Digital twin-driven clamping force control for thin-walled parts. (January 2022)

• Record Type: Journal Article
• Authors: Wang, Gang; Cao, Yansheng; Zhang, Yingfeng
• Is Part Of: Advanced engineering informatics. Volume 51(2022)
• Publication Date: 2022-01
• Subjects: Clamping force control -- Digital twin -- Clamping deformation -- Finite element simulation -- Deep neural network algorithm; TP Thin-walled part -- FMS Finite element simulation -- GA Genetic algorithm -- DNN Deep neural networks -- IOT Internet of Things -- PCS Point cloud scanning -- CPS Cyber-physical systems -- GM Geometrical model -- FESM Finite element simulation model -- CFOP Clamping force optimization model; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2021.101468
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Clamping quality is one of the main factors that will affect the deformation of thin-walled parts during their processing, which can then directly affect parts' performance. However, traditional clamping force settings are based on manual experience, which is a random and inaccurate manner. In addition, dynamic clamping force adjustment according to clamping deformation is rarely considered in clamping force control process, which easily causes large clamping deformation and low machining accuracy. To address these issues, this study proposes a digital twin-driven clamping force control approach to improve the machining accuracy of thin-walled parts. The total factor information model of clamping system is built to integrate the dynamic information of the clamping process. The virtual space model is constructed based on finite element simulation and deep neural network algorithm. To ensure bidirectional mapping of physical-virtual space, the workflow of clamping force control and interoperability method between digital twin models are elaborated. Finally, a case study is used to verify the effectiveness and feasibility of the proposed method.