Modeling and prediction of electrical discharge machining performance parameters for AA 8081 hybrid composite using artificial neural network

Vivekanandhan, M; Rajmohan, Kesavalu; Senthilkumar, C.

doi:10.1088/2051-672x/ac4a44

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Prior literatures related to the investigations on surface integrity of AA7068 in WEDM is very limited. Few researchers explored the impact of parameters on recast layer thickness, micro hardness, and machining efficiency of aluminium alloys [7,14,20]. The study related to the impact of dielectric on the machining characteristics, recast layer formation and microhardness on AA7068 is seldom.…”

Section: Introductionmentioning

confidence: 99%

Investigations on the effect of dielectric medium and WEDM parameters on surface characteristics of Al 7068 (ordnance aluminium) alloy

SHARMILA¹,

Selvakumar²,

S³

2022

Surf. Topogr.: Metrol. Prop.

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The current experimental work focuses on determining the optimum machining parameter combination for machining AA7068 in wire electrical discharge machining (WEDM). WEDM is a promising advanced machining technology, capable of processing complex-shaped components, machining hard/heat treated materials, and cutting conductive ceramics that are complicated to machine using traditional methods. Taguchi’s L18 orthogonal array is adopted to conduct the experiments by considering dielectric medium, pulse on time, pulse off time, peak current, gap voltage, and wire feed as input factors. The output responses like surface roughness, material removal rate, micro hardness, recast layer, surface morphology of the machined surfaces and wire tool were considered for study. The ANOVA method was used to analyse the impact of individual processing parameters on material removal rate and surface roughness. Micro hardness of the cut surface was studied using Vicker’s method. Besides, scanning electron microscopy (SEM) and Energy Dispersive X-Ray analysis are conducted to examine the surface characteristics and elemental composition of the machined working piece at various cutting parameters (EDX). TOPSIS method is chosen to identify the optimal parametric conditions. From the results, the optimum condition achieves surface roughness (3.684 µm) and material removal rate (5.106 mm3/min) respectively.

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Section: Introductionmentioning

confidence: 99%

Investigations on the effect of dielectric medium and WEDM parameters on surface characteristics of Al 7068 (ordnance aluminium) alloy

SHARMILA¹,

Selvakumar²,

S³

2022

Surf. Topogr.: Metrol. Prop.

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Spark erosion behavior in the machining of MoSi2–SiC ceramic composites for improving dimensional accuracy

Alfattani,

Yunus,

Selvarajan

et al. 2023

Journal of the Mechanical Behavior of Biomedical Materials

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Electrical discharge machining of super alloy incoloy 925: a study based on box behnken design and response surface methodology

Chaurasia,

Dutta,

Singh

et al. 2024

Phys. Scr.

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Incoloy 925, a Nikel-based superalloy, exhibits low machinability with conventional machining techniques due to its inhomogeneous properties. Therefore, there is a need to establish a non-conventional method to efficiently machine this alloy. This work is a novel attempt to present the electric discharge machining (EDM) of the superalloy Incoloy 925 and subsequent multi-response optimization. The model for the analysis was designed using the Box Behnken Design (BBD) technique, and the Response Surface Methodology (RSM) was used for the optimization of the results. The machining was performed using a cylindrical copper tool of 11 mm diameter. The effect of pulse-on time (Ton), current, and pulse-off time (Toff) on the material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) was investigated. The results from variance analysis confirmed the significance of all the three input factors. The investigation revealed that the maximum MRR (99.2154 mm3/min) was obtained at a pulse-on time of 90 µs, pulse-off time of 5 µs, and current of 30 A. The minimum TWR (0.8866 mm3/min) were achieved at Ton = 60 µs, Toff = 8 µs and current = 10 A. The microscopic images of the machined surfaces revealed very few micro-voids and globules and no cracks, resulting in a fine surface finish of 1.2436 µm, achieved through optimal discharge energy transfer and copper electrodes. The optimal values MRR, TWR, and SR according to composite desirability function are 99.1524 mm3/min, 1.0915 mm3/min, and 1.3925 µm. The experimental results were accurately predicted using RSM and an artificial neural network (ANN), with the ANN showing a predicted correlation coefficient (R) close to 1 indicating high accuracy of the model.

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Modeling and prediction of electrical discharge machining performance parameters for AA 8081 hybrid composite using artificial neural network

Cited by 3 publications

References 28 publications

Investigations on the effect of dielectric medium and WEDM parameters on surface characteristics of Al 7068 (ordnance aluminium) alloy

Investigations on the effect of dielectric medium and WEDM parameters on surface characteristics of Al 7068 (ordnance aluminium) alloy

Spark erosion behavior in the machining of MoSi2–SiC ceramic composites for improving dimensional accuracy

Electrical discharge machining of super alloy incoloy 925: a study based on box behnken design and response surface methodology

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