A Transferable Digital Twin-Driven Process Design Approach for Surface Roughness Prediction in Multi-Jet Polishing

Fluid jet polishing process (FJP) demonstrates high shape accuracy and surface quality in the machining of nonlinear and complex surfaces, and it achieves precise and adjustable material removal rates through computer control. However, there are still challenges in terms of machining efficiency, system complexity, and stability. Particularly, there is uncertainty in process optimization, especially with higher challenges in optimizing process parameters after changes in working conditions. This study utilizes digital twin technology to propose a new framework for optimizing the FJP process. By reviewing the application of DT in the machining field, this paper identifies the limitations of existing methods and proposes a human-centric design approach that integrates key factors of DT-driven FJP, such as jet kinetic energy, nozzle structure, abrasive type, and machining path. This method encompasses multiple aspects from removal function models to machining path algorithms. By introducing a core method based on transfer learning, this research aims to improve the predictive accuracy, machining efficiency, and stability of the FJP process, realizing efficient and precise polishing operations. Ultimately, this paper validates the proposed method through a case study on 3D printed workpieces, discusses the key enabling technologies, and main challenges. This study not only advances the application potential of FJP process but also provides a new perspective and strategy for optimizing complex machining processes using DT technology.