Development of an intelligent process model for EDM

This paper reports the development of an intelligent model for the electric discharge machining (EDM) process using finite-element method (FEM) and artificial neural network (ANN). A two-dimensional axisymmetric thermal (FEM) model of single-spark EDM process has been developed based on more realistic assumptions such as Gaussian distribution of heat flux, time- and energy-dependent spark radius, etc. to predict the shape of crater cavity, material removal rate, and tool wear rate. The model is validated using the reported analytical and experimental results. A neural-network-based process model is proposed to establish relation between input process conditions (discharge power, spark on time, and duty factor) and the process responses (crater geometry, material removal rate, and tool wear rate) for various work—tool work materials. The ANN model was trained, tested, and tuned using the data generated from the numerical (FEM) simulations. The ANN model was found to accurately predict EDM process responses for chosen process conditions. It can be used for the selection of optimum process conditions for EDM process.     

The effect of machining parameters on tool electrode edge wear and machining performance in Electric Discharge Machining (EDM)

The main purpose of this study is to investigate the variation of tool electrode edge wear and machining performance outputs, namely, the machining rate (workpiece removal rate), tool wear rate and the relative wear, with the varying machining parameters (pulse time, discharge current and dielectric flushing pressure) in EDM die sinking. The edge wear profiles obtained are modeled by using the circular arcs, exponential and power functions. The variation of radii of the circular arcs with machining parameters is given. It is observed that the exponential function models the edge wear profiles of the electrodes very accurately. The variation of exponential model parameters with machining parameters is presented.     

Prediction of quality responses in micro-EDM process using an adaptive neuro-fuzzy inference system (ANFIS) model

In the present trend of technological development, micro-machining is gaining popularity in the miniaturization of industrial products. In this work, a hybrid process of micro-wire electrical discharge grinding and micro-electrical discharge machining (EDM) is used in order to minimize inaccuracies due to clamping and damage during transfer of electrodes. The adaptive neuro-fuzzy inference system (ANFIS) and back propagation (BP)-based artificial neural network (ANN) models have been developed for the prediction of multiple quality responses in micro-EDM operations. Feed rate, capacitance, gap voltage, and threshold values were taken as the input parameters and metal removal rate, surface roughness and tool wear ratio as the output parameters. The results obtained from the ANFIS and the BP-based ANN models were compared with observed values. It is found that the predicted values of the responses are in good agreement with the experimental values and it is also observed that the ANFIS model outperforms BP-based ANN.     

Multi-objective optimization of electric-discharge machining process using controlled elitist NSGA-II

Parametric optimization of electric discharge machining (EDM) process is a multi-objective optimization task. In general, no single combination of input parameters can provide the best cutting speed and the best surface finish simultaneously. Genetic algorithm has been proven as one of the most popular multi-objective optimization techniques for the parametric optimization of EDM process. In this work, controlled elitist non-dominated sorting genetic algorithm has been used to optimize the process. Experiments have been carried out on die-sinking EDM by taking Inconel 718 as work piece and copper as tool electrode. Artificial neural network (ANN) with back propagation algorithm has been used to model EDM process. ANN has been trained with the experimental data set. Controlled elitist non-dominated sorting genetic algorithm has been employed in the trained network and a set of pareto-optimal solutions is obtained.

     

Effect of ultrasonic vibration of tool on electrical discharge machining of cemented tungsten carbide (WC-Co)

This paper deals with the effect of copper tool vibration with ultrasonic (US) frequency on the electrical discharge machining (EDM) characteristics of cemented tungsten carbide (WC-Co). It was found that ultrasonic vibration of the tool (USVT) was more effective in attaining a high material removal rate (MRR) when working under low discharge currents and low pulse times (finishing regimes). In general, the surface roughness and the tool wear ratio (TWR) were increased when ultrasonic vibration was employed. It was observed that application of ultrasonic vibration significantly reduced arcing and open circuit pulses, and the stability of the process had a remarkable improvement. This study showed that, there were optimum conditions for ultrasonic assisted machining of cemented tungsten carbide, although the conditions may vary by giving other input parameters for those which had been set constant in the present work.     

Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling

Hard turning with ceramic tools provides an alternative to grinding operation in machining high precision and hardened components. But, the main concerns are the cost of expensive tool materials and the effect of the process on machinability. The poor selection of cutting conditions may lead to excessive tool wear and increased surface roughness of workpiece. Hence, there is a need to investigate the effects of process parameters on machinability characteristics in hard turning. In this work, the influence of cutting speed, feed rate, and machining time on machinability aspects such as specific cutting force, surface roughness, and tool wear in AISI D2 cold work tool steel hard turning with three different ceramic inserts, namely, CC650, CC650WG, and GC6050WH has been studied. A multilayer feed-forward artificial neural network (ANN), trained using error back-propagation training algorithm has been employed for predicting the machinability. The input?Coutput patterns required for ANN training and testing are obtained from the turning experiments planned through full factorial design. The simulation results demonstrate the effectiveness of ANN models to analyze the effects of cutting conditions as well as to study the performance of conventional and wiper ceramic inserts on machinability.     

Prediction of tool wear using regression and ANN models in end-milling operation

Tool wear prediction plays an important role in industry for higher productivity and product quality. Flank wear of cutting tools is often selected as the tool life criterion as it determines the diametric accuracy of machining, its stability and reliability. This paper focuses on two different models, namely, regression mathematical and artificial neural network (ANN) models for predicting tool wear. In the present work, flank wear is taken as the response (output) variable measured during milling, while cutting speed, feed and depth of cut are taken as input parameters. The Design of Experiments (DOE) technique is developed for three factors at five levels to conduct experiments. Experiments have been conducted for measuring tool wear based on the DOE technique in a universal milling machine on AISI 1020 steel using a carbide cutter. The experimental values are used in Six Sigma software for finding the coefficients to develop the regression model. The experimentally measured values are also used to train the feed forward back propagation artificial neural network (ANN) for prediction of tool wear. Predicted values of response by both models, i.e. regression and ANN are compared with the experimental values. The predictive neural network model was found to be capable of better predictions of tool flank wear within the trained range.     

一种在线监测铣刀磨损量的新方法

提出了一种在线监测铣刀磨损量的新方法,该方法利用B样条神经网络建立不同刀具磨损状态下加工参数与切削力之间的映射关系。通过比较实时采集的切削力与不同刀具磨损值对应的切削力大小,可确定刀具的磨损状态,并利用建立的简化模型计算刀具的精确磨损值。试验结果表明,该方法消除了加工参数变化对特征的影响,简化了特征选取的方法,能够适应外部加工环境的变化,完全满足刀具状态监测系统的实用化需求。     

Machine learning induction of a model for online parameter selection in EDM rough machining

In electrical discharge machining (EDM), appropriate average current in the gap has to be selected for the given machining surface in order to obtain the highest material removal rate at low electrode wear. Thus, rough machining parameters have to be selected according to the machining surface. In the case of sculptured features, the machining surface varies with the depth of machining. Hence, the machining parameters have to be selected on-line to obtain appropriate current density in the gap. In this paper, inductive machine learning is used to derive a model based on the voltage and current in the gap. The sufficient inputs to the model are only two discharge attributes extracted from the voltage signal in the gap. The model successfully selects between two machining parameter settings that obtain different average surface current in the gap. It requires only voltage signal acquisition during the machining process and a simple algorithm that is easy to implement on industrial machines.     

Analysis of the machining characteristics of EDM as functions of the mobilities of electrons and ions

Electrical discharge machining (EDM) is a process that can be used effectively to machine conductive metals regardless of their hardness. In the EDM process, material removal occurs because of the thermal energy of the plasma channel between the electrode and the workpiece. During EDM, the electrode as well as the workpiece is abraded by the thermal energy. Tool wear adversely affects the machining accuracy and increases tooling costs. Many previous studies have focused on mitigating the problems of tool wear by investigating various EDM parameters. In this study, the tool wear problem was investigated on the basis of the mobilities of electrons and ions in the plasma channel. The material removal volumes of both the electrode and the workpiece were compared as functions of the gap voltage. The material removal difference according to the capacitance was also investigated. The tool wear ratio was calculated under different EDM condition and an EDM conditions for reducing the tool wear ratio was suggested.     

Wear of rotary carbide tools in machining of Al/SiCp composites

With the projected widespread application of Metal Matrix Composites, it is necessary to develop an appropriate technology for their efficient and cost-effective machining. This paper deals with the study of feasibility of rotary carbide tools in the intermittent machining of Al/SiCp composites. A rotary tool holder was designed and fabricated for this work. Experiments were designed using Taguchi Methods to analyse the influence of various factors and their interactions on the flank wear of rotary carbide tools during machining. A tool-life model describing the effect of process, tool and material dependent parameter on the magnitude of flank wear of a rotary carbide tool is proposed.     

Review of research work in sinking EDM and WEDM on metal matrix composite materials

Metal matrix composites (MMCs) are newly advanced materials having the properties of light weight, high specific strength, good wear resistance and a low thermal expansion coefficient. These materials are extensively used in industry. Greater hardness and reinforcement makes it difficult to machine using traditional techniques, which has impeded the development of MMCs. The use of traditional machinery to machine hard composite materials causes serious tool wear due to the abrasive nature of reinforcement. These materials can be machined by many non-traditional methods like water jet and laser cutting but these processes are limited to linear cutting only. Electrical discharge machining (EDM) shows higher capability for cutting complex shapes with high precision for these materials. The paper presents a review of EDM process and year wise research work done in EDM on MMCs. The paper also discusses the future trend of research work in the same area.     

Optimization of machining parameters in rotary EDM process by using the Taguchi method

Electrical discharge machining (EDM) is one of the advanced methods of machining. Most publications on the EDM process are directed towards non-rotational tools. But rotation of the tool provides a good flushing in the machining zone. In this study, the optimal setting of the process parameters on rotary EDM was determined. A total of three variables of peak current, pulse on time, and rotational speed of the tool with three types of electrode were considered as machining parameters. Then some experiments have been performed by using Taguchi's method to evaluate the effects of input parameters on material removal rate, electrode wear rate, surface roughness, and overcut. Moreover, the optimal setting of the parameters was determined through experiments planned, conducted, and analyzed using the Taguchi method. Results indicate that the model has an acceptable performance to optimize the rotary EDM process.     

Simultaneous optimisation of multiple performance characteristics in micro-EDM drilling of titanium alloy

Micro-electrical discharge machining (micro-EDM) has become a widely accepted non-traditional material removal process for machining conductive and difficult-to-cut materials effectively and economically. Being a difficult-to-cut material, titanium alloy suffers poor machinability for most cutting processes, especially the drilling of micro-holes using traditional machining methods. Although EDM is suitable for machining titanium alloys, selection of machining parameters for higher machining rate and accuracy is a challenging task in machining micro-holes. In this study, an attempt has been made for simultaneous optimization of the process performances like, metal removal rate, tool wear rate and overcut based on Taguchi methodology. Thus, the optimal micro-EDM process parameter settings have been found out for a set of desired performances. The process parameters considered in the study were pulse-on time, frequency, voltage and current while tungsten carbide electrode was used as a tool. Verification experiments have been carried out and the results have been provided to illustrate the effectiveness of this approach.     

An empirical survey on the influence of machining parameters on tool wear in diamond turning of large single-crystal silicon optics

We conducted a series of screening experiments to survey the influence of machining parameters on tool wear during ductile regime diamond turning of large single-crystal silicon optics. The machining parameters under investigation were depth-of-cut, feed rate, surface cutting speed, tool radius, tool rake angle and side rake angle, and cutting fluid. Using an experimental design technique, we selected twenty-two screening experiments. For each experiment we measured tool wear by tracing the tool edge with an air bearing linear variable differential transformer before and after cutting and recording the amount of tool edge recession. Using statistical tools, we determined the significance of each cutting parameter within the parameter space investigated. We found that track length, chip size, tool rake angle and surface cutting speed significantly affect tool wear, while cutting fluid and side rake angle do not significantly affect tool wear within the ranges tested. The track length, or machining distance, is the single most influential characteristic that causes tool wear. For a fixed part area, a decrease in track length corresponds to an increase in feed rate. Less tool wear occurred on experiments with negative rake angle tools, larger chip sizes and higher surface velocities. The next step in this research is to perform more experiments in this region to develop a predictive model that can be used to select cutting parameters that minimize tool wear.     

Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals

Micro scale machining process monitoring is one of the key issues in highly precision manufacturing. Monitoring of machining operation not only reduces the need of expert operators but also reduces the chances of unexpected tool breakage which may damage the work piece. In the present study, the tool wear of the micro drill and thrust force have been studied during the peck drilling operation of AISI P20 tool steel workpiece. Variations of tool wear with drilled hole number at different cutting conditions were investigated. Similarly, the variations of thrust force during different steps of peck drilling were investigated with the increasing number of holes at different feed and cutting speed values. Artificial neural network (ANN) model was developed to fuse thrust force, cutting speed, spindle speed and feed parameters to predict the drilled hole number. It has been shown that the error of hole number prediction using a neural network model is less than that using a regression model. The prediction of drilled hole number for new test data using ANN model is also in good agreement to experimentally obtained drilled hole number.     

Influence of reinforcement particles on the mechanism of the blasting erosion arc machining of SiC/Al composites

Electrical arc machining has shown its remarkable efficiency in processing difficult-to-cut materials, especially high-temperature alloys and metal-based composites. Despite several studies about the material removal mechanisms of the electrical arc machining of metal alloys, few of these reports relate to the mechanism of machining composites with electrical arcing. Considering that reinforcements such as SiC particles have different thermal and electrical properties with metal alloys, research on the influence of SiC reinforcement on the electrical arc machining process is important and necessary. Based on comparison experiments using 20 and 50 vol.% SiC/Al composites, this research focused on the influence of SiC particles on the machining performance and material removal mechanism of blasting erosion arc machining (BEAM), and further analyzed the influence of reinforcements on composite material removal mechanisms. Analysis revealed that the molten material expelling mechanism is also influenced by the SiC fraction difference. For the BEAM of lower SiC fraction composites, both the SiC particles and the molten aluminum are mainly pumped and ejected by the flushing dielectric. In greater SiC fraction composites, most SiC particles are directly sublimed by heat. In addition, the mechanism of BEAM in the material removal and tool wear of SiC/Al composites was discussed based on heat transfer simulation and observation. Furthermore, the results disclosed that many chemical reactions take place during machining that have an obvious influence on the tool wear rate.     

Modelling and experimental investigation of process parameters in EDM of Si<Subscript>3</Subscript>N<Subscript>4</Subscript>-TiN composites using GRA-RSM

Electric discharge machining (EDM) is a highly promising machining process of ceramics. This research is an out of the paradigm investigation of EDM on Si₃N₄-TiN with Copper electrode. Ceramics are used for extrusion dies and bearing balls and they are more efficient, effective and even have longer life than conventional metal alloys. Owing to high hardness of ceramic composites, they are almost impossible to be machined by conventional machining as it entirely depends on relative hardness of tool with work piece. Whereas EDM offers easy machinability combined with exceptional surface finish. Input parameters of paramount significance such as current (I), pulse on (P_on) and off time (P_off), Dielectric pressure (DP) and gap voltage (SV) are studied using L₂₅ orthogonal array. With help of mean effective plots the relationship of output parameters like Material removal rate (MRR), Tool wear rate (TWR), Surface roughness (Ra), Radial overcut (ROC), Taper angle (α), Circularity (CIR), Cylindricity (CYL) and Perpendicularity (PER) with the considered input parameters and their individual influence were investigated. The significant machining parameters were obtained by Analysis of variance (ANOVA) based on Grey relational analysis (GRA) and value of regression coefficient was determined for each model. The results were further evaluated by using confirmatory experiment which illustrated that spark eroding process could effectively be improved.     

A study on electrode wear sensing and compensation in Micro-EDM using machine vision system

This paper presents an electrode wear compensation method based on a machine vision system for micro-electro-discharge machining (EDM). Front wear and corner wear of tool electrode can be measured and evaluated in a direct manner by the vision system’s image-processing software capabilities. Tool electrode images have depicted that the front wear and corner wear were increased rapidly during EDM drilling and EDM milling, respectively, and thus contributing to an arc and a tapering shape at the end of tool electrode, respectively. Both the depth of the hole and depth of the groove are linearly proportional to the length of the front wear. A new electrode wear compensation method is presented based on the direct measurement of the front wear. Experimental results not only verify the usefulness of the electrode wear compensation method in micro-EDM, they also demonstrate that the machining time can be significantly reduced by 40% when using the proposed method, compared to the uniform wear method.     

4Cr5MoVSi电火花加工试验特征规律研究

采用紫铜工具电极,在峰值电流为4-24A、脉冲宽度为25-200μs、加工电压为80-200V的电参数范围内,综合应用因子试验和正交试验方法,对难加工材料4Cr5MoVSi进行了电火花加工试验。在进行电加工基础特征规律分析的基础上,考查了电参数对加工速度、双边侧面放电间隙、电极损耗的影响,并对电火花加工机理进行了分析。研究结果表明:采用紫铜电极电火花加工4Cr5MoVSi,在本试验范围内,峰值电流Ip与脉冲宽度ti、加工电压U、脉冲间隔t0存在一定的交互作用;与其它三个因素相比,峰值电流对加工速度、电极损耗、双边侧面放电间隙的影响更显著;随脉冲宽度和加工电压的增大,电极损耗逐渐减小。