A hybrid Taguchi-artificial neural network approach to predict surface roughness during electric discharge machining of titanium alloys

Kumar, Sanjeev; Batish, Ajay; Singh, Rupinder; Singh, T. P.

doi:10.1007/s12206-014-0637-x

Cited by 76 publications

(30 citation statements)

References 32 publications

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“…Panda [7] optimized the process parameters used during the EDM process using neuro-gray modeling considering output responses like SR, MRR and hardness of the specimens. Extensive research attempts have been made to apply ANN coupled with various modeling approaches, and Kumar et al [8] connected ANN combined with Taguchi strategy for demonstrating and streamlining of surface finish. Kumar and Choudhury [9] utilized ANN strategies to decide the SR and wheel wear during electrical discharge diamond grinding (EDDG) of steel (HSS).…”

Section: Introductionmentioning

confidence: 99%

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

Singh

Kumar

et al. 2019

SN Appl. Sci.

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This research work discusses the application of three intelligent prediction models, based on artificial neural network (ANN) with back-propagation algorithm, adaptive neuro-fuzzy inference system (ANFIS) and hybrid ANFIS and genetic algorithm (ANFIS-GA). These techniques are used for prediction and comparison of machining aspects such as material removal rate (MRR) and surface roughness during gas-assisted electrical discharge machining of D3 die steel. In the present work, helium-assisted EDM with perforated tool has been performed. In this work, parameters considered for machining are discharge current, pulse on time, duty cycle, tool rotation and discharge gas pressure. The suggested approach is based on up-gradation of ANFIS with GA. The GA algorithm is applied to improve the precision of the ANFIS model. The soft computing models were trained, tested and validated with experimental data. Mean square error (MSE), mean absolute error (MAE), root-mean-square error and correlation coefficient (R 2), were used to measure the efficacy of models predicting abilities developed through ANN, ANFIS and hybrid ANFIS-GA approaches. The experiment and anticipated measure of MRR and SR of the process, acquired by ANN, ANFIS and hybrid ANFIS-GA, was found to be in good agreement. The prediction potential of proposed models was tested using new set of data for the training and testing process. The ANFIS-GA technique provides more accurate prediction of the responses in comparison with the ANN and the ANFIS. In general, the inference of this work discloses that the hybrid algorithm like ANFIS-GA is an efficient and effective approach for precise prediction of EDM process responses.

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Section: Introductionmentioning

confidence: 99%

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

Singh

Kumar

et al. 2019

SN Appl. Sci.

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“…Panda [15] used neuro-grey modeling approach for optimization of process responses, such as depth of heat-affected zone, SR, MRR and micro-hardness of machined surface. Kumar et al [16]…”

Section: Literature Review In Correlation Of Soft Computing Model Devmentioning

confidence: 99%

“…ANN is the most widely used soft computing technique to unravel complex nonlinear problems. This technique offers a flexibility of learning the mapping between the input factors and the process responses to sort out complicated problems [16]. The neural network consists of immensely interconnected neural computing elements.…”

Section: Prediction Of Mrr and Sr In Haedmmentioning

confidence: 99%

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Comparative study of statistical and soft computing-based predictive models for material removal rate and surface roughness during helium-assisted EDM of D3 die steel

Singh

Kumar

et al. 2019

SN Appl. Sci.

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This research work proposes mathematical models, based on artificial neural network (ANN) with back-propagation algorithm, adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM), for prediction of material removal rate (MRR) and surface roughness (SR) of helium-assisted electrical discharge machining of D3 die steel. The helium gas-assisted die-sinking EDM with perforated electrode was carried out by an EDM machine. For the present experimental work, discharge current, pulse on time, duty cycle, electrode rotation and discharge gas pressure were selected as process factors, while MRR and SR were chosen as process responses. Analysis of variance (ANOVA) was done to examine the adequacy of the developed model. The fit summary confirmed that the quadratic model is statistically appropriate and the lack of fit is insignificant. Root mean square error and absolute standard deviation, obtained through RSM, were also used for developing the model and for its predicting abilities through ANN and ANFIS. The experimental and predicted values of MRR and SR during the process, obtained by RSM, ANN and ANFIS, were found to be in accord with each other. However, the ANFIS technique proved to be more fitting to the responses as compared to the ANN and the RSM. The optimum value of the MRR at 28.54 mg/min and the SR at 4.21 µm was obtained with optimal process parameters by optimization of developed statistical models using genetic algorithm.

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“…Recent developments in the field of materials synthesis and characterizations has largely benefited by the machine learning techniques for the non-linear problems of physical and mechanical properties of composite materials [1]. Presently, there has been a rapid growth in studying and using sandwich composites (SWC's) in bringing down the weight of the dynamic structural (e.g.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Core Shear Strength in Sandwich Composites using Deep Learning and Support Vector Regression

Antony¹,

Prajna²,

Jnanesh³

2019

IJRTE

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In the present study, machine learning approaches have been developed to predict the 180 days aged core shear strength of sandwich composites. The characteristics of the sandwich composites depends on the number of factors namely fibre type i.e., Chopped strand Mat, Stitched, Chopped strand Mat and Woven Roving, core density, bond between the core and the face sheets and the ability to bear the load in flexural mode. In the current approach deep learning and SVR models were worked out by taking on six different parameters namely foam density, aging temperature and variety of fiber types as input variables. For each set of these input variables, the 180 days aged shear strength of sandwich composites with a test frequency of 30 days was determined. The paper aims at predicting the core shear strength value of stitch bond sandwich composites using other three aforementioned fibers. To create the model and confirm the accuracy of the algorithm training and test data are considered. The results obtained revealed that the deep learning model develo ped provides better predictive ability than the model of SVR.

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A hybrid Taguchi-artificial neural network approach to predict surface roughness during electric discharge machining of titanium alloys

Cited by 76 publications

References 32 publications

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

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

Comparative study of statistical and soft computing-based predictive models for material removal rate and surface roughness during helium-assisted EDM of D3 die steel

Prediction of Core Shear Strength in Sandwich Composites using Deep Learning and Support Vector Regression

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