Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method

Wire electrical discharge machining (WEDM) is extensively used in machining of conductive materials when precision is of prime importance. Rough cutting operation in WEDM is treated as a challenging one because improvement of more than one machining performance measures viz. metal removal rate (MRR), surface finish (SF) and cutting width (kerf) are sought to obtain a precision work. Using Taguchi’s parameter design, significant machining parameters affecting the performance measures are identified as discharge current, pulse duration, pulse frequency, wire speed, wire tension, and dielectric flow. It has been observed that a combination of factors for optimization of each performance measure is different. In this study, the relationship between control factors and responses like MRR, SF and kerf are established by means of nonlinear regression analysis, resulting in a valid mathematical model. Finally, genetic algorithm, a popular evolutionary approach, is employed to optimize the wire electrical discharge machining process with multiple objectives. The study demonstrates that the WEDM process parameters can be adjusted to achieve better metal removal rate, surface finish and cutting width simultaneously.     

Robust parameter design and multi-objective optimization of laser beam cutting for aluminium alloy sheet

The application of laser beam for precise cutting of sheet metals, in general, and reflective sheet metals, like aluminium, in particular, has become of interest in the recent past. The optimum choice of the cutting parameters is essential for the economic and efficient cutting of difficult to cut materials with laser beams. In this paper, a robust design and quality optimization tool called the Taguchi methodology has been applied to find the optimal cutting parameters for cutting of a reflective sheet made of aluminium alloy with a Nd:YAG laser beam. All the steps of the Taguchi method, such as a selection of orthogonal array, computation of signal-to-noise ratio, decision of optimum setting of parameters, and the analysis of variance (ANOVA), have been done by a self-developed software called computer aided robust parameter design (CARPD). A considerable improvement in the kerf taper (KT) and material removal rate (MRR) has been found by using Taguchi method-based predicted results. Confirmatory experimental results have shown good agreement with predicted results. Further, the Taguchi quality loss function has also been used for multi-objective optimization of laser beam cutting of Al-alloy sheet. The results of multi-objective optimization are compared with the single-objective optimization and it has been found that the kerf taper was increased by 1.60% in multi-objective optimization while the MRR was same in both cases.     

Application of non-dominated sorting genetic algorithm for multi-objective optimization of electrical discharge diamond face grinding process

Hybrid machining processes (HMPs), having potential for machining of difficult to machine materials but the complexity and high manufacturing cost, always need to optimize the process parameters. Our objective was to optimize the process parameters of electrical discharge diamond face grinding (EDDFG), considering the simultaneous effect of wheel speed, pulse current, pulse on-time and duty factor on material removal rate (MRR) and average surface roughness (Ra). The experiments were performed on a high speed steel (HSS) workpiece at a self developed face grinding setup on an EDM machine. All the experimental results were used to develop the mathematical model using response surface methodology (RSM). The developed model was used to generate the initial population for a genetic algorithm (GA) during optimization, non-dominated sorting genetic algorithm (NSGA-II) was used to optimize the process parameters of EDDFG process. Finally, optimal solutions obtained from pareto front are presented and compared with experimental data.     

Investigation and optimization of the internal cylindrical surface lapping process of 440c steel

A new lapping method is proposed for internal cylindrical surfaces finishing. Regression analysis and artificial neural network (ANN) were used for modeling this lapping process and predicting the effects of parameters of rotational speed of the lapping tool (ω), the length of the lapping tool (L) and difference in external diameter of the lapping tool and internal diameter of the workpiece (D) on the material removal rate (MRR), out-of-cylindricity (C) and surface roughness (Ra) of the lapped holes. Comparison of the results of the regression and ANN models with the values obtained from the experiments indicates that the MRR, Ra and C are more accurately predicted using ANN models. MRR, Ra and C of the lapped holes have been optimized using genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The results show that the highest MRR, which is equal to 2029 μg/min, has been achieved at ω of 400 rpm, D of 0.1 mm and L of 45 mm. The lowest Ra of the lapped hole is 64 nm which has been obtained at ω of 100 rpm, D of 0.1 mm and L of 20.82 mm. The minimum C of the lapped hole is 3 μm, which was obtained at ω of 212 rpm, D of 0.1 mm and L of 28.3 mm. The most important problem in the lapping process is the low MRR which causes increased cost and production time. Therefore, in the lapping process, the selection of conditions, that in addition to the production of pieces with geometric accuracy and surface roughness needing a high MRR, is very important. In this study, MRR, Ra and cylindricity of the lapped holes was optimized using multi-objective PSO (MOPSO) algorithm and non-dominated sorting genetic algorithm II (NSGA II), and the Pareto optimal solutions were obtained. Comparison of the results obtained from NSGA II and MOPSO shows that both of these algorithms can achieve optimal Pareto front with the same accuracy, but the time required to reach the MOPSO algorithm to the optimal Pareto front is 25 % less than the NSGA II.

     

激光清洗过程低碳建模与工艺参数优化

激光清洗是应用脉冲激光束高能量冲击去除基材表面污染物,已广泛应用于航空航天、海工等领域高端装备的运维。但其清洗工艺时间长、激光束的高热量及电能的不完全转化导致大量碳排放产生。基于此,提出一种面向低碳的激光清洗过程工艺参数多目标优化方法。首先,分析激光清洗工艺碳排放特性,搭建激光清洗碳排放监测平台,建立激光清洗过程碳排放模型,探究激光清洗工艺参数对碳排放的影响规律;在此基础上,以清洗质量最佳、碳排放最低为目标,建立激光清洗过程多目标工艺参数优化模型,提出一种基于佳点集与协同进化框架多目标进化算法的模型求解方法,解决初始解质量差、种群多样性差、易陷入局部最优等问题;运用改进GRA与TOPSIS方法获得最佳的工艺参数组合。最后,应用QZ2425激光清洗装备对2A12铝合金进行除漆实验,实验表明：该模型及算法可有效降低激光清洗过程碳排放,保证清洗质量,为我国激光加工产业实现碳达峰、碳中和提供一条有效途径。     

基于SVR-GA算法的广义加工空间机床切削稳定性预测与优化研究

针对机床零件加工位置和进给方向不确定造成刀尖频响函数变化,导致切削稳定性叶瓣图与无颤振工艺参数预测具有不确定性问题,提出一种耦合支持向量回归机(SVR)与遗传算法(GA)的切削稳定性预测与优化方法。该方法采用锤击法模态实验和空间坐标变换,获取样本空间不同加工位置与进给方向的刀尖频响函数;进而结合传统切削稳定性预测方法构建以各向运动部件位移、进给角度、主轴转速、切削宽度、每齿进给量为输入的极限切削深度SVR预测模型;采用该SVR模型作为切削稳定性约束建立材料切除率优化模型,通过遗传算法求解各运动轴位移、进给角度与切削参数的最优配置。以某型加工中心展开实例研究,实验结果表明获取的优化配置能实现稳定切削,验证了该方法的有效性。     

激光切割参数对板材品质影响的研究

基于激光切割理论,依靠大量实验完成了对激光切割工艺参数的优化,对影响激光切割表面品质的主要因素,如工作气体、激光功率、辅助气体、焦点位置及切割速度进行分析对比,以切口的宽度和表面粗糙度作为衡量的指标,总结出实验材料的最优工艺参数。     

The optimization of the surface roughness in the process of tangential turn-milling using genetic algorithm

Turn-milling is a relatively new process in manufacturing technology, where both the workpiece and the tool are given a rotary movement simultaneously. This paper presents an approach for optimization of cutting parameters at cylindrical workpieces leading to minimum surface roughness by using genetic algorithm in the tangential turn-milling process. During testing, the effects of the cutting parameters on the surface roughness were investigated. Additionally, by using genetic algorithms for each of the cutting parameters (depth of cut, workpiece speed, tool speed and feed rate) minimum surface roughness for the process of tangential turn-milling was determined according to the cutting parameters.     

Parametric optimization for surface roughness, kerf and MRR in wire electrical discharge machining (WEDM) using Taguchi design of experiment

This paper reports the effect and optimization of eight control factors on material removal rate (MRR), surface roughness and kerf in wire electrical discharge machining (WEDM) process for tool steel D2. The experimentation is performed under different cutting conditions of wire feed velocity, dielectric pressure, pulse on-time, pulse off-time, open voltage, wire tension and servo voltage by varying the material thickness. Taguchi’s L18 orthogonal array is employed for experimental design. Analysis of variance (ANOVA) and signal-tonoise (S/N) ratio are used as statistical analyses to identify the significant control factors and to achieve optimum levels respectively. Additionally, linear regression and additive models are developed for surface roughness, kerf and material removal rate (MRR). Results of the confirmatory experiments are found to be in good agreement with those predicted. It has been found that pulse on-time is the most significant factor affecting the surface roughness, kerf and material removal rate.     

New knowledge-based genetic algorithm for excavator boom structural optimization

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.     

Strength modeling and optimizing ultrasonic welded parts of ABS-PMMA using artificial intelligence methods

This study analyzes variations in metal removal rate (MRR) and quality performance of roughness average (R _a) and corner deviation (CD) depending on parameters of wire electrical discharge machining (WEDM) process in relation to the cutting of pure tungsten profiles. A hybrid method including response surface methodology (RSM) and back-propagation neural network (BPNN) integrated simulated annealing algorithm (SAA) were proposed to determine an optimal parameter setting. The results of 18 experimental runs via a Taguchi orthogonal table were utilized to train the BPNN to predict the MRR, R _a, and CD properties. Simultaneously, RSM and SAA approaches were individually applied to search for an optimal setting. In addition, analysis of variance was implemented to identify significant factors for the processing parameters. Furthermore, the field-emission scanning electron microscope images show that a lot of built-edge layers were presented on the finishing surface after the WEDM process. Finally, the optimized result of BPNN with integrated SAA was compared with that obtained by an RSM approach. Comparisons of the results of the algorithms and confirmation experiments show that both RSM and BPNN/SAA methods are effective tools for the optimization of parameters in WEDM process.     

超声振动车削的有限元仿真及参数优化

基于超声振动切削中的关键参数振幅、振动频率和切削速度,针对如何确定其最优值的问题,以典型的难加工材料不锈钢作为研究对象,利用ABAQUS建立有限元模型,通过数学模型和试验数据验证有限元模型的准确性,并结合优化软件ISIGHT进行联合优化,采用全局优化中的Evol进化优化算法得到超声加工中的最优刀具参数和最优切削参数。     

基于进化神经网络的刀具寿命预测

为预测道具寿命,引入人工神经网络技术,建立了刀具寿命预测神经网络模型,同时对切削参数进行优化选择.在刀具寿命预测中,针对反向传播算法存在收敛速度慢、容易陷入局部极小值及全局搜索能力弱等缺陷,采用遗传算法训练反向传播神经网络,设计了进化神经网络的学习算法.实验和仿真结果表明:基于进化计算的反向传播神经网络可以克服单纯使用反向传播网络易陷入局部极小值等难题,刀具寿命的预测精度较高,从而为刀具需求计划制定、刀具成本核算,以及切削参数制定提供理论依据,节约了制造执行系统中的生产成本.     

Experimental investigation and prediction of optimum process parameters of micro-wire-cut EDM of 2205 DSS

Micro-wire-cut electrode discharge machining (EDM) is an emerging manufacturing process in the field of micro-manufacturing to fabricate the complex profiles of micro-components. It is a complex process involving various process parameters such as pulse on time, pulse off time, wire speed, wire tension and current. In addition to micro-fabrication, this process can also be extended in the field of tool design and developments such as dies, moulds, precision manufacturing, contour cutting, etc., where complex shapes need to be generated with high-grade dimensional accuracy and surface finish. In this research work, an attempt is made to investigate the effect of process parameters on the output variables such as material removal rate (MRR), surface finish and the cutting width (kerf) of wire-cut EDM for duplex stainless steel (DSS). Scanning electron microscopy (SEM) has been used to capture the images of the kerf width, and the measurements are taken with the help of the welding expert system and software. An optimization technique (Taguchi method) has been employed to identify the optimum parameters of the micro-wire-cut EDM process for cutting 2205 grade duplex stainless steel. The effect of each control parameter on the performance measure is studied individually using the plots of signal to noise ratio.     

An improved real-coded genetic algorithm for parameters estimation of nonlinear systems

This paper presents a searching method for parameters estimation of nonlinear system by using a modified real-coded genetic algorithm (GA). It is well known that GA method is an optimal or near-optimal search technique borrowing the concepts from biological evolutionary theory. The ordinary form of GA used for solving a given optimization problem is a binary encoding during operating procedures. However, in the real applications a real-valued encoding is usually used and is easy to directly implement the programming operations. Thus, in this paper we develop a multi-crossover real-coded GA and utilize it to estimate the parameters of nonlinear process systems, even though those have the term of the time delay or are not linear in the parameters. The effectiveness of the proposed algorithms is compared with different evolutionary algorithms. Simulation results of two kinds of process systems will be illustrated to show that the more accurate estimations can be achieved by using our proposed method.     

Investigating material removal rate and surface roughness using multi-objective optimization for focused ion beam (FIB) micro-milling of cemented carbide

Cemented carbide has been investigated as a useful material for the fabrication of micro devices. Focused ion beam (FIB) micro-milling has been found to be one of the most appropriate methods for the fabrication of micro devices. The experimental FIB micro-milling on cemented carbide have been conducted according to the L₁₆ orthogonal array of Taguchi technique. Beam current, extraction voltage, angle of beam incidence, dwell time and percentage overlap between beam diameters have been considered as process variables of FIB micro-milling in experimental design. Material removal rate (MRR) and surface roughness have been determined experimentally for FIB micro-milling of cemented carbide and beam current has been identified as the most significant parameter. The minimum surface roughness of 5.6 nm has been reported on cemented carbide, which is not a usual practice to achieve on such polycrystalline material, and hence it may be considered as a significant research contribution. Maximum MRR of 0.4836 μm³/s has been reported. Moreover, genetic algorithm toolbox of MATLAB has been utilized for multi-objective optimization between MRR and surface roughness. The corresponding optimum values of MRR and surface roughness for multi-objective optimization have been represented by pareto optimum solution generated by genetic algorithm. The research work presented in this paper determines the setting of process parameters of FIB micro-milling for achieving a specific combination of MRR and surface roughness on cemented carbide.     

An Improved genetic algorithm for the prediction of surface finish in dry turning of SS 420 materials

Determination of optimal cutting parameters is one of the most important elements in any process planning of metal parts. This paper presents a development of an improved genetic algorithm (IGA) and its application to optimize the cutting parameters for predicting the surface roughness is proposed. Optimization of cutting parameters and prediction of surface roughness is concerned with a highly constrained nonlinear dynamic optimization problem that can only be fully solved by complete enumeration. The IGA incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. The main advantage of the IGA approach is that the “curse of dimensionality” and a local optimal trap inherent in mathematical programming methods can be simultaneously overcome. The IGA equipped with an improved evolutionary direction operator and a migration operation can efficiently search and actively explore solutions. The IGA approach is applied to predict the influence of tool geometry (nose radius) and cutting parameters (feed, speed, and depth of cut) on surface roughness in dry turning of SS 420 materials conditions based on Taguchi's orthogonal array method. Additionally, the proposed algorithm was compared with the conventional genetic algorithm (CGA), and we found that the proposed IGA is more effective than previous approaches and applies the realistic machining problem more efficiently than does the conventional genetic algorithm (CGA).     

基于遗传算法的超精密切削表面粗糙度预测模型参数辨识及切削用量优化

建立易于分析各切削用量对粗糙度影响关系的表面粗糙度预测模型和最优的切削用量组合,是超精密切削加工技术的不断发展的需要。针对最小二乘法和传统优化方法的不足,提出了将遗传算法用于超精密切削表面粗糙度预测模型的参数辨识,并用于求解最优切削用量,给出了金刚石刀具超精密切削铝合金的表面粗糙度预测数学模型和切削用量优化结果,进行了遗传算法和常规优化算法的比较,结果表明遗传算法较最小二乘法和传统的优化方法更适合于粗糙度预测模型的参数辨识及保证切削用量的最优。     

Optimal Predicted Fuzzy PI Gain Scheduling Controller of Constant Turning Force Systems with Fixed Metal Removal Rate

The dynamic behaviour of the turning process is nonlinear and time-varying owing to variations in cutting depth. This paper proposes an optimal predicted fuzzy PI gain scheduling controller to control the constant turning force (CTF) process with a fixed metal removal rate (MRR) under various cutting conditions. The predicted fuzzy PI gain scheduling control scheme consists of two parts: the fuzzy PI gain scheduling controller; and the grey predictor. First, the optimal parameters of both the grey predictor and the optimal PI gains corresponding to each desired cutting depth in the range of operation, are designed off-line by using the proposed optimal combined method, i.e. Taguchi–RGA method, which integrates the Taguchi method and a real-coded genetic algorithm (RGA). Then, before the parameters of both the grey predictor and the PI gains are scheduled on-line, by fuzzy inference in terms of the changes of cutting depth, the optimal set of triangular-type membership functions of the fuzzy inference mechanism for scheduling the parameters of both the grey predictor and the PI gains are also designed off-line by using the Taguchi–RGA method. Computer simulations are performed to verify the applicability of this optimal predicted fuzzy PI gain scheduling control scheme for controlling the CTF process with a fixed MRR under various cutting conditions. It is shown that such an optimal predicted fuzzy PI gain scheduling control scheme can achieve satisfactory performance and better results than those reported recently in the literature.     

Finite element analysis of laser inert gas cutting on Inconel 718

Inconel 718 has high strength, which makes it difficult to cut using conventional cutting methods. In the present study, the laser inert gas cutting of Inconel 718 was simulated by finite element analysis software ANSYS. Finite element method was used to predict thermal stress and kerf width formation during the laser cutting process. ANSYS Parameter Design Language was used to model the Gaussian-distributed heat flux from the laser beam acting on the workpiece. The removal of melted material during laser cutting to form the kerf width was modeled by employing the element death methodology in ANSYS. In addition, laser cutting was simulated at continuous wave (CW) and the effects of laser power and cutting speed on kerf width were investigated. A series of experiments were carried out to verify the predictions. The temperature fields on the workpiece were measured using thermocouples. The kerf width size was measured using a profile projector, whereas the metallurgical and morphological changes at the cutting edge were examined using scanning electron microscopy. A good correlation was found between the simulation and experimental results.