Augmented Functional Analysis of Variance (A-fANOVA): Theory and Application to Google Trends for Detecting Differences in Abortion Drugs Queries

Maturo, Fabrizio; Porreca, Annamaria

doi:10.1016/j.bdr.2022.100354

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…The main processing parameters include processing polarity, interelectrode voltage, peak current, pulse frequency, pulse width, and tool electrode and workpiece electrode gap. 28,29 Among the above parameters selected in this test, the polarity parameters are two levels (positive and negative polarities), and the other five parameters are three levels. The design of a mixed 2--3 level L 18 orthogonal array containing 6 columns and 18 rows, namely L 18 (2 1 × 3 5 ), can significantly reduce the number of tests.…”

Section: Experimental Design and Resultsmentioning

confidence: 99%

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Performance prediction and parameter optimization of alumina-titanium carbide ceramic micro-EDM hole machining process based on XGBoost

Chen

Cheng

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part L:

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Aiming at the difficulties in setting process parameters and the low accuracy of process performance prediction in electrical discharge machining (EDM) hole machining of alumina ceramics, a novel EDM process performance prediction method based on eXtreme Gradient Boosting (XGBoost) is proposed in this study. The independent variables selected include processing polarity (P), interelectrode voltage (V), peak current (I), pulse frequency (F), pulse width (T), and tool electrode and workpiece electrode gap (W), while the dependent variables are tool electrode length loss (ELW), tool electrode volume loss (EVW), and material removal rate (MRR). An L18 (21 × 35) orthogonal test is designed to obtain training samples for model development. The accuracy of the prediction results for ELW, EVW, and MRR by the XGBoost model is found to be 1.7%, 2.5%, and 9.4%, respectively. These results show a significant improvement compared to the linear regression model, with an improvement of 41.7%, 62.5%, and 19.4%, respectively. Furthermore, when compared to the support vector regression (SVR) model, the XGBoost model also shows improvement of 23.6%, 5.9%, and 12.0% for ELW, EVW, and MRR prediction accuracy, respectively. These findings suggest that the proposed XGBoost-based method is effective and accurate in predicting the performance of alumina ceramic EDM processes. In addition, the third generation nondominated sorting genetic algorithm (NSGA-III) is utilized to optimize the process parameters with both single objective and multi-objective approaches. The optimal parameters for ELW, EVW, and MRR, including the selection of machining polarity, interelectrode voltage, peak current, pulse frequency, pulse width, and tool electrode and workpiece electrode gap are obtained through the Pareto frontier of the three comprehensive optima. The average relative errors between the experimental results and the optimized results are found to be 6.46%, 10.45%, and 9.58% for ELW, EVW, and MRR, respectively, indicating the accuracy and effectiveness of the optimized results.

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Section: Experimental Design and Resultsmentioning

confidence: 99%

“…The main processing parameters include processing polarity, interelectrode voltage, peak current, pulse frequency, pulse width, and tool electrode and workpiece electrode gap. 28,29…”

Section: Orthogonal Testmentioning

confidence: 99%

Performance prediction and parameter optimization of alumina-titanium carbide ceramic micro-EDM hole machining process based on XGBoost

Chen

Cheng

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part L:

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“…The preconditions for applying variance analysis to samples are independence (normality of each experiment is carried out independently); Normality (each sample comes from a normally distributed population); Homogeneity of variances (equal population variances). 29 Research shows that the results of F test are less affected by samples not obeying normal distribution. In practical application, homogeneity of variance is more important than normality, so it is important to test the differential homogeneity.…”

Section: Experimental Data Hypothesis Testingmentioning

confidence: 94%

Experimental study on micro electrical discharge machining of alumina–titanium carbide ceramic materials

Chen

Cheng

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part L:

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When electric discharge machining (EDM) is used to process Al2O3-TiC ceramics, it is difficult to obtain an optimal production efficiency and tool wear rate. As the target demand of processing largely relies on the process parameters, it is pertinent to conduct an in-depth study on the process parameters. In this study, the effects of certain technological parameters on the properties of EDM Al2O3-TiC ceramic materials were assessed and a mixed 2–3 horizontal orthogonal test method was designed. The rules of machining polarity, inter-electrode voltage, peak current, gain, pulse frequency, pulse width, inter-electrode gap and electrode length loss, electrode profile loss, material removal rate were studied. The effect of process parameters on process performance was determined by range analysis. In addition, the analysis of variance revealed that machining polarity has a significant effect on the electrode loss while the electrode voltage and peak current significantly influenced the material removal rate. The optimal parameter combination was determined as negative machining polarity, 130 V inter-electrode voltage, 50 A peak current, 45 gain, 150 Hz pulse frequency, 1.0 μs pulse width, and 40 μs inter-electrode gap.

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Outliers detection in assessment tests’ quality evaluation through the blended use of functional data analysis and item response theory

2022

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Augmented Functional Analysis of Variance (A-fANOVA): Theory and Application to Google Trends for Detecting Differences in Abortion Drugs Queries

Cited by 7 publications

References 26 publications

Performance prediction and parameter optimization of alumina-titanium carbide ceramic micro-EDM hole machining process based on XGBoost

Performance prediction and parameter optimization of alumina-titanium carbide ceramic micro-EDM hole machining process based on XGBoost

Experimental study on micro electrical discharge machining of alumina–titanium carbide ceramic materials

Outliers detection in assessment tests’ quality evaluation through the blended use of functional data analysis and item response theory

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