Adaptive Transferring Method of Digital Twin Model for Machining Domain

doi:10.16183/j.cnki.jsjtu.2021.167

Abstract

Abstract:

In the multi-variety and small batch manufacturing workshop, digital twin model is mostly established for specific scenarios. Due to its lack of adaptive ability under working conditions, the prediction accuracy of machining quality is insufficient. To solve this problem, an adaptive transferring method of the digital twin model is proposed. By building the transferable digital twin model, the online prediction of machining quality based on the fusion of mechanism and algorithm model is realized. The transferring process and strategy of the digital twin model are proposed. Based on the analysis and calculation of characteristic data, the source model to be transferred is selected. At the same time, in combination with the transfer learning theory, the transfer of digital twin models is realized under simple and complex changing conditions. Taking drilling as an example, the drilling experiment platform is built and the feasibility of digital twin model transfer is verified. The results show that the model can keep the mean absolute error of prediction less than 1.5% under changing working conditions. This method provides a new idea to improve the adaptive ability of digital twin models.

Key words: digital twin, quality prediction, change of working conditions, transfer learning

CLC Number:

TG525

SHEN Hui, LIU Shimin, XU Minjun, HUANG Delin, BAO Jingsong, ZHENG Xiaohu. Adaptive Transferring Method of Digital Twin Model for Machining Domain[J]. Journal of Shanghai Jiao Tong University, 2022, 56(1): 70-80.

Figures/Tables 13

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序号	模型种类	MAE/%
1	迁移前模型	4.54
2	迁移后模型	0.92
3	特征数据训练模型	6.39

序号	模型种类	MAE/%
1	迁移前模型	3.95
2	迁移后模型	1.49
3	特征数据训练模型	6.73