Processing Optimization of Shear Thickening Fluid Assisted Micro-Ultrasonic Machining Method for Hemispherical Mold Based on Integrated CatBoost-GA Model

Yin, Jianqin; Zhao, Jun; Song, Fengqi; Xu, Xinqiang; Lan, Yeshen

doi:10.3390/ma16072683

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…CatBoost incorporates all the settings needed to build separate decision trees and set up the random forest. The literature [41][42][43] can be referenced for further information about CatBoost.…”

Section: Categorical Boosting (Catboost)mentioning

confidence: 99%

Machine Learning Assessment of Damage Grade for Post-Earthquake Buildings: A Three-Stage Approach Directly Handling Categorical Features

Li,

Jia,

Chen

et al. 2023

Sustainability

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The rapid assessment of post-earthquake building damage for rescue and reconstruction is a crucial strategy to reduce the enormous number of human casualties and economic losses caused by earthquakes. Conventional machine learning (ML) approaches for this problem usually employ one-hot encoding to cope with categorical features, and their overall procedure is neither sufficient nor comprehensive. Therefore, this study proposed a three-stage approach, which can directly handle categorical features and enhance the entire methodology of ML applications. In stage I, an integrated data preprocessing framework involving subjective–objective feature selection was proposed and performed on a dataset of buildings after the 2015 Gorkha earthquake. In stage II, four machine learning models, KNN, XGBoost, CatBoost, and LightGBM, were trained and tested on the dataset. The best model was judged by comprehensive metrics, including the proposed risk coefficient. In stage III, the feature importance, the relationships between the features and the model’s output, and the feature interaction effects were investigated by Shapley additive explanations. The results indicate that the LightGBM model has the best overall performance with the highest accuracy of 0.897, the lowest risk coefficient of 0.042, and the shortest training time of 12.68 s due to its relevant algorithms for directly tackling categorical features. As for its interpretability, the most important features are determined, and information on these features’ impacts and interactions is obtained to improve the reliability of and promote practical engineering applications for the ML models. The proposed three-stage approach can provide a reference for the overall ML implementation process on raw datasets for similar problems.

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Section: Categorical Boosting (Catboost)mentioning

confidence: 99%

Machine Learning Assessment of Damage Grade for Post-Earthquake Buildings: A Three-Stage Approach Directly Handling Categorical Features

Li,

Jia,

Chen

et al. 2023

Sustainability

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“…Many studies have pointed out that ultrasonic vibration-assisted machining (UVM) technology and non-resonant vibration-assisted machining (NVM) technology can reduce force and surface damage by changing the motion path of the abrasive or tool [ 7 , 8 , 9 ]. Vibration-assisted machining technology based on ultrasonic vibration devices improves surface quality and surface integrity by making abrasive or tools hit the workpiece surface at high frequency [ 10 ]. This technique has received much attention [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Simulation and Experimental Study of Non-Resonant Vibration-Assisted Lapping of SiCp/Al

Zhao,

Gu,

et al. 2024

Micromachines

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SiCp/Al is a difficult-to-machine material that makes it easy to produce surface defects during machining, and researchers focus on reducing the surface defects. Vibration-assisted machining technology is considered an effective method to reduce surface defects by changing the trajectory and contact mode of the abrasive. Aiming at the problem of SiCp/Al processing technology, a vibration-assisted lapping device (VLD) is designed, and elliptical motion is synthesized by a set of parallel symmetrical displacement output mechanisms. The working parameters of the device were tested by simulation and experiment, and the lapping performance was verified. Then, the effects of removal characteristics and process parameters on surface roughness and lapping force were analyzed by simulation and experiment. Simulation and experimental results show that frequency and amplitude that are too low or too high are not conducive to the advantages of NVL. The best surface quality was 54 nm, obtained at A = 8 μm and f = 850 Hz.

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Numerical and experimental investigation on the material removal profile during polishing of inner surfaces using an abrasive rotating jet

Zhao,

Ge,

Khudoley

et al. 2024

Tribology International

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Processing Optimization of Shear Thickening Fluid Assisted Micro-Ultrasonic Machining Method for Hemispherical Mold Based on Integrated CatBoost-GA Model

Cited by 3 publications

References 40 publications

Machine Learning Assessment of Damage Grade for Post-Earthquake Buildings: A Three-Stage Approach Directly Handling Categorical Features

Machine Learning Assessment of Damage Grade for Post-Earthquake Buildings: A Three-Stage Approach Directly Handling Categorical Features

Simulation and Experimental Study of Non-Resonant Vibration-Assisted Lapping of SiCp/Al

Numerical and experimental investigation on the material removal profile during polishing of inner surfaces using an abrasive rotating jet

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