materials such as size and weight fraction of reinforcement particles, and depth of cut were considered as model variables. The training and testing data sets were obtained from the well-established machining test results. The weight fraction of particle, size of particle, and depth of cut were used as independent input variables, while arithmetic mean of surface roughness, maximum roughness of profile height, and mean spacing of profile irregularity as dependent output variables. Different models for the output variables were predicted on the basis of training data set using GEP and accuracy of the best model was proved with testing data set. The test results showed that output variables increased with increasing input variables. The predicted results were compared with experimental results and found to be in good agreement with the experimentally observed ones.     

Al_2O_3-TiB_2 刀具的磨损特性

对ＴｉＢ２颗粒增强Ａｌ２Ｏ３刀具在车削正火态、调质态４５＃钢和球墨铸铁齿轮坯时的刀具磨损性能、磨损机理进行了研究，并与硬质合金刀具的耐磨性能进行了对比。结果表明：Ａｌ２Ｏ３┐ＴｉＢ２陶瓷刀具具有良好的耐磨性能。刀具磨损主要以脆性剥离为主，同时存在着犁耕和塑性流变过程，陶瓷刀具表面形成的粘结层结构疏松，与基体结合力较弱，较易脱落，不易形成粘结磨损。     

Simulation of surface generated during abrasive flow finishing of Al/SiCp-MMC using neural networks

Abrasive flow machining (AFM) is a multivariable finishing process which finds its use in difficult to finish surfaces on difficult to finish materials. Near accurate prediction of generated surface by this process could be very useful for the practicing engineers. Conventionally, regression models are used for such prediction. This paper presents the use of artificial neural networks (ANN) for modeling and simulation of response characteristics during AFM process in finishing of Al/SiC_p metal matrix composites (MMCs) components. A generalized back-propagation neural network with five inputs, four outputs, and one hidden layer is designed. Based upon the experimental data of the effects of AFM process parameters, e.g., abrasive mesh size, number of finishing cycles, extrusion pressure, percentage of abrasive concentration, and media viscosity grade, on performance characteristics, e.g., arithmetic mean value of surface roughness (R _a, micrometers), maximum peak–valley surface roughness height (R _t, micrometers), improvement in R _a (i.e., ΔR _a), and improvement in R _t (i.e., ΔR _t), the networks are trained for finishing of Al/SiC_p-MMC cylindrical components. ANN models are compared with multivariable regression analysis models, and their prediction accuracy is experimentally validated.