A digital twin-driven ultra-precision machining system

The demand for ultra-precision machining has expanded significantly across industries such as aerospace, automotive, electronics, and medical sectors. These industries require parts manufactured to micrometre tolerances in a timely and cost-effective manner. To address these demands, efforts have been focused on developing digital twin technology for ultra-precision machining, aimed at improving production accuracy and efficiency. One of the primary challenges in ultra-precision machining is time-consuming setup due to manual workpiece handling. Additionally, machining speeds are limited to mitigate dynamic errors, further prolonging production times. This paper proposes a digital twin system designed to automate workpiece handling and correct dynamic errors in real time to tackle these challenges. The proposed digital twin comprises two systems: one for controlling a collaborative robot arm (COBOT) to automate workpiece handling with corrective action, eliminating the need for manual loading and unloading; and another for controlling a hybrid mill to mitigate dynamic errors through real-time machine learning-based prediction of elastic deformation allowing for higher machining speeds. In this paper, the current progress is discussed, and a methodology for validating this digital twin system is proposed. The proposed validation process will involve machining microfluidic devices using the digital twin system, compared to conventional machining methods to assess the effectiveness