), 郑小虎 东华大学 机械工程学院, 上海 201620
收稿日期:2021-05-21出版日期:2022-01-28发布日期:2022-01-21通讯作者:鲍劲松E-mail:bao@dhu.edu.cn作者简介:沈 慧(1998-),女,安徽省铜陵市人,硕士生,主要从事数字化制造,数字孪生技术研究.基金资助:国家自然科学基金资助项目(51805079)Adaptive Transferring Method of Digital Twin Model for Machining Domain
SHEN Hui, LIU Shimin, XU Minjun, HUANG Delin, BAO Jingsong(), ZHENG Xiaohu College of Mechanical Engineering, Donghua University, Shanghai 201620, China
Received:2021-05-21Online:2022-01-28Published:2022-01-21Contact:BAO Jingsong E-mail:bao@dhu.edu.cn摘要/Abstract
摘要： 在多品种小批量生产制造车间中,针对特定场景所建立的数字孪生模型,由于缺乏工况变化的自适应能力,导致加工质量预测精度不足.对此,提出一种数字孪生模型自适应迁移方法.通过搭建可迁移的数字孪生模型,实现机理和算法模型融合的加工质量在线预测;提出数字孪生模型迁移流程和迁移策略,基于特征数据分析计算,选择待迁移的源模型;同时,结合迁移学习理论实现简单和复杂变工况下的数字孪生模型迁移.以钻削加工为例,搭建钻削实验平台并对数字孪生模型迁移的可行性进行验证.研究结果表明,变化工况下,迁移后模型仍能保持预测误差低于1.5%.该方法为提高数字孪生模型自适应能力提供了新的思路.
关键词: 数字孪生, 质量预测, 工况变化, 迁移学习
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
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