Integration of GA and neuro-fuzzy approaches for the predictive analysis of gas-assisted EDM responses

Singh, Nishant; Singh, Yashvir; Kumar, Sanjeev; Upadhyay, Ravi Kant

doi:10.1007/s42452-019-1533-x

Cited by 15 publications

(6 citation statements)

References 28 publications

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“…The air inlet pressure level therefore remained between 3 and 15 mmHg. Similar conclusions were mentioned by Singh et al [21], Beravala and Pandey [22], and Srivastava and Pandey [23]. In this work during machining, the tool was immersed in kerosene dielectric and compressed air was provided in the gap directly through a multi-hole rotary tool.…”

Section: Description Of the Experiments Processsupporting

confidence: 80%

Intelligent hybrid approaches for ensuring better prediction of gas-assisted EDM responses

et al. 2020

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The present research work explores the implementation of three smart hybrid predictive models based on the adaptive neuro-fuzzy inference system (ANFIS), ANFIS and genetic algorithm (GA), and ANFIS and particle swarm optimization (PSO). All such strategies have been used to determine and compare machining key elements including material removal rate (MRR) and surface roughness (SR) during the gas-assisted electrical discharge (GAEDM) process. In this study, inert gas-based EDM with a multi-hole rotating tool has been carried out. In this experimentation, pulse-on time, peak current, duty cycle, electrode rotation, and gas discharge pressure were selected as input factors. The proposed method is to upgrade ANFIS with GA and PSO techniques. The GA and PSO algorithms are used to enhance the accuracy of the ANFIS model. The models have been trained, tested, and validated with observational results. Statistical techniques were applied to assess the effectiveness of the predictive capability models established through the ANFIS, ANFIS-GA, and ANFIS-PSO techniques. The actual and predicated estimates of MRR and SR of the GAEDM, obtained by ANFIS, ANFIS-GA, and ANFIS-PSO, were observed to be as per one another. In addition, the ANFIS-PSO framework proved to be even more responsive when compared with the ANFIS and the ANFIS-GA system. In particular, the assertion of this work is that modified algorithms such as ANFIS-GA and ANFIS-PSO are an efficient and productive approach to accurate EDM response estimation.

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Section: Description Of the Experiments Processsupporting

confidence: 80%

Intelligent hybrid approaches for ensuring better prediction of gas-assisted EDM responses

et al. 2020

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“…Similarly, data set (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) are entered and weights are readjusted. The data set (19)(20)(21)(22)(23)(24)(25)(26)(27) are used to test the performance of the network. It has been observed that after 31,250 iterations, the network achieves a satisfactory level of error as shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…To solve the single response optimization problem of Taguchi approach, Liao [23] proposed an effective method named as PCR-TOPSIS which is a combination of process capability ratio (PCR) and TOPSIS theories. Singh et al [24] used the adaptive neuro-fuzzy inference system for predictive analysis of EDM responses. The MRR and Ra in the EDM process were optimized using ANN with PSO [25].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy based multi-response optimization: a case study on EDM machining process

Sahu

Shrivastava

2021

SN Appl. Sci.

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The current study challenges the multi-objective optimization of electric discharge machining (EDM) parameters. EDM is used for creating profiles by machining of workpiece that are difficult to machine by conventional method. In the current work four responses such as material removal rate (production rate), tool wear rate, surface roughness (quality) and circularity (profile) are collectively investigated with varying controlling parameters. The human decision for best combination of controlling parameters for highest performance has uncertainties, which results in inferior solution. The multiple responses along with uncertainties and impreciseness can be addressed by combining a neuro-fuzzy system with particle swarm optimization (PSO). To illustrate the superiority of the proposed approach a set of experiment have been conducted in EDM process using AISI D2 tool steel as workpiece and brass tool. The experimental plan was made according to the Box-Behnken response surface methodology design with four process parameters namely discharge current, pulse-on-time, duty factor, and flushing pressure. The four response parameters such as material removal rate, tool wear rate, surface roughness, and circularity of machined components were optimized simultaneously. One unique Multi-response Performance Characteristic Index was obtained by combining the four responses using the proposed neuro-fuzzy technique. A regression model was developed on single response and optimized by PSO to obtain the optimal parameter setting. An experiment was conducted on optimal parameter to test the optimum performance. It is observed that the EDM responses were affected significantly by discharge current and pulse-on-time. The increase in pulse-on-time leads to larger surface cracks and more micro-pores on the machined surface. Article Highlights RSM was proven to be an effective statistical tool for reducing the experimental runs, and also establishes the relation between multiple inputs and single output. The neuro-fuzzy system combined with PSO results a suitable model to convert multiple response into an equivalent single response. The presented approach can be a practical method for situations where multiple conflicting objectives are needed to be optimized at the same time.

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“…Population-based metaheuristic algorithms, particularly GA, are widely used as the training algorithm to construct the most reliable and robust ANFIS networks for complex machining processes through optimizing their premise and consequent parameters by probability-based search strategies. The GA-ANFIS hybrid approach has been extensively used in the modeling of several machining processes such as drilling [ 35 ], electrical discharge machining [ 36 ], and milling [ 37 , 38 ]. However, the modeling of PAC parameters using a GA-ANFIS integrated approach has not been dealt with in the literature.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy

et al. 2021

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This paper focusses on a hybrid approach based on genetic algorithm (GA) and an adaptive neuro fuzzy inference system (ANFIS) for modeling the correlation between plasma arc cutting (PAC) parameters and the response characteristics of machined Monel 400 alloy sheets. PAC experiments are performed based on box-behnken design methodology by considering cutting speed, gas pressure, arc current, and stand-off distance as input parameters, and surface roughness (Ra), kerf width (kw), and micro hardness (mh) as response characteristics. GA is efficaciously utilized as the training algorithm to optimize the ANFIS parameters. The training, testing errors, and statistical validation parameter results indicated that the ANFIS learned by GA outperforms in the forecasting of PAC responses compared with the results of multiple linear regression models. Besides that, to obtain the optimal combination PAC parameters, multi-response optimization was performed using a trained ANFIS network coupled with an artificial bee colony algorithm (ABC). The superlative responses, such as Ra of 1.5387 µm, kw of 1.2034 mm, and mh of 176.08, are used to forecast the optimum cutting conditions, such as a cutting speed of 2330.39 mm/min, gas pressure of 3.84 bar, arc current of 45 A, and stand-off distance of 2.01 mm, respectively. Furthermore, the ABC predicted results are validated by conducting confirmatory experiments, and it was found that the error between the predicted and the actual results are lower than 6.38%, indicating the adoptability of the proposed ABC in optimizing real-world complex machining processes.

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Integration of GA and neuro-fuzzy approaches for the predictive analysis of gas-assisted EDM responses

Cited by 15 publications

References 28 publications

Intelligent hybrid approaches for ensuring better prediction of gas-assisted EDM responses

Intelligent hybrid approaches for ensuring better prediction of gas-assisted EDM responses

Fuzzy based multi-response optimization: a case study on EDM machining process

A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy

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