Polyaxial Rock Failure Criteria: Insights from Explainable and Interpretable Data-Driven Models

Fathipour‐Azar, Hadi

doi:10.1007/s00603-021-02758-8

Cited by 13 publications

(1 citation statement)

References 92 publications

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“…In recent years, machine-learning algorithms have been widely used as a data-driven modeling method in many fields, such as geotechnical engineering [ 27 ], traffic safety [ 28 ], material engineering [ 29 ], and biomedicine [ 30 ]. To improve computational accuracy and computational efficiency, Breima proposed the random forest (RF) algorithm in 2001 [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Both E-Jet Printing Ejection Cycle Time and Droplet Diameter Based on Random Forest Regression

et al. 2023

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Electrohydrodynamic jet (E-jet) printing has broad application prospects in the preparation of flexible electronics and optical devices. Ejection cycle time and droplet size are two key factors affecting E-jet-printing quality, but due to the complex process of E-jet printing, it remains a challenge to establish accurate relationships among ejection cycle time and droplet diameter and printing parameters. This paper develops a model based on random forest regression (RFR) for E-jet-printing prediction. Trained with 72 groups of experimental data obtained under four printing parameters (voltage, nozzle-to-substrate distance, liquid viscosity, and liquid conductivity), the RFR model achieved a MAPE (mean absolute percent error) of 4.35% and an RMSE (root mean square error) of 0.04 ms for eject cycle prediction, as well as a MAPE of 2.89% and an RMSE of 0.96 μm for droplet diameter prediction. With limited training data, the RFR model achieved the best prediction accuracy among several machine-learning models (RFR, CART, SVR, and ANN). The proposed prediction model provides an efficient and effective way to simultaneously predict the ejection cycle time and droplet diameter, advancing E-jet printing toward the goal of accurate, drop-on-demand printing.

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