State monitoring of machining process in small batch production mode based on recurrence analysis and machine learning
With the increasingly fierce competition in the market,the small batch production mode has become an im-portant production method.Aiming at the characteristics of small batch production and the problem that a large number of pre-experiments are required in existing research,a state monitoring method of machining process based on recurrence analysis and machine learning was proposed.The power signal of a small number of trials processing of different workpieces was collected through processing experiments;the preprocessed power data was input into the deep belief network for training,and the trained workpiece recognition model was obtained by optimizing genetic algorithm;the recurrence analysis and iterative self-organizing data analysis were performed to obtain the state mo-nitoring model.Finally,the case study verified the effectiveness of the condition monitoring method,in which the workpiece identification accuracy was 99.3％,and the condition monitoring accuracy was 98％.
small batchrecurrence analysisdeep belief networkiterative self-organizing data analysisstate moni-toring
万方数据
维普
