论文信息 - Research On Intelligent Evaluation Method For Machining State Oriented To Process Quality Control

Research On Intelligent Evaluation Method For Machining State Oriented To Process Quality Control

The dynamic control of process quality is of great significance to improve the intellectualization of manufacturing process. The real-time monitoring and evaluation of machining state provides support for the intelligent control of process quality. In view of the timevarying, coupling and dynamic characteristics of monitoring parameters, as well as the real-time dynamic correlation and nonlinear relationship between the processing state and the product quality, this paper uses the Stacked Auto-encoder (SAE) to optimize the multidimensional real-time monitoring parameters in the machining process. By using the hybrid model of SAE-BP neural network, the nonlinear mapping relation between multidimensional monitoring parameters and the processing state is characterized adaptively and the dynamic intelligent evaluation of the machining state in the intelligent manufacturing process is realized. Taking an experimental platform as an example, the validity of the dynamic intelligent evaluation of the SAE-BP neural network used in processing state is verified. The proposed method provides support for the real-time dynamic evaluation of machining state.

[1] Yaguo Lei,et al. A Deep Learning-based Method for Machinery Health Monitoring with Big Data , 2015 .

[2] Robert B. Randall,et al. THE RELATIONSHIP BETWEEN SPECTRAL CORRELATION AND ENVELOPE ANALYSIS IN THE DIAGNOSTICS OF BEARING FAULTS AND OTHER CYCLOSTATIONARY MACHINE SIGNALS , 2001 .

[3] N. A. O. Demerdash,et al. Diagnostics of Bar and End-Ring Connector Breakage Faults in Polyphase Induction Motors through a Novel Dual Track of Time-Series Data Mining and Time-Stepping Coupled FE-State Space Modeling , 2002, IEEE Power Engineering Review.

[4] Liao Ming-fu. Study on grading of health condition of aerospace propulsion system , 2008 .

[5] Bin Guang-fu,et al. Quantitative system evaluation method for equipment state using fuzzy and analytic hierarchy process , 2010 .

[6] David He,et al. Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis , 2007, Eur. J. Oper. Res..

[7] Dong,et al. Real-time Health Condition Evaluation on Wind Turbines Based on Operational Condition Recognition , 2013 .

[8] Ming Yin,et al. Healthy Assessment Research Electronic Equipment Based on Data Fusion , 2015 .

[9] Nagi Gebraeel,et al. Residual life predictions from vibration-based degradation signals: a neural network approach , 2004, IEEE Transactions on Industrial Electronics.