Abstract: Research on digital process twin manufacturing for the propellant grain turning-shaping process was conducted to address issues of poor equipment-process visualization and high operational risk during the propellant grain turning-shaping process. Firstly, based on the digital twin architecture, a method for multi-source heterogeneous data acquisition and real-time processing was proposed. Secondly, a surface location error prediction model for propellant grain turning was constructed. Thirdly, temperature feedback during the shaping process was achieved through the use of intelligent temperature-measuring tools. Finally, an equipment-process interaction model for the propellant grain shaping process was developed, and a digital twin platform for equipment-process visualization in propellant grain turning machining was established. The results indicate that the proposed equipment-process interaction model enables synchronized monitoring and interactive visualization of surface location errors and temperatures during the machining process. Furthermore, the developed digital twin platform is demonstrated to effectively support intelligent monitoring and accuracy assurance for the propellant grain turning-shaping process.