Digital twin based dynamic prediction and simulation model of carbon efficiency in gear hobbing process

Abstract The transformation of manufacturing industry to green manufacturing is one of the important tasks to achieve the carbon peaking and carbon neutrality goals, which needs to improve the use efficiency of unit carbon emission. Aiming at the defect of static model in predicting accuracy and reflecting real-time machining state, a dynamic prediction and simulation model of carbon efficiency in hobbing process based on digital twin is proposed. After analyzing the dynamic characteristics of carbon emission during hobbing, three carbon efficiency targets are defined. Then a dynamic prediction and simulation model is constructed based on convolutional neural network and dynamic discrete event system specification. The framework of the carbon efficiency digital twin (CEDT) of the hobbing process is built, and the dynamic prediction and simulation models are integrated into CEDT as virtual models. Finally, the model is applied to a case study to verify the effectiveness of the method, and the influence of dynamic parameters is discussed. This model can realize the dynamic analysis of carbon emission in hobbing process, provide support for the real-time dynamic optimization of carbon efficiency in hobbing process, and help enterprises to realize green manufacturing.