Milling Microchannels in Monel 400 Alloy by Wire EDM: An Experimental Analysis

Saleh, Mustafa; Anwar, Saqib; El-Tamimi, Abdualziz; Mohammed, Muneer Khan; Ahmad, Shafiq

doi:10.3390/mi11050469

Cited by 20 publications

(8 citation statements)

References 34 publications

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“…The results showed that increasing the current (from 2A to 12A) reduced the cutting time (by 60.95%) while keeping all other parameters constant. Likewise, in similar studies [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] related to the WIRE-EDM of PCMs, it was observed that the quality and accuracy of holes in a low-conductive material can be regulated by applying a conductive layer above the non-conductive PCM. Also, the development of theoretical models of the WIRE-EDM of PCMs provides a guide to obtain the accuracy required in the process [37].…”

Section: Introductionmentioning

confidence: 83%

Analysis of Wire-Cut Electro Discharge Machining of Polymer Composite Materials

et al. 2021

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This study presents the analysis of wire-cut electro-discharge machining (WIRE-EDM) of polymer composite material (PCM). The conductivity of the workpiece is improved by using 1 mm thick titanium plates (layers) sandwiched on the PCM. Input process parameters selected are variable voltage (50–100 V), pulse duration (5–15 μs), and pause time (10–50 μs), while the cut-width (kerf) is recognized as an output parameter. Experimentation was carried out by following the central composition design (CCD) design matrix. Analysis of variance was applied to investigate the effect of process parameters on the cut-width of the PCM parts and develop the theoretical model. The results demonstrated that voltage and pulse duration significantly affect the cut-width accuracy of PCM. Furthermore, the theoretical model of machining is developed and illustrates the efficacy within the acceptable range. Finally, it is concluded that the model is an excellent way to successfully estimate the correction factors to machine complex-shaped PCM parts.

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Section: Introductionmentioning

confidence: 83%

Analysis of Wire-Cut Electro Discharge Machining of Polymer Composite Materials

et al. 2021

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“…A similar character is seen in Figure 11 for 2024 (D16) duralumin. The studies [2,11,[15][16][17]19,[46][47][48] show that penetration (adsorption in the recast layer) of the tool electrode material components can reach a depth of 10 µm (from 2.55 up to 50 µm depending on used materials of electrodes, dielectric medium, and experiment factors). At the same time, this uneven distribution of the workpiece and wire elements can be explained by sublimation phenomena of the recast layers (similar to laser ablation during heating with the concentrated energy flows such as laser, plasma, electric discharges, fast energy neutral atoms [49][50][51][52]), when components of workpiece material sublimate primarily [53,54].…”

Section: Submicrostructure Of Surface and Subsurface Layersmentioning

confidence: 99%

“…Wire electrical discharge machining of Ni-Cu alloy (67% Ni-23% Cu, Monel 400) showed the formed recast layer of 2.55 µm [17]. The surface layer's microhardness was 172-220 HV and increased steadily up to the depth of 20 µm up to 260-275 HV values.…”

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confidence: 99%

Sub-Microstructure of Surface and Subsurface Layers after Electrical Discharge Machining Structural Materials in Water

Grigoriev

Волосова

Okunkova

et al. 2021

Metals

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The material removal mechanism, submicrostructure of surface and subsurface layers, nanotransformations occurred in surface and subsurface layers during electrical discharge machining two structural materials such as anti-corrosion X10CrNiTi18-10 (12kH18N10T) steel of austenite class and 2024 (D16) duralumin in a deionized water medium were researched. The machining was conducted using a brass tool of 0.25 mm in diameter. The measured discharge gap is 45–60 µm for X10CrNiTi18-10 (12kH18N10T) steel and 105–120 µm for 2024 (D16) duralumin. Surface roughness parameters are arithmetic mean deviation (Ra) of 4.61 µm, 10-point height (Rz) of 28.73 µm, maximum peak-to-valley height (Rtm) of 29.50 µm, mean spacing between peaks (Sm) of 18.0 µm for steel; Ra of 5.41 µm, Rz of 35.29 µm, Rtm of 43.17 µm, Sm of 30.0 µm for duralumin. The recast layer with adsorbed components of the wire tool electrode and carbides was observed up to the depth of 4–6 µm for steel and 2.5–4 µm for duralumin. The Levenberg–Marquardt algorithm was used to mathematically interpolate the dependence of the interelectrode gap on the electrical resistance of the material. The observed microstructures provide grounding on the nature of electrical wear and nanomodification of the obtained surfaces.

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“…For example, Ahmed et al [ 5 ] conducted the experiment on the manufacture of high-aspect-ratio thin structures of micrometer thickness (117–500 μm) from D2 steel through WEDM. In order to produce microchannels with desired/target geometry and acceptable surface quality, Saleh et al [ 6 ] carried out the results of an investigation on the capacity of WEDM to produce microchannels in the nickel-based alloy, Monel 400. WEDM is one of typical kind of EDM, they are developed by using the phenomenon of spark erosion, and a lot of research has been carried out in various aspects relating to improving performance measures, optimizing the process variables, and monitoring and controlling the sparking process [ 4 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Spark Analysis Based on the CNN-GRU Model for WEDM Process

et al. 2021

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Wire electrical discharge machining (WEDM), widely used to fabricate micro and precision parts in manufacturing industry, is a nontraditional machining method using discharge energy which is transformed into thermal energy to efficiently remove materials. A great amount of research has been conducted based on pulse characteristics. However, the spark image-based approach has little research reported. This paper proposes a discharge spark image-based approach. A model is introduced to predict the discharge status using spark image features through a synchronous high-speed image and waveform acquisition system. First, the relationship between the spark image features (e.g., area, energy, energy density, distribution, etc.) and discharge status is explored by a set of experiments). Traditional methods have claimed that pulse waveform of “short” status is related to the status of non-machining while through our research, it is concluded that this is not always true by conducting experiments based on the spark images. Second, a deep learning model based on Convolution neural network (CNN) and Gated recurrent unit (GRU) is proposed to predict the discharge status. A time series of spark image features extracted by CNN form a 3D feature space is used to predict the discharge status through GRU. Moreover, a quantitative labeling method of machining state is proposed to improve the stability of the model. Due the effective features and the quantitative labeling method, the proposed approach achieves better predict result comparing with the single GRU model.

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Milling Microchannels in Monel 400 Alloy by Wire EDM: An Experimental Analysis

Cited by 20 publications

References 34 publications

Analysis of Wire-Cut Electro Discharge Machining of Polymer Composite Materials

Analysis of Wire-Cut Electro Discharge Machining of Polymer Composite Materials

Sub-Microstructure of Surface and Subsurface Layers after Electrical Discharge Machining Structural Materials in Water

Spark Analysis Based on the CNN-GRU Model for WEDM Process

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