@article{LIU2026, 
author = {Qingtao LIU and Panyu YU and Jiongqi GUO and Enhuai YIN and Pengtao YANG and Jingxiang LÜ},
title = {A Collaborative Control Method for Inkjet Printing Accuracy of Electronic Additive Manufacturing},
year = {2026},
journal = {Journal of South China University of Technology (Natural Science Edition)},
volume = {54},
number = {1},
pages = {94-103},
keywords = {electronic additive manufacturing, printing accuracy, LinuxCNC system, speed control algorithm, collaborative control},
url = {https://www.sciopen.com/article/10.12141/j.issn.1000-565X.250084},
doi = {10.12141/j.issn.1000-565X.250084},
abstract = {Electronic additive manufacturing technology possesses significant application value in high-precision microelectronics manufacturing. However, the improvement of printing quality is always restricted by the droplet placement inaccuracies caused by speed fluctuations. To address this issue, a collaborative control strategy based on LinuxCNC, termed S-shaped speed planning + fixed-distance injection (SSP-FDI), was proposed. By optimizing the traditional trapezoidal speed algorithm in numerical control systems into an S-shaped speed algorithm, mechanical shock can be effectively reduced. Simultaneously, by adopting a fixed-distance triggering mode, the droplet spacing can be accurately controlled, thus mitigating the impact of speed fluctuations on placement accuracy. Moreover, an experimental platform integrating five-axis motion control and electronic inkjet printing technology was independently developed, and the corresponding control system was developed. Finally, comparative experiments involving multi-angle polylines and electrode printing were designed. The results demonstrate that, as compared with the traditional trapezoidal speed planning + fixed frequency injection (TSP-FFI) strategy, SSP-FDI strategy significantly reduces droplet placement errors. In a 20 mm × 20 mm rectangular electrode printing experiment with a substrate temperature of 100 ℃, the maximum surface roughness of compensated electrodes decreases to 6 µm. Across five substrate temperature groups, the surface roughness of printed samples shows an average reduction of 18.79%and an average resistivity reduction of 18.70%. These findings indicate that the proposed LinuxCNC-based collaborative control strategy effectively improves the printing quality for complex trajectories, offering a novel technical solution to high-precision additive manufacturing of electronic devices.}
}