Experimental and predictive study by multi-output fuzzy model of electrical discharge machining performances

Belloufi, Abderrahim; Mezoudj, Mourad; Abdelkrim, M; Rezgui, Imane; Chiba, Elhocine

doi:10.1007/s00170-020-05718-8

Cited by 15 publications

(6 citation statements)

References 28 publications

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“…Step IV: Rule evaluation The capability of developed fuzzy model can be determined through rule evaluation [13]. The fuzzy rules were evaluated for the condition: Normal load -40N and Sliding distance -2700m.Out of nine rules, four rules map to the condition, as represented in Normal load -40N Sliding distance -2700m Step V: Defuzzification The fuzzified data has been converted to crisp data by defuzzification technique.…”

Section: Fuzzy Logic Approachmentioning

confidence: 99%

“…The fuzzy rules were evaluated for the condition: Normal load -40N and Sliding distance -2700m.Out of nine rules, four rules map to the condition, as represented in Normal load -40N Sliding distance -2700m Step V: Defuzzification The fuzzified data has been converted to crisp data by defuzzification technique. It executes the reverse phenomenon of the fuzzification process [13]. It transforms the fuzzy values of output responses into corresponding practical values of wear and CoF.…”

Section: Fuzzy Logic Approachmentioning

confidence: 99%

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A fuzzy logic approach for investigating the tribological behavior of polymer composite

Jagadeesh¹,

Kumar²,

Aneesh³

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The application spectrum of natural fiber reinforced polymer composites has been increasing tremendously because of enhanced tribologicalbehavior. The present study investigates the wear and coefficient of friction of castor oil fiber reinforced composites for developing as new tribo-material. Composites with 40 vol.% with short fiber length of 5mm were fabricated by hand lay-up method. The tribology tests were performed with pin on disc tribometer at normal loads (15N, 30N & 45N) and sliding distances (1000m, 2000m & 3000m). A fuzzy logic model has been developed to predict and analyze the wear and friction characteristics at unknown test cases. Each input variable was divided into three linguistic variables and each output variable was divided into five linguistic variables. A triangular membership function was used for defining all the variables. The capabilities of fuzzy logic model were tested by confirmatory experiments. The model predicted the wear results with an error of 3.12% and coefficient of friction by 2.19%.

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Section: Fuzzy Logic Approachmentioning

confidence: 99%

Section: Fuzzy Logic Approachmentioning

confidence: 99%

A fuzzy logic approach for investigating the tribological behavior of polymer composite

Jagadeesh¹,

Kumar²,

Aneesh³

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…The result obtained with HAS proved to be better and the prediction error was less than 6%. Belloufi et al [31] predicted the performance of EDM process using fuzzy model during the machining of AISI 1095 treated steel. The model predicted MRR, TWR, SR and radial overcut with an average error of 1.51, 3.386, 5.285, and 4.004%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Analysis of H13 Steel during Electrical Discharge Machining Process Using Cu–TiC Sintered Electrode

et al. 2021

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In electrical discharge machining (EDM), the machined surface quality can be affected by the excessive temperature generation during the machining process. To achieve a longer life of the finished part, the machined surface quality plays a key role in maintaining its overall integrity. Surface roughness is an important quality evaluation of a material’s surface that has considerable influence on mechanical performance of the material. Herein, a sintered cermet tooltip with 75% copper and 25% titanium carbide was used as tool electrode for processing H13 steel. The experiments have been performed to investigate the effects of EDM parameters on the machined surface roughness. The findings show that, as the pulse current, pulse length, and pulse interval are increased, the surface roughness tends to rise. The most significant determinant for surface roughness was found to be pulse current. A semi-empirical surface roughness model was created using the characteristics of the EDM technique. Buckingham’s theorem was used to develop a semi-empirical surface roughness prediction model. The semi-empirical model’s predictions were in good agreement with the experimental studies, and the built empirical model based on physical features of the cermet tooltip was tested using dimensional analysis.

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“…Bhaumik et al [ 34 ] used the GRA method to optimize MRR, SR, ROC, and TWR when machining titanium alloy (grade 6). Belloufi et al [ 35 ] used fuzzy logic to optimize MRR, TWR, wear rate (WR), SR, ROC, circularity (CIR), and cylindricity (CYL) during the machining of AISI 1095 steel utilizing Kerosene oil as a dielectric medium. In addition, a lot of research has been done in this area, using the grey approach [ 36 ] and hybrid nature-inspired algorithms [ 37 , 38 ] as multi-objective optimization tools, as well as the TOPSIS technique [ 39 ] for parameter selection.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation and Optimization of Electric Discharge Machining Process Parameters Using Grey-Fuzzy-Based Hybrid Techniques

et al. 2021

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Electrical discharge machining (EDM) has recently been shown to be one of the most successful unconventional machining methods for meeting the requirements of today’s manufacturing sector by producing complicated curved geometries in a broad variety of contemporary engineering materials. The machining efficiency of an EDM process during hexagonal hole formation on pearlitic Spheroidal Graphite (SG) iron 450/12 grade material was examined in this study utilizing peak current (I), pulse-on time (Ton), and inter-electrode gap (IEG) as input parameters. The responses, on the other hand, were the material removal rate (MRR) and overcut. During the experimental trials, the peak current ranged from 32 to 44 A, the pulse-on duration ranged from 30–120 s, and the inter-electrode gap ranged from 0.011 to 0.014 mm. Grey relational analysis (GRA) was interwoven with a fuzzy logic method to optimize the multi-objective technique that was explored in this EDM process. The effect of changing EDM process parameter values on responses was further investigated and statistically analyzed. Additionally, a response graph and response table were produced to determine the best parametric setting based on the calculated grey-fuzzy reasoning grade (GFRG). Furthermore, predictor regression models for response characteristics and GFRG were constructed, and a confirmation test was performed using randomly chosen input parameters to validate the generated models.

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Experimental and predictive study by multi-output fuzzy model of electrical discharge machining performances

Cited by 15 publications

References 28 publications

A fuzzy logic approach for investigating the tribological behavior of polymer composite

A fuzzy logic approach for investigating the tribological behavior of polymer composite

Surface Roughness Analysis of H13 Steel during Electrical Discharge Machining Process Using Cu–TiC Sintered Electrode

Experimental Investigation and Optimization of Electric Discharge Machining Process Parameters Using Grey-Fuzzy-Based Hybrid Techniques

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