# A Transferable Digital Twin-Driven Process Design Framework for High-Performance Multi-Jet Polishing

## TL;DR

This paper introduces a digital twin framework to improve the efficiency and accuracy of multi-jet polishing processes.

## Contribution

A transfer learning-based model is proposed to enhance surface roughness prediction and adaptability in varying conditions.

## Key findings

- The proposed model improves predictive performance in multi-jet polishing.
- It demonstrates cross-condition adaptability through experiments on 3D printed workpieces.
- The framework supports data-scarce and uncertain manufacturing scenarios.

## Abstract

The multi-jet polishing process (MJP) 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. In particular, maintaining the polishing quality presents a greater challenge when working conditions change. To overcome these issues, this paper conceptually proposes a digital twin (DT)-driven, human-centric design framework that integrates key factors of MJP, such as jet kinetic energy, nozzle structure, abrasive type, and machining path. Within this framework, a feature-encoded transfer learning-based model is introduced to enhance surface roughness prediction accuracy and robustness under varying working conditions. The effectiveness of the proposed model was verified by conducting experiments on 3D printed workpieces under two different MJP working conditions. The results show that our proposed method yields better predictive performance and cross-condition adaptability. Overall, this work provides a predictive modeling component that supports DT-driven process design, offering a practical and extensible perspective for optimizing complex ultra-precision manufacturing processes under data-scarce and uncertainty-dominated conditions.