OPTIMAL MACHINING CONDITIONS FOR TURNING OF PARTICULATE METAL MATRIX COMPOSITES USING TAGUCHI AND RESPONSE SURFACE METHODOLOGIES

The present investigation focuses on the influence of machining parameters on the surface finish obtained in turning of LM25 Al/SiC particulate composites. The experiments are conducted based on Taguchi's experimental design technique. In this work, the effect of machining parameters on the surface roughness is evaluated and optimum machining conditions for maximizing the metal removal rate and minimizing the surface roughness are determined using response surface methodology. A second-order response surface model for the surface roughness is developed to predict the surface roughness. The predicted values and measured values are fairly close to each other, which indicates that the developed model can be effectively used to predict the surface roughness on the machining of Al/SiC-MMC composites with 95% confidence intervals within the ranges of parameters studied.     

Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling

This paper discusses the use of Taguchi and response surface methodologies for minimizing the surface roughness in machining glass fiber reinforced (GFRP) plastics with a polycrystalline diamond (PCD) tool. The experiments have been conducted using Taguchi’s experimental design technique. The cutting parameters used are cutting speed, feed and depth of cut. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness is determined. A second-order model has been established between the cutting parameters and surface roughness using response surface methodology. The experimental results reveal that the most significant machining parameter for surface roughness is feed followed by cutting speed. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the surface roughness in the machining of GFRP composites. The predicted values are confirmed by using validation experiments.     

Application of the central composite design in optimization of machining parameters in drilling hybrid metal matrix composites

In this study, the application of response surface methodology (RSM) and central composite design (CCD) for modeling, optimization, and an analysis of the influences of dominant machining parameters on thrust force, surface roughness and burr height in the drilling of hybrid metal matrix composites produced through stir casting route. Experiments are carried out using Al 356-aluminum alloy reinforced with silicon carbide of size 25 μm and Mica of size 45 μm. Drilling test is carried out using carbide drill of 6 mm diameter. The design of experiment concept has been used to optimize the experimental conditions. The experimental data are collected based on a three-factor-three-level full central composite design. The multiple regression analysis using RSM is used to establish the input–output relationships of the process. The mathematical models are developed and tested for adequacy using analysis of variance and other adequacy measures using the developed models. The main and interaction effect of the input variables on the predicted responses are investigated. The predicted values and measured values are fairly close, which indicate that the developed models can be effectively used to predict the responses in the drilling of hybrid metal matrix composites. The optimized drilling process parameters have been obtained by numerical optimization using RSM by ensuring the minimum thrust force of 84 N, surface roughness of 1.67 μm, and the burr height of 0.16 mm. After the drilling experiments, a scanning electron microscope (SEM) is used to investigate the machined surface and tool wear.     

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.     

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.     

Face Grinding Surface Quality of High Volume Fraction SiCp/Al Composite Materials

The existing research on SiCp/Al composite machining mainly focuses on the machining parameters or surface morphology.However,the surface quality of SiCp/Al composites with a high volume fraction has not been extensively studied.In this study,32 SiCp/Al specimens with a high volume fraction were prepared and their machining parame-ters measured.The surface quality of the specimens was then tested and the effect of the grinding parameters on the surface quality was analyzed.The grinding quality of the composite specimens was comprehensively analyzed taking the grinding force,friction coefficient,and roughness parameters as the evaluation standards.The best grinding parameters were obtained by analyzing the surface morphology.The results show that,a higher spindle speed should be chosen to obtain a better surface quality.The final surface quality is related to the friction coefficient,surface roughness,and fragmentation degree as well as the quantity and distribution of the defects.Lower feeding amount,lower grinding depth and appropriately higher spindle speed should be chosen to obtain better surface quality.Lower feeding amount,higher grinding depth and spindle speed should be chosen to balance grind efficiently and surface quality.This study proposes a systematic evaluation method,which can be used to guide the machining of SiCp/Al composites with a high volume fraction.     

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.     

Optimization of tool geometry parameters for turning operations based on the response surface methodology

This investigation focuses on the influence of tool geometry on the surface finish obtained in turning of AISI 1040 steel. In order to find out the effect of tool geometry parameters on the surface roughness during turning, response surface methodology (RSM) was used and a prediction model was developed related to average surface roughness (Ra) using experimental data. The results indicated that the tool nose radius was the dominant factor on the surface roughness. In addition, a good agreement between the predicted and measured surface roughness was observed. Therefore, the developed model can be effectively used to predict the surface roughness on the machining of AISI 1040 steel with in 95% confidence intervals ranges of parameters studied.     

Drilling of a hybrid Al/SiC/Gr metal matrix composites

The present study investigates the influence of cutting parameters on cutting force and surface roughness in drilling of Al/20%SiC/5%Gr and Al/20%SiC/10%Gr hybrid composites fabricated by vortex method. The drilling tests are conducted with diamond-like carbon-coated cutting tools. This paper is an attempt to understand the machining characteristics of the new hybrid metal matrix composites. The results indicate that inclusion of graphite as an additional reinforcement in Al/SiCp reinforced composite reduces the cutting force. The cutting speed and its interactions with feed rate are minimum. Feed rate is the main factor influencing the cutting force in both composites. The surface roughness value is proportional with the increase in feed rate while inversely proportional with cutting speed in both composites. For all cutting conditions, Al/20%SiC/10%Gr composite has lower surface roughness values than Al/20%SiC/5%Gr composite. The surface is analyzed using scanning electron microscope.     

基于RSM的SiC单晶片表面粗糙度预测及参数优化

通过响应面分析法(RSM)对超声振动辅助金刚石线锯切割SiC单晶体的工艺参数进行分析和优化。采用中心组合设计实验,考察线锯速度、工件进给速度、工件转速和超声波振幅这4个因素对SiC单晶片表面粗糙度值的影响,建立了SiC单晶片表面粗糙度的响应模型,进行响应面分析,采用满意度函数(DFM)确定了切割SiC单晶体的最佳工艺参数,验证试验表明该模型能实现相应的硬脆材料切割过程的表面粗糙度预测。     

OPTIMAL MQL AND CUTTING CONDITIONS DETERMINATION FOR DESIRED SURFACE ROUGHNESS IN TURNING OF BRASS USING GENETIC ALGORITHMS

The evolving concept of minimum quantity of lubrication (MQL) in machining is considered as one of the solutions to reduce the amount of lubricant to address the environmental, economical and ecological issues. This paper investigates the influence of cutting speed, feed rate and different amount of MQL on machining performance during turning of brass using K10 cemented carbide tool. The experiments have been planned as per Taguchi's orthogonal array and the second order surface roughness model in terms of machining parameters was developed using response surface methodology (RSM). The parametric analysis has been carried out to analyze the interaction effects of process parameters on surface roughness. The optimization is then carried out with genetic algorithms (GA) using surface roughness model for the selection of optimal MQL and cutting conditions. The GA program gives the minimum values of surface roughness and the corresponding optimal machining parameters.     

On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations

This research work concerns the elaboration of a surface roughness model in the case of hard turning by exploiting the response surface methodology (RSM). The main input parameters of this model are the cutting parameters such as cutting speed, feed rate, depth of cut and tool vibration in radial and in main cutting force directions. The machined material tested is the 42CrMo4 hardened steel by Al₂O₃/TiC mixed ceramic cutting tool under different conditions. The model is able to predict surface roughness of Ra and Rt using an experimental data when machining steels. The combined effects of cutting parameters and tool vibration on surface roughness were investigated while employing the analysis of variance (ANOVA). The quadratic model of RSM associated with response optimization technique and composite desirability was used to find optimum values of cutting parameters and tool vibration with respect to announced objectives which are the prediction of surface roughness. The adequacy of the model was verified when plotting the residuals values. The results indicate that the feed rate is the dominant factor affecting the surface roughness, whereas vibrations on both pre-cited directions have a low effect on it. Moreover, a good agreement was observed between the predicted and the experimental surface roughness. Optimal cutting condition and tool vibrations leading to the minimum surface roughness were highlighted.     

Drilling of hybrid aluminium matrix composites

This paper presents the influence of cutting parameters on thrust force, surface finish, and burr formation in drilling Al2219/15SiCp and Al2219/15SiCp-3Gr composites. The composites were fabricated using the liquid metallurgy method. The tools used were commercially available carbide and coated carbide drills. The results revealed that feed rate had a major influence on thrust force, surface roughness, and exit burr formation. Graphitic composites exhibit lesser thrust force, burr height, and higher surface roughness when compared to the other material. The reduced thrust force and burr height is attributed to the solid lubricating property of the graphite particles. The higher surface roughness value for Al2219/15SiCp-3Gr composite is due to the pullout of graphite from the surface. The chips formed when machining graphitic composites are more discontinuous when compared to SiCp reinforced composites and hence advantageous.     

SURFACE ROUGHNESS PREDICTION MODEL FOR ULTRAPRECISION TURNING ALUMINIUM ALLOY WITH A SINGLE CRYSTAL DIAMOND TOOL

A surface roughness model utilizing regression analysis method is developed for predicting roughness of ultra-precision machined surface with a single crystal diamond tool. The effects of the main variables, such as cutting speed, feed, and depth of cut on surface roughness are also analyzed in diamond turning aluminum alloy. In order to predict and control the surface roughness before ultraprecision machining, constrained variable metric method is used to select the optimum cutting conditions during process planning. A lot of experimental results show that the model can predict the surface roughness effectively under a certain cutting conditions .     

Modeling and analysis of performances in drilling hybrid metal matrix composites using D-optimal design

The present paper deals with the modeling and analysis of machining responses such as the thrust force, surface roughness, burr height, and tool wear in the drilling of hybrid metal matrix composites using carbide, coated carbide, and polycrystalline diamond drills. Experiments are conducted on Al 356 aluminum alloy reinforced with silicon carbide of size 25 μm and mica of size 45 μm. Machining parameters such as spindle speed, feed rate, and weight percent of silicon carbide are chosen as the numerical factors; the drill material is considered as the categorical factor. An experimental plan of a four-factor (three numerical plus one categorical) D-optimal design based on the response surface methodology is employed to carry out the experimental study. The results indicated that the predicted values through the developed model are well in agreement with the experimental results. The results also indicated that the method used is effectively applied for the modeling and analysis of drilling parameters in drilling hybrid metal matrix composites.     

Effects of tool overhang on selection of machining parameters and surface finish during diamond turning

In precision machining leading to nano-metric surface finish, selection of the suitable machining parameters is a critical task. To ensure the desired surface quality, one needs to optimally select the machining parametric matrix. Towards this effort, this paper adds another critical parameter in terms of tool overhang. A well-defined set of machining exercises is carried out with different tool overhangs and machining parameters. In this investigation, an attempt has been made to locate the optimum range of tool overhang with minimum tool vibrations. The interaction between tool overhang with other parameters is also thoroughly investigated. Another important focus of this study is to find out the optimum machining parameters for the situations where it is not possible to select an optimum tool overhang. One such situation occurs when a steep concave parabolic surface needs to be fabricated. In this case a large tool overhang has to be selected. Power spectral density distribution analysis of surface roughness for different tool overhangs is performed to find out significant parameters and their degree of contribution to surface roughness. Analysis of variance is also applied to ascertain statistically significant factors contributing to surface roughness. To model the surface roughness, response surface methodology is being used. The model has been verified by conducting a series of experiments and a steep concave parabolic surface is developed by following the predictions of the developed model.     

高体积分数SiCp/Al复合材料旋转超声铣磨加工的试验研究

由于大量高硬度增强相SiC颗粒的存在,高体积分数铝基碳化硅（SiCp/Al）复合材料的机械加工十分困难。旋转超声加工被认为是加工这种材料的有效方法。通过超声辅助划痕试验,分析高体积分数SiCp/Al复合材料旋转超声铣磨加工的材料去除机理。在超声振动的作用下,材料中铝基体发生塑性变形,其表面得到夯实;SiC增强相被锤击成细小的颗粒而发生脱落,形成较大的空洞。由于材料加工的缺陷大多产生于SiC颗粒的去除过程中,SiC颗粒的去除方式对加工表面的质量起着决定性的作用,选择合适的工艺参数可以有效提高加工表面质量。旋转超声加工工艺特征试验表明,超声振动可有效降低切削力;主轴转速对轴向切削力的影响最大,其次是进给速度,切削深度对轴向切削力的影响较小;另外主轴转速对表面质量的影响效果也最大,并随主轴转速的增大表面粗糙度增大。因此在加工过程中,可以适当加大切削深度,在保证加工质量的基础上,选择较大的进给速度,在保证刀具寿命的前提下,选择合适的主轴转速,以获得较优的加工表面质量和加工效率。     

Influence of graphite and machining parameters on the surface roughness of Al-fly ash/graphite hybrid composite: a Taguchi approach

This paper presents an experimental investigation on the surface roughness of pure commercial Al, Al-15 wt% fly ash, and Al-15 wt% fly ash/1.5 wt% graphite (Gr) composites produced by modified two-step stir casting. The effect of reinforcements and machining parameters such as cutting speed, feed rate, and depth of cut on surface roughness, which greatly influence the performance of the machined product, were analyzed during turning operation. The optimum machining parameters were found in minimizing the surface roughness of the materials by using the Taguchi and ANOVA approach. Results show that the presence of the fly ash particles reduces the surface roughness of composites compared with pure Al. The inclusion of 1.5 wt% Gr in the Al-fly ash composite reduces the surface roughness considerably. A scanning electron microscopy investigation was carried out on the machined surfaces of the tested materials. Confirmation tests were performed to validate the regression models.     

基于响应曲面法的表面粗糙度预测模型研究

通过正交试验,研究了高速端铣加工中切削参数对表面粗糙度的影响。采用田口设计方法和响应曲面法构建了表面粗糙度预测模型,分析了主轴转速、进给量、切深对表面粗糙度的影响。结果显示,进给量对表面粗糙度的影响最显著,主轴转速次之,切深的影响不大。模型预测精度为99．84％,达到了较高的预测水平。     

SiC_p/Al复合材料高速铣削工艺过程参量预测模糊专家系统研究

使用聚晶金刚石(PCD)刀具在600-1200m/min切削速度范围内对SiCp/2009Al复合材料进行高速铣削试验。对刀具耐用度、表面粗糙度、切削力、切削温度等工艺参量进行了测量。运用VC++及WXCLIPS软件开发了一套具有自学习功能的模糊专家系统,对SiCP/2009Al复合材料高速铣削加工中的上述工艺参量进行预测。经验证,预测结果与试验结果有很好的一致性。