Application of GRNN and multivariate hybrid approach to predict and optimize WEDM responses for Ni-Ti shape memory alloy

Majumder, Himadri; Maity, Kalipada

doi:10.1016/j.asoc.2018.06.026

Cited by 72 publications

(32 citation statements)

References 58 publications

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“…In this study, after finding the statistical relationship between the daily number of new cases and weather factors, we also forecast the number of new cases in Europe's five most affected countries by neural network methods. The method that we implement in this research study is the GRNN, which uses a feedforward network with fast training ability (Bărbulescu, 2018;Majumder & Maity, 2018). As we discussed above, some studies about SARS-CoV-1 (Bai & Jin, 2005;Hsieh et al, 2004;Lai, 2005;Wang & Ruan, 2004) found that this type of virus follows the Gaussian or Exponential distribution.…”

Section: Neural Network Analysismentioning

confidence: 99%

Estimation and demographic analysis of COVID-19 infections with respect to weather factors in Europe

Ahangar

Pavur

Fathi

et al. 2020

Journal of Business Analytics

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The main objective of this study is to investigate the relationship between the COVID-19 and the weather factors of the most populated and industrialised countries in Europe and propose the best mathematical model to forecast the daily number of COVID-19 cases. To find the relationship between the COVID-19 and the weather factors of absolute humidity and temperature in Spain, France, Italy, Germany, and the United Kingdom, we conducted a Poisson analysis. We also used the General Linear Neural Network (GRNN) model to forecast the trend and number of daily COVID-19 cases in these European countries. The results reveal a statistically significant negative relationship between the number of COVID-19 infections and weather factors of temperature & absolute humidity. Furthermore, the results show a stronger negative relationship between COVID-19 and absolute humidity than temperature. In our proposed GRNN method, we find better compatibility for the COVID-19 cases in Italy relative to the other European countries in this study.

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Section: Neural Network Analysismentioning

confidence: 99%

Estimation and demographic analysis of COVID-19 infections with respect to weather factors in Europe

Ahangar

Pavur

Fathi

et al. 2020

Journal of Business Analytics

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“…In another study by Soni et al [18], WEDM machining of Ti50Ni40CO10 SMA has been explored. The final result revealed formation of microcracks can be avoided and recast layer thickness can be reduced by setting pulse on time lower than 125 µs and servo voltage larger than 20 V. Majumder and Maity [19] conducted a similar study wherein microhardness (MH) and SR were considered as output response variables and they are optimized with the help of a fuzzy technique for the SMA alloy Ni55Ti45. T on was identified as the main significant input process parameter as compared to other input variables.…”

Section: Introductionmentioning

confidence: 99%

Multi-Response Optimization of WEDM Process Parameters for Machining of Superelastic Nitinol Shape-Memory Alloy Using a Heat-Transfer Search Algorithm

et al. 2019

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Nitinol, a shape-memory alloy (SMA), is gaining popularity for use in various applications. Machining of these SMAs poses a challenge during conventional machining. Henceforth, in the current study, the wire-electric discharge process has been attempted to machine nickel-titanium (Ni55.8Ti) super-elastic SMA. Furthermore, to render the process viable for industry, a systematic approach comprising response surface methodology (RSM) and a heat-transfer search (HTS) algorithm has been strategized for optimization of process parameters. Pulse-on time, pulse-off time and current were considered as input process parameters, whereas material removal rate (MRR), surface roughness, and micro-hardness were considered as output responses. Residual plots were generated to check the robustness of analysis of variance (ANOVA) results and generated mathematical models. A multi-objective HTS algorithm was executed for generating 2-D and 3-D Pareto optimal points indicating the non-dominant feasible solutions. The proposed combined approach proved to be highly effective in predicting and optimizing the wire electrical discharge machining (WEDM) process parameters. Validation trials were carried out and the error between measured and predicted values was negligible. To ensure the existence of a shape-memory effect even after machining, a differential scanning calorimetry (DSC) test was carried out. The optimized parameters were found to machine the alloy appropriately with the intact shape memory effect.

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“…The finding suggested that ANFIS model outperformed the polynomial model in the context of precision and accuracy of prediction. Majumder and Maity [19] developed a general regression neural network (GRNN) and multiple regression analysis (MRA)-based model to predict the responses of wire electrical discharge machining process. The finding suggested that GRNNbased model predicts the responses with more accuracy in comparison with MRA-based model.…”

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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Application of GRNN and multivariate hybrid approach to predict and optimize WEDM responses for Ni-Ti shape memory alloy

Cited by 72 publications

References 58 publications

Estimation and demographic analysis of COVID-19 infections with respect to weather factors in Europe

Estimation and demographic analysis of COVID-19 infections with respect to weather factors in Europe

Multi-Response Optimization of WEDM Process Parameters for Machining of Superelastic Nitinol Shape-Memory Alloy Using a Heat-Transfer Search Algorithm

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

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