Integration of Digital Twin and Machine Learning for Geometric Feature Online Inspection System

Abstract

The effective control of the welding quality can ensure the overall performance of the product. For the geometric feature online inspection system of welding process, this paper used digital twin technology to realize the mapping integration between physical and virtual workshop. The paper also proposed a control chart pattern recognition method based on distance mode profile of time series and convolutional neural network, along with an ensemble learning model for the decision of fixture adjustment. The experiment results show that the integration of digital twin and machine learning provides a feasible way for real-time monitoring and accurate control of welding quality.