Modeling and Optimization of Machining Process in Discontinuously Reinforced Aluminium Matrix Composites

This article presents development of an Artificial Neural Networks (ANN) based model for the prediction of surface roughness during machining of composite material using Back Propagation algorithm. Statistically designed experiments based on Taguchi method were carried out on machining of Al/SiCp composite material. The experimentation helped generate a knowledge base for the ANN system and understand the relative importance of process, tool and work material dependent parameters on the roughness of surface generated during machining. The ANN model trained using the experimental data was found to predict the surface roughness with fair accuracy. An optimization approach was also proposed to obtain optimal cutting conditions that yield the desired surface roughness while maximizing the metal removal rate.     

Modeling and Optimization of Machining Process in Discontinuously Reinforced Aluminium Matrix Composites

Abstract

This article presents development of an Artificial Neural Networks (ANN) based model for the prediction of surface roughness during machining of composite material using Back Propagation algorithm. Statistically designed experiments based on Taguchi method were carried out on machining of Al/SiCp composite material. The experimentation helped generate a knowledge base for the ANN system and understand the relative importance of process, tool and work material dependent parameters on the roughness of surface generated during machining. The ANN model trained using the experimental data was found to predict the surface roughness with fair accuracy. An optimization approach was also proposed to obtain optimal cutting conditions that yield the desired surface roughness while maximizing the metal removal rate.     

Free abrasive wire saw machining of ceramics

Currently, many kinds of ceramics are used in advanced industrial fields due to their superior mechanical properties, such as thermal, wear, corrosion resistance, and lightweight features. Wire saw machining ceramic (Al₂O₃) was investigated by ultrasonic vibration in this study. Taguchi approach is a powerful design tool for high-quality systems. Material removal rate, wafer surface roughness, steel wire wear, kerf width, and flatness during machining ceramic were selected as quality character factors to optimize the machining parameters (swinging angle, concentration, mixed grain and direction of ultrasonic vibration) to get the larger-the-better (material removal rate) and the smaller-the-better (wafer surface roughness, steel wire wear, kerf width and flatness) machining characteristics by Taguchi method. The results indicated that wire swinging produces a higher material removal rate and good wafer surface roughness. Ultrasonic vibration improved material removal rate, without affecting the flatness under different machining conditions. Experimental results show that the optimal wire saw machining parameters based on grey relational analysis can be determined effectively and material removal rate increases from 2.972 to 3.324 mm²/min, wafer surface roughness decreases from 0.37 to 0.34 μm, steel wire wear decreases from 0.78 to 0.77 μm, kerf width decreases from 0.352 to 0.350 mm, and flatness decreases from 7.51 to 7.22 μm are observed.     

Construction of a surface roughness prediction model for high speed machining

In manufacturing environment prediction of surface roughness is very important for product quality and production time. For this purpose, the finite element method and neural network is coupled to construct a surface roughness prediction model for high-speed machining. A finite element method based code is utilized to simulate the high-speed machining in which the cutting tool is incrementally advanced forward step by step during the cutting processes under various conditions of tool geometries (rake angle, edge radius) and cutting parameters (yielding strength, cutting speed, feed rate). The influences of the above cutting conditions on surface roughness variations are thus investigated. Moreover, the abductive neural networks are applied to synthesize the data sets obtained from the numerical calculations. Consequently, a quantitative prediction model is established for the relationship between the cutting variables and surface roughness in the process of high-speed machining. The surface roughness obtained from the calculations is compared with the experimental results conducted in the laboratory and with other research studies. Their agreements are quite well and the accuracy of the developed methodology may be verified accordingly. The simulation results also show that feed rate is the most important cutting variable dominating the surface roughness state.     

Rapid measurement of surface roughness for face-milling aluminum using laser scattering and the Taguchi method

In this paper, an experimental model for the rapid measurement of surface roughness (R_rms) in CNC face-milling specimens using the laser speckle method and digital image processing is established. The specimens used in this study were made of 6061-aluminum alloys through the high-speed face-milling process. In order to evaluate the effect of machining conditions, such as the feed rate, the spindle speed, the depth of cut, and the material of the cutting tool on the roughness of the specimens, the Taguchi method was used to determine the optimal parameters for machining. The laser radiation results in the speckle structure formed in the space when coherent light is scattered through an optically rough surface. The features of the speckles depend on the characteristics of the rough surfaces. Hence, the experimental work for the roughness measurement is based on the speckle effect. The experimental setup in this study consisted of a He-Ne laser, a ground glass, a CCD camera, and a digital image processing system developed using the Virtual Basic language. Computer evaluation of the speckle images revealed the values of R_rms rapidly. This study proposed a precise and non-contact optical method for evaluating the surface roughness from 0.20 to 0.60 μm.     

MACHINING OF SODA LIME GLASS USING ABRASIVE HOT AIR JET: AN EXPERIMENTAL STUDY

Abrasive Jet Machining is becoming one of the most prominent machining techniques for glass and other brittle materials. In this article, an attempt has been made to combine abrasive and hot air to form an abrasive hot air jet. Abrasive hot air jet machining can be applied to various operations such as drilling, surface etching, grooving and micro finishing on the glass and its composites. The effect of air temperature on the material removal rate applied to the process of glass etching and grooving is discussed in this article. The roughness of machined surface is also analyzed. It is found that the Material Removal Rate (MRR) increases as the temperature of carrier media (air) is increased. The results have revealed that the roughness of machined surface is reduced by increasing temperature of carrier media. The mechanism of material removal rate has been discussed with aid of SEM micrographs.     

Design and implementation of a system for laser assisted milling of advanced materials

Laser assisted machining is an effective method to machine advanced materials with the added benefits of longer tool life and increased material removal rates. While extensive studies have investigated the machining properties for laser assisted milling(LAML), few attempts have been made to extend LAML to machining parts with complex geometric features. A methodology for continuous path machining for LAML is developed by integration of a rotary and movable table into an ordinary milling machine with a laser beam system. The machining strategy and processing path are investigated to determine alignment of the machining path with the laser spot. In order to keep the material removal temperatures above the softening temperature of silicon nitride, the transformation is coordinated and the temperature interpolated, establishing a transient thermal model. The temperatures of the laser center and cutting zone are also carefully controlled to achieve optimal machining results and avoid thermal damage. These experiments indicate that the system results in no surface damage as well as good surface roughness, validating the application of this machining strategy and thermal model in the development of a new LAML system for continuous path processing of silicon nitride. The proposed approach can be easily applied in LAML system to achieve continuous processing and improve efficiency in laser assisted machining.     

An investigation of the surface contours in laser grooving

The surface contours generated in laser grooving by a compound jet nozzle on mild steels have been characterized experimentally and theoretically in this study. A side-jet grooving nozzle was designed with various attacking angles to achieve the optimum conditions on the material removal rate and surface roughness for this technique. The grooving depth was estimated through the formulation of the energy equations with the consideration of exothermic heat and the grooving contours were measured by the laser displacement sensor. The results show a close agreement between the theoretical prediction and experimental testing. The process characteristics and the parametric analysis were also discussed.     

Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm

Modeling and optimization of cutting parameters are one of the most important elements in machining processes. The present study focused on the influence machining parameters on the surface roughness obtained in drilling of AISI 1045. The matrices of test conditions consisted of cutting speed, feed rate, and cutting environment. A mathematical prediction model of the surface roughness was developed using response surface methodology (RSM). The effects of drilling parameters on the surface roughness were evaluated and optimum machining conditions for minimizing the surface roughness were determined using RSM and genetic algorithm. As a result, the predicted and measured values were quite close, which indicates that the developed model can be effectively used to predict the surface roughness. The given model could be utilized to select the level of drilling parameters. A noticeable saving in machining time and product cost can be obtained by using this model.     

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.     

Roughness modeling and optimization in CNC end milling using response surface method: effect of workpiece material variation

Influence of machining parameters, viz., spindle speed, depth of cut and feed rate, on the quality of surface produced in CNC end milling is investigated. In the present study, experiments are conducted for three different workpiece materials to see the effect of workpiece material variation in this respect. Five roughness parameters, viz., centre line average roughness, root mean square roughness, skewness, kurtosis and mean line peak spacing have been considered. The second-order mathematical models, in terms of the machining parameters, have been developed for each of these five roughness parameters prediction using response surface method on the basis of experimental results. The roughness models as well as the significance of the machining parameters have been validated with analysis of variance. It is found that the response surface models for different roughness parameters are specific to workpiece materials. An attempt has also been made to obtain optimum cutting conditions with respect to each of the five roughness parameters considered in the present study with the help of response optimization technique.     

Statistical model to determine surface roughness when milling hastelloy C-22HS

The aim of this study is to develop the surface roughness prediction models, with the aid of statistical methods, for hastelloy C-22HS when machined by PVD and CVD coated carbide cutting tools under various cutting conditions. These prediction models were then compared with the results obtained experimentally. By using response surface method (RSM), first order models were developed with 95 % confidence level. The surface roughness models were developed in terms of cutting speed, feed rate and axial depth using RSM as a tool of design of experiment. In general, the results obtained from the mathematical models were in good agreement with those obtained from the machining experiments. It was found that the feed rate, cutting speed and axial depth played a major role in determining the surface roughness. On the other hand, the surface roughness increases with a reduction in cutting speed. PVD coated cutting tool performs better than CVD when machining hastelloy C-22HS. It was observed that most of the chips from the PVD cutting tool were in the form of discontinuous chip while CVD cutting tool produced continuous chips.     

Thermal modeling of the material removal rate and surface roughness for die-sinking EDM

The die-sinking electrical discharge machining (EDM) process is characterized by slow processing speeds. Research effort has been focused on optimizing the process parameters so as for the productivity of the process to be increased. In this paper a simple, thermal based model has been developed for the determination of the material removal rate and the average surface roughness achieved as a function of the process parameters. The model predicts that the increase of the discharge current, the arc voltage or the spark duration results in higher material removal rates and coarser workpiece surfaces. On the other hand the decrease of the idling time increases the material removal rate with the additional advantage of achieving slightly better surface roughness values. The model’s predictions are compared with experimental results for verifying the approach and present good agreement with them.     

An experimental investigation on machining parameters of AISI D2 steel using WEDM

The performance of the wire electrodischarge machining (WEDM) machining process largely depends upon the selection of the appropriate machining variables. Optimization is one of the techniques used in manufacturing sectors to arrive for the best manufacturing conditions, which are essential for industries toward manufacturing of quality products at lowest cost. As there are many process variables involved in the WEDM machining process, it is difficult to choose a proper combination of these process variables in order to maximize material removal rate and to minimize tool wear and surface roughness. The objective of the this work is to investigate the effects of process variables like pulse on time, pulse off time, peak current, servo voltage, and wire feed on material removal rate (MRR), surface roughness (SR), gap voltage, gap current, and cutting rate in the WEDM machining process. The experiment has been done using Taguchi’s orthogonal array L27 (3⁵). Each experiment was conducted under different conditions of input parameters and statistically evaluated the experimental data by analysis of variance (ANOVA) using MINITAB and Design Expert tools. The present work also aims to develop mathematical models for correlating the inter-relationships of various WEDM machining parameters and performance parameters of machining on AISI D2 steel material using response surface methodology (RSM).The significant machining parameters and the optimal combination levels of machining parameters associated with performance parameters were also drawn. The observed optimal process parameter settings based on composite desirability (61.4 %) are pulse on time 112.66 μs, pulse off time 45 μs, spark gap voltage 46.95 V, wire feed 2 mm/min, peak current of 99.99 A for achieving maximum MRR, gap current, gap voltage, cutting rate, and minimum SR; finally, the results were experimentally verified.     

超声辅助内圆磨削40Cr15Mo2VN轴承套圈的试验研究#br#

针对传统加工方式难以获得轴承套圈较小的表面粗糙度和表面波纹度的问题,采用超声辅助内圆磨削的加工方法来改善轴承套圈的表面质量。基于超声内圆磨削单颗磨粒运动轨迹分析,建立了表面粗糙度的理论模型,通过对轴承套圈进行超声内圆磨削试验,研究了各个加工参数对轴承表面质量的影响。研究结果表明：超声内圆磨削加工方法可明显改善轴承的表面质量;增大超声振幅可减小表面粗糙度而表面波纹度会先减小后增大;随着砂轮转速的增大,表面粗糙度及表面波纹度会先减小后增大;磨削深度和进给速度的增大会使表面粗糙度及表面波纹度增大,但超声内圆磨削可减小它们的增加量。     

Experimental investigation on CO2 laser-assisted micro-slot milling characteristics of borosilicate glass

Demands for micro-machining on glass have been increasing in various industries due to the unique properties of glass, such as transparency or biocompatibility. However, micro-channel fabrication on glass with high precision has been challenging due to its brittle characteristics. This research presents the CO₂ laser-assisted micro-milling process and investigates the machining characteristics experimentally. Micro-channels without cracks were fabricated using micro-end mill and CO₂ laser irradiation as an assisting heat source. Compared to the process without laser heating in the same matching conditions, the average surface roughness was reduced by 96%, and cutting force was reduced by 28% and 66% for the feed and thrust direction, respectively. Continuous and sheared chips were observed with laser heating, indicating the process is in ductile-regime machining. Through the investigation of machining parameters, it was found that micro-channels with low average surface roughness can be achieved at the proper laser power when the workpiece is heated up to the strain point at tool position, at low feed rate, and at high axial depth of cut, as long as the tool withstands the cutting forces. Consequently, it can be concluded that it is possible to increase the material removal rate in micro-milling of borosilicate glass with high quality by using the CO₂ laser, which was found to be an effective and suitable heating method.     

Effect of high-frequency orbital and vertical oscillations of the laser focus position on the quality of the cut surface in a thick plate by laser beam machining

In laser beam machining with oxygen gas, striations are formed on the cut surface due to the cyclic oxidization reaction, which is affected by the cutting conditions and the thermal properties of the workpiece. The formation of striations causes an increase in surface roughness. In order to reduce the surface roughness, we propose controlling the formation of striations by utilizing multi-DOF oscillation of the focus position of the laser. In this paper, we construct a laser machining system in which positioning control of the focus position is achieved by driving the focal lens. The effect of orbital oscillation of the laser focus position parallel to the top surface of the workpiece and vertical oscillation perpendicular to this surface on the formation of striations was evaluated by performing laser cutting tests on thick mild steel plates. The experimental results show that control of the formation of striations can be realized by choosing the appropriate oscillation conditions. The surface roughness with oscillation was less than half that without oscillation.     

The effects of the silicon wafer resistivity on the performance of microelectrical discharge machining

In this study, the performance of Si wafer machining by employing the die-sinking microelectrical discharge machining technique is reported. Specifically, the machining performance was examined on both high- (1–10 Ω cm) and low-resistivity (0.001–0.005 Ω cm) Si wafers by means of using a range of discharge energies. In this regard, the machining time, material removal rate, surface quality, surface roughness, and material mapping, which are categorized among the important properties in micromachining, have been investigated. In order to analyze the surface properties and to perform the elemental analysis, the scanning electron microscope and energy-dispersive X-ray spectroscopy were used. In contrast, the 3D surface profiler was used to evaluate the roughness of machined surface. The results of this experimental study revealed that the electrical resistivity and discharge energy parameter of microelectrical discharge machining had a great influence on the Si wafer machining performances. The observations in this study indicated a decrease in machining time, high material removal rate, and high surface roughness with an increased discharge energy values. Overall, it was learnt that the minimum amount of energy required to machine Si wafer was 5 μJ for both low and high-resistivity Si. In addition, the highest material removal of 5.842 × 10^?5 mm³/s was observed for low-resistivity Si. On the contrary, the best surface roughness, R _a, of 0.6203 μm was achieved for high-resistivity Si and it also pointed to a higher carbon percentage after the machining process.     

A model to simulate surface roughness in the pad dressing process

Pad dressing, which is one of the most important planarization processes, is widely used in CMP. The estimation of surface roughness under various machining parameters (such as dressing force, diamond density of the dresser, rotational speed of the dresser, different machining paths, etc.) is essential to the pad dressing process. In this study, elastic-plastic theory and the wear model are used to construct the expression for the magnitude of material removal as a function of the indentation depth. The deformation of the pad is obtained by using elastic-plastic theory, and the material removal caused by individual micro-contacts is calculated with the help of wear theory. Finally, the macroscopic wear volume is found by summing the volumetric wear of each individual micro-contact. A parametric study is conducted to explore the influence on the surface roughness results and the pad dressing interfacial phenomena of operational parameters. The results reveal that a rapid initial improvement followed by a leveling off, manifesting a saturation effect. Moreover, the model shows that a higher dressing force with a dresser speed close to the pad speed and a moderate translation speed increase the material removal rate corresponding with a lower surface roughness dressing.