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Prediction of WEDM Performances Using Clustering Techniques in ANFIS During Machining of A286 Superalloy
https://orcid.org/http://orcid.org/0000-0003-3049-8084
Wire Electric Discharge Machining (WEDM) involves a high degree of nonlinearity and stochastic phenomena due to its complexity and process anisotropy. Thus, prediction of WEDM performances must be carried out through an efficient tool. The choice of data clustering technique in adaptive neuro-fuzzy inference system (ANFIS) is crucial since it has a substantial impact on prediction accuracy. To this end, this study investigates the effect of the choice of clustering algorithms [grid partitioning (GP) and subtractive clustering (SC)] on the performance of ANFIS while forecasting the WEDM performances such as material removal rate and surface roughness. Sensitivity analysis is carried out with the analysis of variance test. The predictive capability of the ANFIS-GP model is found to be superior to the ANFIS-SC model. The percentage of error plots are showcased to check the efficacy of the selected ANFIS-GP model for the responses. The parametric studies are conducted to portray the effect of process variables on responses.
Prediction of WEDM Performances Using Clustering Techniques in ANFIS During Machining of A286 Superalloy
https://orcid.org/http://orcid.org/0000-0003-3049-8084
Wire Electric Discharge Machining (WEDM) involves a high degree of nonlinearity and stochastic phenomena due to its complexity and process anisotropy. Thus, prediction of WEDM performances must be carried out through an efficient tool. The choice of data clustering technique in adaptive neuro-fuzzy inference system (ANFIS) is crucial since it has a substantial impact on prediction accuracy. To this end, this study investigates the effect of the choice of clustering algorithms [grid partitioning (GP) and subtractive clustering (SC)] on the performance of ANFIS while forecasting the WEDM performances such as material removal rate and surface roughness. Sensitivity analysis is carried out with the analysis of variance test. The predictive capability of the ANFIS-GP model is found to be superior to the ANFIS-SC model. The percentage of error plots are showcased to check the efficacy of the selected ANFIS-GP model for the responses. The parametric studies are conducted to portray the effect of process variables on responses.
Prediction of WEDM Performances Using Clustering Techniques in ANFIS During Machining of A286 Superalloy
https://orcid.org/http://orcid.org/0000-0003-3049-8084
Wire Electric Discharge Machining (WEDM) involves a high degree of nonlinearity and stochastic phenomena due to its complexity and process anisotropy. Thus, prediction of WEDM performances must be carried out through an efficient tool. The choice of data clustering technique in adaptive neuro-fuzzy inference system (ANFIS) is crucial since it has a substantial impact on prediction accuracy. To this end, this study investigates the effect of the choice of clustering algorithms [grid partitioning (GP) and subtractive clustering (SC)] on the performance of ANFIS while forecasting the WEDM performances such as material removal rate and surface roughness. Sensitivity analysis is carried out with the analysis of variance test. The predictive capability of the ANFIS-GP model is found to be superior to the ANFIS-SC model. The percentage of error plots are showcased to check the efficacy of the selected ANFIS-GP model for the responses. The parametric studies are conducted to portray the effect of process variables on responses.
Prediction of WEDM Performances Using Clustering Techniques in ANFIS During Machining of A286 Superalloy
J. Inst. Eng. India Ser. C
Saha, Subhankar (author) / Maity, Saikat Ranjan (author) / Dey, Sudip (author)
Journal of The Institution of Engineers (India): Series C ; 104 ; 315-326
2023-04-01
12 pages
Article (Journal)
Electronic Resource
English
The effect of applied strain range on the fatigue cracking in Nb-A286 iron-base superalloy
| British Library Online Contents | 2001
| British Library Online Contents | 2017