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公开(公告)号：US11879869B2
公开(公告)日：2024-01-23
申请号：US18352263
申请日：2023-07-14
发明人： Cong Ding , Shiqing Feng , Zhongyu Piao , Zhipeng Yuan , Jing Liu
摘要： Disclosed is a method for predicting surface quality of a burnishing workpiece. The method includes the steps: using vibration sensors and signal acquisition instrument to acquire vibration signals generated on a surface of the burnishing workpiece during machining, evaluating the surface quality of the burnishing workpiece based on a coupling coordination degree model, processing signals by using an ensemble empirical mode decomposition method, identifying power spectral density, kurtosis and form factor as signal characteristics, identifying a support vector machine as a decision-making model, optimizing penalty parameters and kernel function parameters by using the Bayesian optimization method, and establishing the relationship between the signal characteristics and the surface quality. The method can quickly identify the signal characteristics for evaluating the workpiece surface quality, thereby improving the workpiece surface quality by intervening in process parameters, making up for the technical defect that condition monitoring cannot be performed during the machining process.
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公开(公告)号：US20230366855A1
公开(公告)日：2023-11-16
申请号：US18352263
申请日：2023-07-14
发明人： Cong Ding , Shiqing Feng , Zhongyu Piao , Zhipeng Yuan , Jing Liu
摘要： Disclosed is a method for predicting surface quality of a burnishing workpiece. The method includes the steps: using vibration sensors and signal acquisition instrument to acquire vibration signals generated on a surface of the burnishing workpiece during machining, evaluating the surface quality of the burnishing workpiece based on a coupling coordination degree model, processing signals by using an ensemble empirical mode decomposition method, identifying power spectral density, kurtosis and form factor as signal characteristics, identifying a support vector machine as a decision-making model, optimizing penalty parameters and kernel function parameters by using the Bayesian optimization method, and establishing the relationship between the signal characteristics and the surface quality. The method can quickly identify the signal characteristics for evaluating the workpiece surface quality, thereby improving the workpiece surface quality by intervening in process parameters, making up for the technical defect that condition monitoring cannot be performed during the machining process.
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