![]() ![]() After adjustment for established outcome predictors, patients with MTPG ≥60 mm Hg had a significantly higher risk of mortality than patients with MTPG <60 mm Hg ( HR=1.71 P<0.001), even after adjusting for surgery as a time‐dependent variable ( HR=1.71 P<0.001). ![]() Patients with MTPG ≥60 mm Hg had a significantly increase risk of mortality compared with patients with MTPG <60 mm Hg (hazard ratio =1.62 P<0.001), even for the subgroup of asymptomatic or minimally symptomatic patients ( HR=1.56 P=0.032). The population was divided into 3 groups according to MTPG: between 40 and 49 mm Hg, between 50 and 59 mm Hg, and ≥60 mm Hg. Stroke: Vascular and Interventional NeurologyĪ total of 1143 patients with severe aortic stenosis defined by aortic valve area ≤1 cm 2 and MTPG ≥40 mm Hg were included.Journal of the American Heart Association (JAHA).Circ: Cardiovascular Quality & Outcomes.Arteriosclerosis, Thrombosis, and Vascular Biology (ATVB).This can not only realize the on-line intelligent prediction of the white layer phenomenon on the workpiece surface in hard turning, but also is of great significance to ensure the processing quality and improve the processing efficiency of products. Based on various sensor technologies, combined with gradient boosting decision tree, this method can effectively predict and identify the white layer phenomenon in hard turning. At the same time, compared with other model algorithms (SVM algorithm and XGBoost algorithm), the prediction accuracy based on the gradient boosting decision tree model can reach 90%, F1 score can reach 92% and Auc value can reach 89%, which can better reflect the white layer phenomenon. The model with acoustic emission signal characteristics greatly improved the accuracy, precision, recall, F1 value and Auc value. The experimental results showed that compared with the characteristics of power and vibration signal, the characteristics of acoustic emission signal were more sensitive to the white layer. The machining processing signal data was collected such as acoustic emission signal, three-way vibration signal and power signal for on-line prediction and identification of hard turning process then the microstructure test specimens were prepared for the machined parts to explore the relationship between the white layer and the above sensor signals. In order to verify the effectiveness of the above method, a hard turning experiment was carried out on the actual bearing products in the factory. Finally, based on the confusion matrix, a set of evaluation methods was proposed to ensure the prediction performance of the model. Thirdly, the grid search method was used to optimize the model parameters, reducing the dependence of the model on the amount of data and improving the accuracy of the model in predicting the white layer. Secondly, the time-domain parameters and wavelet packet energy parameters were extracted as the characteristic parameters to identify the white layer, and the influence degree of the above characteristic parameters was sorted by the feature importance analysis method, then the principal feature quantity related to the white layer was extracted by the PCA method as the input sample in the gradient boosting decision tree model. Firstly, the normal (without white layer) and abnormal (with white layer) signal data in the process of hard turning were collected by power sensor, acoustic emission sensor and vibration sensor. ![]() The method mainly includes signal data acquisition, feature extraction and analysis, prediction model construction and prediction result analysis. The work aims to propose a prediction method of white layer on workpiece surface in hard turning based on gradient lifting decision to realize the real-time on-line detection of white layer phenomenon for each product in hard turning process, and improve the production efficiency and processing quality of products. The current methods of detecting white layer not only affects the processing efficiency, but also fail meet the requirements on full detection of product parts. In the process of hard turning, the workpiece surface is easy to cause white layer, which affects the surface quality of the workpiece. University of Shanghai for Science and Technology, Shanghai, China Higher Vocational and Technical College, Shanghai University of Engineering Science, Shanghai, China KeyWord: white layer confusion matrix hard cutting intelligent prediction gradient boosting decision tree Surface White Layer Prediction Method of Hard Turning Based on Gradient Boosting Decision Tree ZHU Huan-huan,GE Ai-li,CHI Yu-lun,ZHANG Meng-meng,LI Hou-jia.Surface White Layer Prediction Method of Hard Turning Based on Gradient Boosting Decision Tree,52(2):328-342
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