Multi-response optimization of machining process parameters for powder mixed electro-discharge machining of H-11 die steel using grey relational analysis and topsis

Electro-discharge machining (EDM) is an enormously used nonconventional process for removing material in die making, aerospace, and automobile industries. It consists of limitations like poor volumetric material removal rate (MRR) and reduced surface quality. Powder mixed EDM (PMEDM) is a new development in EDM to enhance its machining capabilities. The present work investigates the effect of powder concentration (C_p), peak current (I_p), pulse on time (T_on), duty cycle (DC) and gap voltage (V_g) on MRR, tool wear rate (TWR), electrode wear ratio (EWR), and surface roughness (SR) simultaneously for H-11 die steel using SiC powder. Taguchi's L₂₇ orthogonal array has been used to conduct the experiments. Multiobjective optimization using grey relational analysis (GRA) and technique for order of preference by similarity to ideal solution (TOPSIS) has been used to maximize the MRR and minimize the TWR, EWR, and SR and determine the optimal set of process parameters. Analysis of variance (ANOVA) has been performed to understand the significance of each process parameter. Results were verified by conducting confirmatory tests. GRA and TOPSIS exhibit an improvement of 0.1843 and 0.14308 in the preference values, respectively. Microstructure analysis has been done using scanning electron microscope (SEM) for the optimum set of parameters.     

电火花成型加工间隙电压在线检测与处理的新方法

通过对电火花成型加工过程放电状态的分析,在阐述了传统的间隙电压在线检测与处理方法的基础上,提出了一种新的在线检测与处理方法。该方法利用积分电路对间隙电压进行积分,而后求取其在本分析周期内的平均值,这适合于间隙电压检测适用统计规律的特点。与传统方法相比,本方法无需使用高速数据采集器件和大容量存储器件,硬件成本显著降低,由于无需对大量数据进行处理,节省大量CPU时间,软件更趋简洁。此方法具有很强的工程应用价值,通过对运行结果进行对比,效果良好。     

Characteristics of the surfaces obtained in electro-discharge machining

According to the established thermal theories on electro-discharge machining (edm), the size of the craters produced in edm is proportional to the discharge energy, which is equal to the product of mean pulse current and pulse duration for a constant gap voltage. As a practical edm surface is a random superposition of such craters, the behaviour of the surfaces largely depends on the two important parameters, pulse current and pulse duration. With the help of multiple linear regression analysis, the effect of pulse duration and pulse current on the surface topography parameters R_q and β* has been studied. sem photomicrographs have been taken in support of the qualitative evidence     

Mathematical modeling and process parameters optimization of ultrasonic assisted electrochemical magnetic abrasive machining

Journal of Mechanical Science and Technology - A new machining technique called ultrasonic assisted electrochemical magnetic abrasive machining integrates ultrasonic vibrations, electrochemical...     

Soft computing models and intelligent optimization system in electro-discharge machining of SiC/Al composites

In this paper, a multi-variable regression model, a back propagation neural network (BPNN) and a radial basis neural network (RBNN) have been utilized to correlate the cutting parameters and the performance while electro-discharge machining (EDM) of SiC/Al composites. The four cutting parameters are peak current (I_p), pulse-on time (T_on), pulse-off time (T_off), and servo voltage (S_v); the performance measures are material remove rate (MRR) and surface roughness (Ra). By testing a large number of BPNN architectures, 4-5-1 and 4-7-1 have been found to be the optimal one for MRR and Ra, respectively; and it can predict them with 10.61 % overall mean prediction error. As for RBNN architectures, it can predict them with 12.77 % overall mean prediction error. The multivariable regression model yields an overall mean prediction error of 13.93 %. All of these three models have been used to study the effect of input parameters on the material remove rate and surface roughness, and finally to optimize them with genetic algorithm (GA) and desirability function. Then, an intelligent optimization system with graphical user interface (GUI) has been built based on these multi-optimization techniques, in which users can obtain the optimized cutting parameters under the desired surface roughness (Ra).     

多轴联动电火花加工数控系统开发

为加工具有复杂几何结构或者材料较难加工的零件,开发了多轴联动电火花加工数控系统。为了增强系统的实时性、稳定性和可靠性,采用RT-Linux技术,提出双核结构概念来分别处理实时任务和非实时任务。确立数控系统由实时控制模块、驱动模块、用户管理模块和模块间的通信所组成。提出了线程承载法解决实时任务间协调控制问题,以及循环选择法解决任务调度问题。给出了平面二轴联动插补派生法构建适合电火花加工机床的多轴联动插补控制算法,提出内存映射法构建硬件实时驱动方式以增强系统驱动的实时性,提出线程-处理器模式构建用户管理模块,以使复杂的管理任务清晰化和易于管理,选择最优的通信方式来增强数控系统的实时性能。开发出了五轴联动电火花加工数控系统,并运用该系统进行了带冠整体式涡轮盘的加工实验。实验结果表明,该系统具有实时性强、稳定性好和可靠性高等特点。     

Experimental modeling and multi-objective optimization of traveling wire electrochemical spark machining (TW-ECSM) process

The present paper attempts to focus an application of a hybrid methodology comprising of Taguchi methodology (TM) coupled with response surface methodology (RSM) for modeling and TM coupled with weighted principal component (WPC) methodology for multiobjective optimization of a self developed traveling wire electro-chemical spark machining (TW-ECSM) process. First optimum level of input parameters is found using TM which is used as the central values in RSM to develop the second-order response model. For multiobjective optimization two quality characteristics surface roughness (R_a) and material removal rate (MRR), which are of opposite nature (R_a is the lower-the-better type, while MRR is the higher-the-better type), have been selected. The WPC is employed for the calculation of weight corresponding to each quality characteristic. The results indicate that the hybrid approaches applied for modeling and optimization of the TW-ECSM process are reasonable.     

Empirical modeling and optimization of wire electrical discharge machining

Wire electrical discharge machining is a widely used process in manufacturing industries to machine complex profiles. The performance of any machining process is based on choosing the right combination of input parameters. Metal removal rate and surface roughness are the most important output parameters, which decide the performance of a machining process. The selection of optimal parameters in wire electrical discharge machining is difficult as it is a complex process and involves a large number of variables. The present work models the metal removal rate and the surface roughness in terms of the input variables using the response surface methodology and, consequently, the developed mathematical models are utilized for optimization. Since the influences of machining parameters on the metal removal rate and the surface roughness are opposite, the problem is formulated as a multiobjective optimization problem. Non-dominated sorting genetic algorithm is then applied to obtain the Pareto-optimal set of solutions.     

Concurrent optimization of machining process parameters and tolerance allocation

With the advent use of sophisticated and high-cost machines coupled with higher labor costs, concurrent optimization of machining process parameters and tolerance allocation plays a vital role in producing the parts economically. In this paper, an effort is made to concurrently optimize the manufacturing cost of piston and cylinder components by optimizing the operating parameters of the machining processes. Design of experiments (DoE) is adopted to investigate systematically the machining process parameters that influence product quality. In addition, tolerance plays a vital role in assembly of parts in manufacturing industries. For the selected piston and cylinder component, improvements efforts are made to reduce the total manufacturing cost of the components. By making use of central composite rotatable design method, a module of DoE, a mathematical model is developed for predicting the standard deviation of the tolerance achieved by grinding process. This mathematical model, which gives 93.3% accuracy, is used to calculate the quality loss cost. The intent of concurrent optimization problem is to minimize total manufacturing cost and quality loss function. Genetic algorithm is followed for optimizing the parameters. The results prove that there is a considerable reduction in manufacturing cost without violating the required tolerance, cutting force, and power.     

Bending strength analysis of micro WC-shaft manufactured by micro electro-discharge machining

Micro electro-discharge machining (micro-EDM) is a critical technology to fabricate high aspect ratio 3D micro-components. However, the surfaces of micro-components manufactured by micro-EDM will exhibit micro-cracks to produce notch effects, and lead to stress concentrate and reduction of fatigue strength. This paper performs micro-bending tests to investigate the influence of various roughness and size on the bending fracture strength of micro WC-shafts manufactured by micro-EDM. The experimental results indicate that the surface roughness, axial surface area, volume, and length of the specimen will affect its bending fracture strength. For specimens with the same size, the mean bending fracture strength decreases as the surface roughness increases, and the bending fracture probability of the specimens also increases (roughness effect). For specimens with the same roughness, reducing the length or axial surface area will increase the mean bending fracture strength, and reduces the bending fracture probability (size effect).     

Characteristic investigation of pipe cutting technology based on electro-discharge machining

Pipe cutting technology plays an important role in the process of offshore platforms decommissioning, as many devices such as tubing, drill pipe, and casing need to be decommissioned. In this study, a novel cutting pipe technology based on electro-discharge machining (EDM) is proposed, and a cutting pipe mechanism is developed to cut the pipes for decommissioning offshore platforms. The machining principles and characteristics of the technique are described. The effects of machining parameters, including tool polarity, dielectric fluid, electrode material and width, pulse on-time, pulse off-time, peak voltage, and electrode rotation speed to machining performance, are investigated. The material removal rate (MRR) of the machined casing and tool electrode wear ratio (EWR) is obtained based on the calculation of the percentage of mass loss per machining time. The experimental results show that a better cutting performance can be obtained with negative tool polarity at the conditions of dielectric fluid of emulsion, pulse on-time of 500 μs, pulse off-time of 200 μs, peak voltage of 70 V, copper electrode width of 28 mm, and electrode rotation speed of 250 rpm is a better choice. Additionally, the cutting slots surface has been investigated by the means of SEM. The cutting slots machined by the rotary EDM are clean and smooth.     

仿真机械加工过程的实体建模技术研究

介绍了根据机械加工的原理，通过仿真复杂零件的加工过程来构造其实体模型的方法。实践证明，运用此法造型，可直观完整地描述了零件的设计和制造过程，有利于提高设计效率和产品质量。     

Development of multi-objective optimization models for electrochemical machining process

Owing to the complexity of electrochemical machining (ECM), it is very difficult to determine optimal cutting parameters for improving cutting performance. Hence, optimization of operating parameters is an important step in machining, particularly for unconventional machining procedures like ECM. A suitable selection of machining parameters for the ECM process relies heavily on the operator’s technologies and experience because of their numerous and diverse range. Machining parameters provided by the machine tool builder cannot meet the operator’s requirements. Since for an arbitrary desired machining time for a particular job, they do not provide the optimal conditions. To solve this task, multiple regression model and ANN model are developed as efficient approaches to determine the optimal machining parameters in ECM. In this paper, current, voltage, flow rate and gap are considered as machining parameters and metal removal rate and surface roughness are the objectives. Then by applying grey relational analysis, we calculate the grey grade for representing multi-objective model. Multiple regression model and ANN model have been developed to map the relationship between process parameters and objectives in terms of grade. The experimental data are divided into training and testing data. The predicted grade is found and then the percentage deviation between the experimental grade and predicted grade is calculated for each model. The average percentage deviations for the training data of the linear regression model, logarithmic transformation model, excluding interaction terms and ANN model, are 12.7, 25.6 and 3.03, respectively. The average percentage deviations for the testing data of the three models are 9.83, 26.8 and 2.67. While examining the average percentage deviations of three models, ANN is having less percentage deviation. So ANN is considered as the best prediction model. Based on the testing results of the artificial neural network, the operating parameters are optimized. Finally, ANOVA is used to identify the significance of multiple regression model and ANN model.     

Neural network modeling and analysis of the material removal process during laser machining

To manufacture parts with nano- or micro-scale geometry using laser machining, it is essential to have a thorough understanding of the material removal process in order to control the system behaviour. At present, the operator must use trial-and-error methods to set the process control parameters related to the laser beam, motion system, and work piece material. In addition, dynamic characteristics of the process that cannot be controlled by the operator such as power density fluctuations, intensity distribution within the laser beam, and thermal effects can significantly influence the machining process and the quality of part geometry. This paper describes how a multi-layered neural network can be used to model the nonlinear laser micro-machining process in an effort to predict the level of pulse energy needed to create a dent or crater with the desired depth and diameter. Laser pulses of different energy levels are impinged on the surface of several test materials in order to investigate the effect of pulse energy on the resulting crater geometry and the volume of material removed. The experimentally acquired data is used to train and test the neural network's performance. The key system inputs for the process model are mean depth and mean diameter of the crater, and the system outputs are pulse energy, variance of depth and variance of diameter. This study demonstrates that the proposed neural network approach can predict the behaviour of the material removal process during laser machining to a high degree of accuracy.     

支持个性化优化的业务过程建模

为支持根据企业具体的资源环境和目标进行相应的业务过程优化,给出一种基于有向超图的业务过程建模方法.该方法给出了考虑可能支持资源、资源所属企业及可用状态的业务过程的有向超图模型.基于该模型,应用超图理论和模型所附加的过程语义讨论了该模型如何支持个性化的、动态的业务过程优化,并给出具体的优化过程.通过具体实例说明了该方法的特点.该建模方法既是超图理论应用范围的一个扩展,又丰富了超图理论.     

Mathematical modeling and machining parameter optimization for the surface roughness of face gear grinding

Face gear is an important part in the power transmission of helicopter, but its grinding is a difficult problem. In order to enhance the finishing machining surface quality of face gear, the mathematical formula of the residual height of motion trajectory of abrasive grains was obtained, and the model of grinding surface roughness of face gear was corrected. In addition, machining parameter optimization for grinding surface roughness on a five-axis blade grinding machine was investigated by orthogonal experiment method. The experiment results indicated that disk wheel spindle speed and feed velocity of the disk wheel are more significant effect factors among the three factors. The calculation results of model showed that the maximum error comparing with the experiment results is not more than 13.5 %, which suggests that the mathematical model is reasonable.     

Some studies into wire electro-discharge machining of alumina particulate-reinforced aluminum matrix composites

Non-traditional process like wire electro-discharge machining is found to show a promise for machining metal matrix composites. However, the machining information for the difficult-to-machine particle-reinforced material is inadequate. This paper is focused on experimental investigation to examine the effect of electrical as well as non-electrical machining parameters on performance in wire electro-discharge machining of metal matrix composites (Al/Al₂O₃p). Taguchi orthogonal array was used to study the effect of combination of reinforcement, current, pulse on-time, off-time, servo reference voltage, maximum feed speed, wire speed, flushing pressure and wire tension on cutting speed, surface finish, and kerf width. Reinforcement percentage, current, and on-time was found to have significant effect on cutting rate, surface finish, and kerf width. The optimum machining parameter combinations were obtained for surface finish, cutting speed, and kerf width separately. Wire breakages were found to pose limitations on the cutting speed in machining of these materials. Wire shifting was found to deteriorate the machined surfaces.