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.     

An integrated study of surface roughness for modelling and optimization of cutting parameters during end milling operation

The aim of this study is to develop an integrated study of surface roughness to model and optimize the cutting parameters when end milling of AISI 1040 steel material with TiAlN solid carbide tools under wet condition. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental measurements and to show the effect of four cutting parameters on the surface roughness. Artificial neural network (ANN) based on Back-propagation (BP) learning algorithm is used to construct the surface roughness model exploiting a full factorial design of experiments. Genetic algorithm (GA) supported with the tested ANN is utilized to determine the best combinations of cutting parameters providing roughness to the lower surface through optimization process. GA improves the surface roughness value from 0.67 to 0.59 μm with approximately 12% gain. Then, machining time has also decreased from 1.282 to 1.0316 min by about 20% reduction based on the cutting parameters before and after optimization process using the analytical formulas. The final measurement experiment has been performed to verify surface roughness value resulted from GA with that of the material surface by 3.278% error. From these results, it can be easily realized that the developed study is reliable and suitable for solving the other problems encountered in metal cutting operations as the same as surface roughness.     

Intelligent optimization and selection of machining parameters in finish turning and facing of Inconel 718

The heat-resistant super alloy material like Inconel 718 machining is an inevitable and challenging task even in modern manufacturing processes. This paper describes the genetic algorithm coupled with artificial neural network (ANN) as an intelligent optimization technique for machining parameters optimization of Inconel 718. The machining experiments were conducted based on the design of experiments full-factorial type by varying the cutting speed, feed, and depth of cut as machining parameters against the responses of flank wear and surface roughness. The combined effects of cutting speed, feed, and depth of cut on the performance measures of surface roughness and flank wear were investigated by the analysis of variance. Using these experimental data, the mathematical model and ANN model were developed for constraints and fitness function evaluation in the intelligent optimization process. The optimization results were plotted as Pareto optimal front. Optimal machining parameters were obtained from the Pareto front graph. The confirmation experiments were conducted for the optimal machining parameters, and the betterment has been proved.     

Prediction and analysis of surface roughness characteristics of a non-ferrous material using ANN in CNC turning

Surface roughness, an indicator of surface quality is one of the most-specified customer requirements in a machining process. For efficient use of machine tools, optimum cutting parameters (speed, feed, and depth of cut) are required. So it is necessary to find a suitable optimization method which can find optimum values of cutting parameters for minimizing surface roughness. The turning process parameter optimization is highly constrained and non-linear. In this work, machining process has been carried out on brass C26000 material in dry cutting condition in a CNC turning machine and surface roughness has been measured using surface roughness tester. To predict the surface roughness, an artificial neural network (ANN) model has been designed through feed-forward back-propagation network using Matlab (2009a) software for the data obtained. Comparison of the experimental data and ANN results show that there is no significant difference and ANN has been used confidently. The results obtained conclude that ANN is reliable and accurate for predicting the values. The actual R _a value has been obtained as 1.1999???m and the corresponding predicted surface roughness value is 1.1859???m, which implies greater accuracy.     

The role of stepover ratio in prediction of surface roughness in flat end milling

Surface roughness prediction studies in end milling operations are usually based on three main parameters composed of cutting speed, feed rate and depth of cut. The stepover ratio is usually neglected without investigating it. The aim of this study is to discover the role of the stepover ratio in surface roughness prediction studies in flat end milling operations. In realising this, machining experiments are performed under various cutting conditions by using sample specimens. The surface roughnesses of these specimens are measured. Two ANN structures were constructed. First of them was arranged with considering, and the second without considering the stepover ratio. ANN structures were trained and tested by using the measured data for predicting the surface roughness. Average RMS error of the ANN model considering stepover ratio is 0.04 and without considering stepover ratio is 0.26. The first model proved capable of prediction of average surface roughness (Ra) with a good accuracy and the second model revealed remarkable deviations from the experimental values.     

A method to predict possibility of arcing in EDM of TiB2p reinforced ferrous matrix composite

Electrical Discharge Machining (EDM) is very popular for machining conductive metal matrix composites (MMCs) because the hardness rendered by the ceramic reinforcements to these composites causes very high tool wear and cutting forces in conventional machining processes. EDM requires selection of a number of parameters for desirable results. Inappropriate parameter selection can lead to high overcuts, tool wear, excessive roughness, and arcing during machining and adversely affect machining quality. Arcing leads to short circuit gap conditions resulting in large energy discharges and uncontrolled machining. Arcing is a detrimental phenomenon in EDM which causes spoiling of workpiece and tool electrode and tends to damage the power supply of EDM machine. Parameter combinations that lead to arcing during machining have to be identified and avoided for every tool, work material, and dielectric combination. Proper selection of parameter combinations to avoid arcing is essential in EDM. In the work, experiments were conducted using L27 design of experiment to determine the parameter settings which cause arcing in EDM machining of TiB₂p reinforced ferrous matrix composite. Important EDM process parameters were selected in roughing, intermediate, and finishing range so as to study the occurrence of arcing. Using the experimental data, an artificial neural network (ANN) model was developed as a tool to predict the possibility of arcing for selected parameter combinations. This model can help avoid the parameter combinations which can lead to arcing during actual machining using EDM. The ANN model was validated by conducting validation experiments to ensure that it can work accurately as a predicting tool to know beforehand whether the selected parameters will lead to arcing during actual machining using EDM. Validation results show that the ANN model developed can predict arcing possibility accurately when the depth of machining is included as input variable for the model.     

HAP/SiCw复合生物陶瓷材料的超声波加工

研究了用超声波加工技术对HAP/SiC复合生物陶瓷材料进行加工时晶须取向对加工机理、材料去除率和加工表面粗糙度的影响。研究结果表明材料去除率和加工表面粗糙度随晶须方向角的增大而增大。在相同的加工条件下 ,材料的断裂韧性越高 ,其MMR越小。该研究为HAP/SiCw复合生物陶瓷材料的超声波加工提供了工艺依据     

Optimization of surface roughness in end milling Castamide

Castamide is vulnerable to humidity up to 7%; therefore, it is important to know the effect of processing parameters on Castamide with and without humidity during machining. In this study, obtained quality of surface roughness of Castamide block samples prepared in wet and dry conditions, which is processed by using the same cutting parameters, were compared. Moreover, an artificial neural network (ANN) modeling technique was developed with the results obtained from the experiments. For the training of ANN model, material type, cutting speed, cutting rate, and depth of cutting parameters were used. In this way, average surface roughness values could be estimated without performing actual application for those values. Various experimental results for different material types with cutting parameters were evaluated by different ANN training algorithms. So, it aims to define the average surface roughness with minimum error by using the best reliable ANN training algorithm. Parameters as cutting speed (V _c), feed rate (f), diameter of cutting equipment, and depth of cut (a _p) have been used as the input layers; average surface roughness has been also used as output layer. For testing data, root mean squared error, the fraction of variance (R ²), and mean absolute percentage error were found to be 0.0681%, 0.9999%, and 0.1563%, respectively. With these results, we believe that the ANN can be used for prediction of average surface roughness.     

Modeling and Simulation of Cylindrical Electrochemical Magnetic Abrasive Machining Process

Cylindrical Electrochemical Magnetic Abrasive Machining (C-EMAM) is an advanced abrasion-based hybrid machining process that constitutes magnetic abrasive machining and electrochemical dissolution. During the C-EMAM process, a large amount of material is removed from the peaks of the surface irregularities under the simultaneous effect of electrochemical dissolution, abrasion and abrasion-passivation synergism. This article presents the mathematical modeling for material removal and surface roughness during the C-EMAM process. Magnetic potential distribution between the two magnetic poles in which a cylindrical workpiece was placed was calculated using the finite element method. It was further used to find the forces acting on the ferromagnetic particles at contact surfaces. An empirical relation has been also developed considering the effect of electrochemical dissolution and abrasion-passivation synergism based on experiments conducted on a self-developed C-EMAM setup. Finally, a surface roughness model was developed by considering the total volume of material removed with the assumption of a triangular surface profile. The simulated results for material removal and surface roughness were validated using self-conducted experimental results. The computed results were found to be in good agreement with experimental observations.     

Modeling of abrasive flow rotary machining process by artificial neural network

Abrasive flow machining (AFM) is one of the non-traditional machining processes applicable to finishing, deburring, rounding of edges, and removing defective layers from workpiece surface. Abrasive material, used as a mixture of a polymer with abrasive material powder, has reciprocal motion on workpiece surface under pressure during the process. In the following study, a new method of AFM process called henceforth abrasive flow rotary machining (AFRM) will be proposed, in which by elimination of reciprocal motion of abrasive material and the mere use of its stirring and rotation of workpiece, the amount of used material would be optimized. Furthermore, AFRM is executable by simpler tools and machines. In order to investigate performance of the method, experimental tests were designed by the Taguchi method. Then, the tests were carried out and the influence of candidate effective parameters was determined and modeled by artificial neural network (ANN) method. To evaluate the ANN results, they were compared with reported results of AFM. An agreement between our ANN results on predictions of AFRM material removal value and surface roughness was observed with AFM data. The results showed through AFRM, in addition to saving of abrasive material, surface finish is achievable same as AFM’s.     

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.     

Experimental investigation on thermally enhanced machining of high-chrome white cast iron and to study its machinability characteristics using Taguchi method and artificial neural network

Machining of hard-to-wear materials such as high-chrome white cast iron (HCWCI) and high-manganese steels is an uphill task when conventional route followed. Alternatively, thermally enhanced machining (TEM) can be used to minimize the tooling cost very effectively. This paper presents the detailed study of TEM of HCWCI in which the effect of cutting parameters and surface temperature of the stock material on machinability characteristics (cutting forces and surface roughness) are analyzed using ANOVA and artificial neural network (ANN). The experimental work was conducted to follow Taguchi techniques. HCWCI is finding newer applications in mining; mineral processing industries were the workpiece in the machining studies using cobalt-based cubic boron nitride insert tool. Localized heat was added at the tool-work interface which softens the metal and eases the machining operation. The influences of the control factors on the process responses have been analyzed using analysis of variance (ANOVA), and the results are correlated using ANN. Linear regression was used to establish the relation between the control parameters and the process responses. The results show that TEM causes easy shearing of the material, leading to the reduction in cutting forces with expected improvement in tool life and surprisingly good surface finish. The confirmation tests suggest both second-order regression and ANN which are better predictive models for quantitative prediction of TEM of HCWCI, and ANN is more accurate of the two. Also, it was proved that oxy-LPG flame heating is an economical option compared to laser-heated machining in hard turning process.     

One-dimensional multipulse laser machining of structural alumina: evolution of surface topography

The development and understanding of laser–material interactions have steered to the machining of advanced structural ceramics. At one point, it was nearly impossible to machine effectively using various conventional machining techniques. Nevertheless, achieving a higher material removal rate along with a good surface finish is a critical issue. In this study, a multistep computational model based on COMSOL? Multiphysics was designed and developed to study the influence of multiple laser pulses on the evolution of surface roughness of alumina. The computational model employed the various heat transfer and hydrodynamic boundary conditions and thermomechanical properties for better prediction of surface roughness under various laser processing conditions. The results indicate that, as the pulse rate increases, the surface roughness also increases. The results of the computational model are also validated by experimental observations with reasonably close agreement.     

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。     

多线砂轮复合自动修磨装置的设计与精度检验

多线砂轮复合自动修磨装置采用CNC数控系统,利用两个独立金刚石滚轮休整器,实现单支和多支砂轮的高精度修磨,极大地降低了人工操作带来的加工误差,提高了产品加工精度和效率。通过表面粗糙度检测数据可知,具有多线砂轮复合自动修磨装置的数控丝锥螺纹磨床完全符合加工精度要求。同时,采用多元回归方程建立基于砂轮修整参数的表面粗糙度预测模型,并设计单因素试验,得到砂轮修整参数与表面粗糙度之间的关系。由显著性分析结果得出,径向修整进给量是影响表面粗糙度的主要因素。     

50%SiC/Cu复合材料电火花加工研究

采用粉末冶金工艺制备了50%SiC/Cu复合材料.研究了电参数对线切割加工SiC颗粒增强Cu基复合材料的加工速度和表面质量的影响规律.用扫描电子显微镜分析了复合材料加工表面的形貌特征.结果表明,选用较大的峰值电流和较短的脉冲宽度,可对50%SiCp/Al复合材料进行较理想的线切割加工     

Investigation of surface roughness in turning unidirectional GFRP composites by using RS methodology and ANN

Fibre reinforced plastics (FRP) contain two phases of materials with drastically distinguished mechanical and thermal properties, which brings in complicated interactions between the matrix and the reinforcement during machining. Surface quality and dimensional precision will greatly affect parts during their useful life especially in cases where the components will be in contact with other elements or materials during their useful life. Therefore, their study and characterisation is extremely important and, above all, those cases subjected to adverse environmental conditions and in contact with other elements or materials. Thus, measuring and characterising surface properties represent one of the most important aspects in manufacturing processes. In this paper, orthogonal cutting tests were carried out on unidirectional glassfibre reinforced plastics (GFRP), using cermet tools. During the tests, the depth of cut (a), feedrate (f), cutting speed (Vc) were varied, whereas the cutting direction was held parallel to the fibre orientation. Turning experiments were designed based on statistical three level full factorial experimental design technique. An artificial neural network (ANN) and response surface (RS) model were developed to predict surface roughness on the turned part surface. In the development of predictive models, cutting parameters of cutting speed, depth of cut and feed rate were considered as model variables. The required data for predictive models are obtained by conducting a series of turning test and measuring the surface roughness data. Good agreement is observed between the predictive models results and the experimental measurements. The ANN and RSM models for GFRPs turned part surfaces are compared with each other for accuracy and computational cost.     

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.     

A feasibility approach by simulated annealing on optimization of micro-wire electric discharge machining parameters

Due to the presence of large number of process variables and complicated stochastic nature, selection of optimum machining parameter combinations for obtaining higher material removal rate with minimum overcut and surface roughness is a challenging task in Micro Wire Electric Discharge Machining (μ-WEDM). The important parameters of Material Removal Rate (MRR), overcut and surface roughness are considered in this study of single pass μ-WEDM machining of aluminium. The system model is created with statistical based regression analysis using experimental data. This system model is employed to maximize the material removal rate and minimize the surface roughness and overcut using Simulated Annealing (SA) scheme. Finally consistency of the method is tested with trial values. The model is found as capable of predicting the response characteristics as a function of different control variables. Experiments are carried out to check the validity of the developed model and then optimal parametric combinations are searched out using an advanced optimization strategy.     

Performance prediction of high-pressure coolant assisted turning of Ti-6Al-4V

The present study focuses on the development of predictive models of average surface roughness, chip-tool interface temperature, chip reduction coefficient, and average tool flank wear in turning of Ti-6Al-4V alloy. The cutting speed, feed rate, cutting conditions (dry and high-pressure coolant), and turning forces (cutting force and feed force) were the input variables in modeling the first three quality parameters, while in modeling tool wear, the machining time was the only variable. Notably, the machining environment influences the machining performance; yet, very few models exist wherein this variable was considered as input. Herein, soft computing-based modeling techniques such as artificial neural network (ANN) and support vector machines (SVM) were explored for roughness, temperature, and chip coefficient. The prediction capability of the formulated models was compared based on the lowest mean absolute percentage error. For surface roughness and cutting temperature, the ANN and, for chip reduction coefficient, the SVM revealed the lowest error, hence recommended. In addition, empirical models were constructed by using the experimental data of tool wear. The adequacy and good fit of tool wear models were justified by a coefficient of determination value greater than 0.99.