An Intelligent Control Model Based on Digital Twin Technology and Optimized Least-Squares Support Vector Regression for Predicting Electromagnetic Brake Assembly Quality

Abstract

Electromagnetic (EM) brakes are widely applied in many automation and control fields. Assembly quality defects in EM brakes can significantly reduce braking performance and pose potential safety risks. The primary challenge in assembly quality control is the ability to predict product quality and prevent potential failures. In view of the lack of quality information interaction and real-time feedback in the traditional quality prediction process, an intelligent predictive quality control model for the assembly process of EM brakes is proposed in this paper. First, we introduce an assembly quality control model based on digital twin (DT) technology. Second, a data-driven quality prediction algorithm is developed using least-squares support vector regression (LSSVR) and improved particle swarm optimisation (IPSO). To reduce the complexity of the prediction process, grey relational analysis (GRA) is used to analyse and extract key quality characteristics data. EM friction brakes are employed as an example to analyse the product assembly process, and the results provide valuable insights into the implementation of DT technology. Finally, the feasibility of the developed prediction model is verified with real assembly datasets. The results demonstrate the effectiveness and precision of the DT model and the GRA-IPSO-LSSVR method in predicting assembly quality.

Keywords

• Digital twin
• particle swarm optimization
• predictive control
• quality control
• reliability engineering
• support vector machines