End wear compensation during planetary EDM of Ti–6Al–4V by adaptive neuro fuzzy inference system

Mathai, Vishal John; Dave, Harshit K.; Desai, Keyur P.

doi:10.1007/s11740-017-0778-8

Cited by 10 publications

(7 citation statements)

References 23 publications

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“…other. The neural components have an ability to acquire knowledge, separate data and prepared for use [20]. They have been referenced to have an ability to practice like a human, by social event data along over and over learning interest.…”

Section: Prediction Of Mrr and Sr In Gaedm By Annmentioning

confidence: 99%

“…ANFIS is a hybrid model which coordinates ANN's versatile potential and fuzzy logic's rationale's subjective strategies. ANFIS exploit the competency of the ANN and fuzzy rationale and at the same time beat their particular constraints [20,22]. Since the goal of this examination is to foresee the GAEDM responses, in the present examination, the ANFIS system was connected to build up the relationship between input factors and output response; For the ANFIS modeling, it is compulsory that all the procedure input parameters ought to be quantitative.…”

Section: Prediction Of Mrr and Sr In Gaedm By Anfismentioning

confidence: 99%

“…Figure 7 shows that there is a good accord between the measured values of the MRR and the SR as well as the predicted values through the ANFIS model. The precision of the prediction model was evaluated by using the rootmean-square error (RMSE) [17,20].…”

Section: Prediction Of Mrr and Sr In Gaedm By Anfismentioning

confidence: 99%

“…where X is the number of output nodes, Y is the total number of training data, p j is output of the jth neuron, and q j is the predicted value of jth neuron [20]. where N is the total training data, X i is the value of the measured data, and Y i is the value predicted by the ANN, ANFIS and ANFIS-GA models.…”

Section: Anfis Trained By Gamentioning

confidence: 99%

“…The finding suggested that GRNNbased model predicts the responses with more accuracy in comparison with MRA-based model. Mathai et al [20] developed a model using ANFIS to compensate the end wear of the tool during the planetary EDM. The finding uncovers that model is proficient to anticipate precisely the cavity dimension when input machining parameters are given.…”

Section: Introductionmentioning

confidence: 99%

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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: Prediction Of Mrr and Sr In Gaedm By Annmentioning

confidence: 99%

Section: Prediction Of Mrr and Sr In Gaedm By Anfismentioning

confidence: 99%

Section: Prediction Of Mrr and Sr In Gaedm By Anfismentioning

confidence: 99%

Section: Anfis Trained By Gamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Prediction of EDMed micro-hole quality characteristics using hybrid bio-inspired machine learning-based predictive approaches

Rao

2022

Int J Interact Des Manuf

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Friction stir welding process parameters optimization and weld structure analyses to study mechanical characteristics using hybrid artificial intelligent soft computing techniques

Rana,

Hooda,

Kumar

et al. 2024

Int J Interact Des Manuf

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Friction stir welding (FSW) is a solid-state fusion welding technique enormously used for welding different aluminum alloy plates. It involves a number of input parameters that significantly affect the different mechanical characteristics of the prepared weld joints. In this study, an attempt is made to optimize the input process parameters of FSW and the diverse mechanical characteristics of the resulting weld. The analysis of ultimate tensile strength (UTS), hardness, and yield strength (YS) is conducted following the test procedures outlined in the American Society for Testing and Materials (ASTM) standards E18-15 and E8-M04. The test specimens are prepared using two different aluminium alloy plates (AA6061-T6 and AA5083-H111). The response surface methodology-based face-centered central composite design (FCCD) strategy is used to develop the experimental design matrix. The input parameters values are varied at three levels. To predict more précised and optimized results, an adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA), in combination, is exercised by utilizing the Matlab tool. Using field emission scanning electron microscopy (FESEM) images, structural analysis is performed to study behavior at the weld joint area. After conducting the tensile and hardness test, the maximum experimental value of UTS, hardness, and YS are noticed as 325 MPa, 69 HRB, and 265 MPa, respectively. By using the GA-ANFIS hybrid tool, the maximum value of UTS and hardness is noticed as 326.99 MPa and 71.40 HRB, respectively, using triangular TPP with 30.5 mm/minute welding speed and 1200 rpm tool pin profile. The optimized value of YS is observed as 271.56 MPa using cylindrical TPP with 24 mm/minute welding speed and 1600 rpm tool rotational speed. In the present work, FESEM-based images reveal a fine-grain structure in the stir zone for the optimized values of mechanical characteristics. The results obtained using the hybrid soft computing technique GA-ANFIS showed that these techniques are adequate tools for the optimization of FSW input process parameters and mechanical characteristics.

show abstract

End wear compensation during planetary EDM of Ti–6Al–4V by adaptive neuro fuzzy inference system

Cited by 10 publications

References 23 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

Prediction of EDMed micro-hole quality characteristics using hybrid bio-inspired machine learning-based predictive approaches

Friction stir welding process parameters optimization and weld structure analyses to study mechanical characteristics using hybrid artificial intelligent soft computing techniques

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