electrical discharge machining
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2022 ◽  
Vol 14 (4) ◽  
pp. 5-12
Author(s):  
Ol'ga Ermilina ◽  
Elena Aksenova ◽  
Anatoliy Semenov

The paper provides formalization and construction of a model of the process of electrical discharge machining. When describing the process, a T-shaped equivalent circuit containing an RLC circuit was used. Determine the transfer function of the proposed substitution scheme. Also, a task is formulated and an algorithm for neural network parametric identification of a T-shaped equivalent circuit is proposed. The problem is posed and an algorithm is developed for neural network parametric identification of the equivalent circuit with a computational experiment, the formation of training samples on its basis, and the subsequent training of dynamic and static neural networks used in the identification problem. The process was simulated in Simulink, Matlab package. Acceptable coincidence of the calculated data with the experimental ones showed that the proposed model of electrical discharge machining reflects real electromagnetic processes occurring in the interelectrode gap.


Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 96
Author(s):  
Hai-Ping Tsui ◽  
Shih-Yu Hsu

Fe-based metallic glass possesses high hardness and brittleness. It is a hard-to-cut metal material and difficult to machine by conventional methods. Although electrical discharge machining (EDM) has advantages in machining hard-to-cut metal materials, recast layer, pores, and micro cracks will form on the machined surface after machining. The study used a helical tool for the micro electrical discharge drilling (µ-EDD) process on Fe-based metallic glass. The influence of processing parameters, including the pulse on time, gap voltage, duty factor, and spindle rotational speed on the micro hole machining quality characteristics was investigated. The helical tool with SiC electrophoretic deposited (EPD) film was used to polish the inner surface of the electrical discharged micro hole. The findings show that the best micro hole accuracy, tool wear length, and inner surface were obtained at the spindle rotation speed of 1150 rpm, pulse on time of 5 μs, gap voltage of 30 V, and duty factor of 40%. The inner surface roughness can be reduced to 0.018 µm by using EPD tool. The inner surface was polished up to form a mirror surface.


2022 ◽  
Author(s):  
Tao Xue ◽  
Long Chen ◽  
Zhen Zhang ◽  
Jiaquan Zhao ◽  
Yi Zhang ◽  
...  

Abstract This paper presents a framework of data-driven intelligence system which can be applied on magnetic field-assisted electrical discharge machining (MF-EDM) machining process for SiC particulate reinforced Al-based metal matrix composites (SiCp/Al) with different high-volume fractions. The implemented system consists of data modelling, predicating, optimization and monitoring modules. A multi-objective moths search (MOMS) optimization algorithm with back-propagation neural network (BPNN) model and multi-hierarchy non-dominated strategy is proposed for tuning optimal processing performance. Data are collected from machining different fraction volumes of SiCp/Al composites by MF-EDM, with peak current, magnetic, pulse width and pulse interval time as input, and material removal rate, electrode wear rate, surface roughness as output. The BPNN model shows the best accuracy compared to K-nearest neighbours, least square support vector machine and Kriging model. To demonstrate the effectiveness of the MOMS optimization algorithm, a set of results is selected as paradigm, which dominates 95.83% original experiments. A verification experiment is also done for an optimized parameter with 65% fraction and 0.2T magnetic. Both result data and three-dimensional surface topography comparison show that the verification experiment result dominates the original experiment of similar input designs.


2022 ◽  
Vol 11 (2) ◽  
pp. 147-158
Author(s):  
Akash Singh ◽  
Karan Kumar ◽  
K. Gnana Sundari ◽  
Rishitosh Ranjan ◽  
B. Surekha

In the current paper, the authors are intended to manufacture the aluminum based metal matrix composite (MMC) employing the stir casting process. Further, the fabricated composite sample is investigated for machining characteristics during the die sink electrical discharge machining process (EDM). EDM is most commonly employed to satisfy the special needs of industry such as developing deep holes and complex contours from high strength materials such as composites, alloys, smart materials, and functionally graded materials. In the current study A356 and 4%, tungsten carbide (WC) powder are considered as matrix and strengthening materials respectively to fabricate the MMCs. During the machining activity, the input factors like discharge current (Ip), Voltage (Vg), Pulse On-Time (Ton), and flushing pressure (P) are optimized for achieving optimum surface roughness (SR), Tool Wear Rate (TWR) and Material Removal Rate (MRR). To estimate the ideal set of process factors grey regression analysis (GRA) is used. From the results, it was observed that the GRA is found to perform better than the RSM.


2022 ◽  
pp. 824-842
Author(s):  
Kaushik Kumar ◽  
J. Paulo Davim

Electrical Discharge Machining (EDM) process is a widely used machining process in several fabrication, construction and repair work applications. Considering Pulse-On Time, Pulse OFF time, Peak-Current and Gap voltage as the inputs and among all possible outputs, in the present work Material Removal Rate and Surface Roughness are considered as outputs. In order to reduce the number of experiments Design of Experiments (DOE) was undertaken using Orthogonal Array and later on the outputs were optimized using ANN and PSO. It was found that the results obtained from both the techniques were tallying with each other.


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