White layer thickness prediction in wire-EDM using CuZn-coated wire electrode – ANFIS modelling

Maher, Ibrahem; Sarhan, Ahmed A.D.; Marashi, Houriyeh; Barzani, Mohsen Marani; Hamdi, M.

doi:10.1080/00202967.2016.1180847

Cited by 22 publications

(9 citation statements)

References 12 publications

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“…The thermal energy of discharge sparks depends on technological parameters including Ue, Ie, Ton, and Tof [9]. Ue, Ie, and Ton are the parameters that strongly affect the energy of the discharge sparks [19]. The energy of the discharge sparks is determined by Equation (1).…”

Section: Theory Of the Temperature Effects Of Dischargesmentioning

confidence: 99%

“…The best technological parameter set for RLT and HAZ is the smallest as determined by the Taguchi method [18]. Taguchi combined with ANFIS was also able to accurately determine the dimensions of the RLT [19]. The results showed that Ie, Ton and Tof strongly influenced RLT and that RLT was smallest at Ie and Ton at the lowest, and Tof was at the highest.…”

Section: Introductionmentioning

confidence: 99%

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Influence of Process Parameters on the Microstructural Characteristics and Mechanical Properties of Recast Layer Thickness Coating on Die Steel Machined Surface after Electrical Discharge Machining

Huu

Dong

Jatti

et al. 2021

IJE

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Section: Theory Of the Temperature Effects Of Dischargesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence of Process Parameters on the Microstructural Characteristics and Mechanical Properties of Recast Layer Thickness Coating on Die Steel Machined Surface after Electrical Discharge Machining

Huu

Dong

Jatti

et al. 2021

IJE

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“…It was concluded that WEDM is not a possible alternative to generate a precrack in a compact tension specimen to achieve fracture toughness testing of aluminum 7075-T6511 [8]. In this paper the effect of wire EDM process on the surface integrity was studied using a coated wire electrode , the adaptive neuro-fuzzy inference system (ANFIS) was used to predict the White layer thickness (WLT) in WEDM [9]. It was concluded that the smallest value of WLT is reached at the minimum levels of peak current and pulse duration with high level of pulse interval.…”

Section: -Literature Reviewmentioning

confidence: 99%

Experimental Investigation of Wire EDM Process Parameters on Heat Affected Zone

Najm

2018

ETJ

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It is vital to study the impact of heat since it has an unwanted phenomenon on the operating life of parts produced, which is in turn effect the cost of production. when dimension accuracy is the primary function Wire cut machining comprehensively used specially when machining hard metal by using precisely controlled sparks that occur between a very thin wire and a workpiece in the presence of a dielectric fluid. The aim of this research is to study the effects of some Wire Electrical Discharge Machining (WEDM) variables on heat affected zone, white layer and Surface Roughness (SR) of high speed steel using ANOVA method to analyze the behavior of control parameters such as pulse on time (µs), current and wire tension. Experimental investigations separates into two main parts in the first one the wire electrical discharge machining process was accomplished on smart EDM (Electra wire cut machine), and in the second part the roughness of the machined samples surface was tested and the samples were prepared to be measured. The results shows that the heat affected zone and the white layer are affected with the risen of the current and pulse on time values more than the risen in wire tension values, as well as the wire tension affects the surface roughness more than the current and pulse on time.

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“…Soft computing techniques are useful to provide mathematical relations when exact relations are not available. These techniques differ from conventional computing methods in that they tolerate imprecision, uncertainty, partial truth, approximation, and metaheuristics [ 17 ]. Two famous forms of artificial intelligence; including neural networks and fuzzy systems, are used together to enhance the developed models of 3D printed PLA [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques differ from conventional computing methods in that they tolerate imprecision, uncertainty, partial truth, approximation, and metaheuristics [ 17 ]. Two famous forms of artificial intelligence; including neural networks and fuzzy systems, are used together to enhance the developed models of 3D printed PLA [ 17 ]. Two famous forms of artificial intelligence; including neural networks and fuzzy, were used together to enhance the developed models of 3D printed polylactic acid.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System for Modelling the Effect of Slurry Impacts on PLA Material Processed by FDM

et al. 2020

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In this research, the effect of water-silica slurry impacts on polylactic acid (PLA) processed by fused deposition modeling (FDM) is examined under different conditions with the assistance of an adaptive neuro-fuzzy interference system (ANFIS). Building orientation, layer thickness, and slurry impact angle are considered as the controllable variables. Weight gain resulting from water, net weight gain, and total weight gain are the predicting variables. Results uncover the accomplishment of the ANFIS model to appropriately appraise slurry erosion in correlation with comparing real data. Both experimental and ANFIS results are almost identical with average percentage error less than 5.45 × 10−6. We observed during the slurry impacts tests that all specimens showed an increase in their weights. This weight gain was finally interpreted to the synergetic effect of water absorption and the solid particles fragmentations immersed within the specimens due to the successive slurry impacts.

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White layer thickness prediction in wire-EDM using CuZn-coated wire electrode – ANFIS modelling

Cited by 22 publications

References 12 publications

Influence of Process Parameters on the Microstructural Characteristics and Mechanical Properties of Recast Layer Thickness Coating on Die Steel Machined Surface after Electrical Discharge Machining

Influence of Process Parameters on the Microstructural Characteristics and Mechanical Properties of Recast Layer Thickness Coating on Die Steel Machined Surface after Electrical Discharge Machining

Experimental Investigation of Wire EDM Process Parameters on Heat Affected Zone

Adaptive Neuro-Fuzzy Inference System for Modelling the Effect of Slurry Impacts on PLA Material Processed by FDM

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