A Data and Model Hybrid Driven Method for Multi-Feature Workpiece Processing Energy Consumption Prediction
The material removal rate is constantly changing during the actual machining process,and exist-ing modeling methods for energy consumption,which is regarded as a constant quantity,is difficult to accu-rately predict energy consumption.In order to improve the prediction accuracy of energy consumption in the cutting process,this paper proposes a digital and analog linkage machining energy consumption prediction method based on material removal rate.Firstly,based on the contact relationship between the cutting tool and the workpiece,the change law of material removal rate in the cutting,complete cutting and cutting out stages is analyzed,and the corresponding processing energy consumption characteristics are analyzed.Sec-ondly,a data-driven tool cut-in,cut-out stage machining energy consumption prediction method and model-driven machining energy consumption prediction method for the full cut-in stage are proposed to achieve accurate prediction of machining process energy consumption.Finally,experimental cases are used to verify the effectiveness of the proposed model and method,which lays a foundation for future research on the ac-curacy of energy consumption prediction.
data and model hybrid drivenmaterial removal ratemulti-feature workpieceprocessing ener-gy consumption prediction
NETL
NSTL
万方数据
维普
