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.     

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.     

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.     

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.     

Soft computing models based prediction of cutting speed and surface roughness in wire electro-discharge machining of tungsten carbide cobalt composite

This paper reports on an experimental investigation of small deep hole drilling of Inconel 718 using the EDM process. The parameters such as peak current, pulse on-time, duty factor and electrode speed were chosen to study the machining characteristics. An electrolytic copper tube of 3 mm diameter was selected as a tool electrode. The experiments were planned using central composite design (CCD) procedure. The output responses measured were material removal rate (MRR) and depth averaged surface roughness (DASR). Mathematical models were derived for the above responses using response surface methodology (RSM). The results revealed that MRR is more influenced by peak current, duty factor and electrode rotation, whereas DASR is strongly influenced by peak current and pulse on-time. Finally, the parameters were optimized for maximum MRR with the desired surface roughness value using desirability function approach.     

Modeling and optimization of burr height in drilling of Al-7075 using Taguchi method and response surface methodology

This investigation presents the use of Taguchi and response surface methodologies for minimizing the burr height and the surface roughness in drilling Al-7075. The Taguchi method, a powerful tool to design optimization for quality, is used to find optimal cutting parameters. Response surface methodology is useful for modeling and analyzing engineering problems. The purpose of this paper was to investigate the influence of cutting parameters, such as cutting speed and feed rate, and point angle on burr height and surface roughness produced when drilling Al-7075. A plan of experiments, based on L₂₇ Taguchi design method, was performed drilling with cutting parameters in Al-7075. All tests were run without coolant at cutting speeds of 4, 12, and 20 m/min and feed rates of 0.1, 0.2, and 0.3 mm/rev and point angle of 90^°, 118°, and 135°. The orthogonal array, signal-to-noise ratio, and analysis of variance (ANOVA) were employed to investigate the optimal drilling parameters of Al-7075. From the analysis of means and ANOVA, the optimal combination levels and the significant drilling parameters on burr height and surface roughness were obtained. The optimization results showed that the combination of low cutting speed, low feed rate, and high point angle is necessary to minimize burr height. The best results of the surface roughness were obtained at lower cutting speed and feed rates while at higher point angle. The predicted values and measured values are quite close to each other; therefore, this result indicates that the developed models can be effectively used to predict the burr height and the surface roughness on drilling of Al-7075.     

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.     

Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression

In this study, models for predicting the surface roughness of AISI 1040 steel material using artificial neural networks (ANN) and multiple regression (MRM) are developed. The models are optimized using cutting parameters as input and corresponding surface roughness values as output. Cutting parameters considered in this study include cutting speed, feed rate, depth of cut, and nose radius. Surface roughness is characterized by the mean (R _a) and total (R _t) of the recorded roughness values at different locations on the surface. A total of 81 different experiments were performed, each with a different setting of the cutting parameters, and the corresponding R _a and R _t values for each case are measured. Input–output pairs obtained through these 81 experiments are used to train an ANN is achieved at the 200,00th epoch. Mean squared error of 0.002917120% achieved using the developed ANN outperforms error rates reported in earlier studies and can also be considered admissible for real-time deployment of the developed ANN algorithm for robust prediction of the surface roughness in industrial settings.     

Surface quality evaluation in ultrasonic drilling through the Taguchi technique

Ultrasonic drilling of commercially pure titanium and titanium alloy (Ti-6Al-4v) was investigated in this study. During the experiments, process parameters such as work piece, grit size, slurry concentration, power rating and tools were changed to explore their effect on the surface roughness. Taguchi’s technique was applied to obtain an optimal setting of ultrasonic drilling (USD) process parameters. Average surface roughness (Ra) was measured by using the Optical Profiling System. Two-dimensional and three-dimensional contour plots were obtained from the profiling system to quantify and visualize the surface roughness. From the experimental results and further analysis, it is concluded that the effect of slurry concentration and grit size have a significant effect on surface roughness more than other parameters. In addition, the surface roughness is apparently similar in two and three dimensions as visualized from contour plots. Ultrasonic drilling is established as a material removal process with good surface quality.     

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.     

Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid technique

The effective study of hybrid machining processes (HMPs), in terms of modeling and optimization has always been a challenge to the researchers. The combined approach of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) has attracted attention of researchers for modeling and optimization of the complex machining processes. In this paper, a hybrid machining process of Electrical Discharge Face Grinding (EDFG) and Diamond Face Grinding (DFG) named as Electrical Discharge Diamond face Grinding (EDDFG) have been studied using a hybrid methodology of ANN-NSGA-II. In this study, ANN has been used for modeling while NSGA-II is used to optimize the control parameters of the EDDFG process. For observations of input-output relations, the experiments were conducted on a self developed face grinding setup, which is attached with the ram of EDM machine. During experimentation, the wheel speed, pulse current, pulse on-time and duty factor are taken as input parameters while output parameters are material removal rate (MRR) and average surface roughness (R_a). The results have shown that the developed ANN model is capable to predict the output responses within the acceptable limit for a given set of input parameters. It has also been found that hybrid approach of ANN-NSGA-II gives a set of optimal solutions for getting appropriate value of outputs with multiple objectives.     

Investigations into high-speed rough and finish end-milling of hardened EN24 steel for implementation of control strategies

High-speed end-milling is used for production of variety of parts, dies, and molds made of hardened EN24 steel which are widely used in power and transport industries. Since desired productivity and quality are important in these industries, different strategies are needed for rough and finish end-milling operations. In this paper, a framework is presented for integrating different requirements of high-speed end-milling. In flat end-milling experiments, slots are machined in hardened EN24 steel using single insert cutter under different sets of cutting parameters for roughing and finishing operations. For rough end-milling, the responses such as material removal volume, tool wear and cutting forces are measured with respect to cutting time. A response surface is developed to predict material removal volume and a set of cutting parameters is selected for a given range of material removal volume using differential evolution (DE) algorithm till the tool wear reaches certain value. The experimental data is also used to develop Bayesian-based artificial neural network (ANN) model. Using this ANN model, reference values for cutting force and cutting time are generated for rough end-milling. Similarly, DE is used to predict a set of cutting parameters for a given range of surface roughness using response surface model. The reference cutting force is obtained for finish end-milling using ANN model. These reference values are useful in the monitoring and implementation of control strategy for the high-speed end-milling operations.     

基于不同磨料的氮化硅陶瓷球精研工艺研究

为探究磨料对氮化硅陶瓷球精研加工的影响,从而提高氮化硅陶瓷球的表面质量和材料去除率,以基液种类、磨料种类和研磨盘转速为主要影响因素设计正交试验,并分析各因素对表面粗糙度Ra的影响程度。以表面粗糙度Ra和材料去除率为评价指标,通过单因素试验优化研磨参数。根据正交试验结果,得到精研加工过程中各影响因素对于表面粗糙度Ra的影响程度,从大到小排列依次为:磨料种类>基液种类>研磨盘转速。综合考虑陶瓷球精研加工的要求,确定最佳的研磨参数组合为:煤油基液、碳化硅磨料以及150 r/min的研磨盘转速。在金刚石、碳化硅、氮化硼、氧化铬和氧化铁这5种磨料中,氧化铁磨料修复粗研过后的氮化硅陶瓷球表面缺陷的效果最好。     

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.     

Grinding-aided electrochemical discharge machining of particulate reinforced metal matrix composites

A grinding-aided electrochemical discharge machining (G-ECDM) process has been developed to improve the performance of the conventional ECDM process in machining particulate reinforced metal matrix composites (MMCs). The G-ECDM process functions under a combined action of electrochemical dissolution, spark erosion, and direct mechanical grinding. The tool electrode has a coating containing a hard reinforcement phase of diamond particles. The MMC employed in this study was Al₂O₃ particulate reinforced aluminum 6061 alloy. The material removal mechanism of this hybrid process has been analyzed. The results showed that the grinding action can effectively remove re-cast material deposited on the machining surface. The surface roughness (R _a) measured for the G-ECDM specimen was ten times smaller than that of the specimen machined without grinding aid (i.e., ECDM alone). Moreover, the material removal rate (MRR) of G-ECDM was about three times higher than that of ECDM under the experimental conditions of this study. The voltage waveform and crater distribution were also analyzed, and the experimental results showed that the G-ECDM process operates in a stable condition. The relative importance of the various processing parameters on MRR was established using orthogonal analysis. The results showed that MRR is influenced by the machining parameters in the order of duty cycle?>?current?>?electrolyte concentration. This study showed that the G-ECDM process is superior to the ECDM process for machining particulate reinforced MMCs, where a higher machining efficiency and a better surface quality can be obtained.     

Surface roughness and material removal rate of lapping process on ceramics

Lapping is a widely used surface finishing process for ceramics. An experimental investigation is conducted into the lapping of alumina, Ni−Zn ferrite and sodium silicate glass using SiC abrasive to study the effect of process parameters, such as abrasive particle size, lapping pressure, and abrasive concentration, on the surface roughness and material removal rate during lapping. A simple model is developed based on the indentation fracture and abrasive particle distribution in the slurry to explain various aspects of the lapping process. The model provides predictions for the surface roughness,R _a andR _t , on the machined surface and rough estimation for the material removal rate during lapping. Comparison of the predictions with the experimental measurements reveals same order of magnitude accuracy.     

Single and multiobjective optimization of Inconel 718 nickel-based superalloy in the wire electrical discharge machining

Inconel 718 is widely used in high-temperature environments, high-performance aircraft, and hypersonic missile weapon systems; however, it is very difficult to machine using conventional techniques. This study employed an L9 Taguchi orthogonal array for the analysis of wire electrical discharge machining parameters when used for the machining of Inconel 718. Our aim was to determine the optimal combination of parameters to minimize surface roughness while maximizing the material removal rate. The Taguchi method is widely applied in mechanical engineering with the aim of identifying the optimal combination of processing parameters as they pertain to single quality characteristics. Unfortunately, Taguchi analysis often leads to contradictory results when seeking to rectify multiple objectives. To resolve this issue, this study implemented gray relational analysis in conjunction with Taguchi method to obtain the optimal combination of parameters to deal specifically with multiple quality objectives. For the dual objectives of surface roughness and material removal rate, the optimal combination of parameters derived using gray relational analysis resulted in a mean surface roughness of 2.75 μm. In L9 orthogonal array experiments, run 1 produced the best gray relational grade with mean surface roughness of 2.80 μm, representing an improvement of 1.8%. The material removal rate achieved after the application of gray relational analysis was 0.00190 g/s, whereas the L9 experiment achieved a material removal rate of 0.00123 g/s, representing an improvement of 54.5%.     

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.     

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.     

Taguchi analysis of drilling quality associated with core drill in drilling of composite material

The thrust force and surface roughness of core drill with drill parameters (grit size of diamond, thickness, feed rate and spindle speed) in drilling carbon fiber reinforced plastic (CFRP) laminate was experimentally investigated in this study. A L₂₇ (3¹³) orthogonal array and signal-to-noise (S/N) were employed to analyze the effect of drill parameters. Using Taguchi method for design of a robust experiment, the interactions among factors are also investigated. The experimental results indicate that thickness and feed rate are recognized to make the most significant contribution to the overall performance. For thrust force, the drilling conditions were A₁B₁C₁D₂, (i.e., grit size of diamond=#60, thickness=1.0 mm, feed rate=0.012 mm/rev, and spindle speed=950 rpm). For surface roughness, the drilling conditions were A₁B₁C₁D₃, (i.e., grit size of diamond=#60, thickness=1.0 mm, feed rate=0.012 mm/rev, and spindle speed=1150 rpm). The correlation was obtained by multi-variable nonlinear regression and compared with the experimental results. The confirmation tests demonstrated a feasible and an effective method for the evaluation of drilling-induced thrust force and surface roughness (errors within 10%) in drilling of composite material.