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Prediction model of part topography in curved surface inkjet 3D printing

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Abstract

Inkjet 3D printing using array nozzles allows efficient deposition of complex shapes on free-form surfaces. However, the slip of droplets on curved substrates and the flow of printing materials can cause edge collapse and poor surface morphology of curved multilayer printed parts. In this study, a part morphology prediction model for curved inkjet 3D printing is developed to predict the surface morphology of printed parts. First, from the perspective of computational fluid dynamics, a surface drop prediction model is formed to predict the slip and deposition patterns of ink droplets on curved substrates. The model calculates the spreading diameter of droplets and slip distance on curved surfaces based on the physical parameters of droplets, substrate roughness, and droplet impact angle. Then, based on the droplet slip distance and spreading diameter data obtained from the droplet drop prediction model, a multilayer stacking model for surface printing is established to predict the evolution of part height and contour shape in printing. After discretizing the print area, the morphology prediction of curved inkjet 3D printed parts is achieved by analytically modeling the physical processes such as droplet deposition, local ink flow, and solvent volatilization. Finally, multilayer printing experiments on a spherical surface are conducted to verify the accuracy of the prediction model. The results show that the predicted shape of the sample parts is in good agreement with the experimental results. The model provides a theoretical basis for the closed-loop control of surface inkjet 3D printing and printing defect compensation.

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Funding

This work was supported by the National Natural Science Foundation of China [Grant 52035010], Shaanxi Key Industry Chain Project [grant number 2020ZDLGY14-08], Shaanxi Innovation Team Project [grant number 2018TD-012], National 111 Project [grant number B14042], National Natural Science Foundation of China [grant number 52205411], Shaanxi Province Science and Technology Project Youth Fund [grant number 2022JQ-366], and National Defense Science and Technology Key Laboratory Fund [grant number 2022-JCJQ-LB-018].

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Authors and Affiliations

  1. School of Electro-Mechanical Engineering, Xidian University, Xi’an, 710071, China

    Bu Ping, Jin Huang & Fanbo Meng

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  1. Bu Ping
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  2. Jin Huang
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  3. Fanbo Meng
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Contributions

Bu Ping: methodology, writing—original draft, software, visualization. Jin Huang: conceptualization, writing—review and editing, resources, supervision. Fanbo Meng: conceptualization, writing—review and editing, supervision.

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Correspondence to Jin Huang or Fanbo Meng.

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Ping, B., Huang, J. & Meng, F. Prediction model of part topography in curved surface inkjet 3D printing. Int J Adv Manuf Technol 127, 3371–3384 (2023). https://doi.org/10.1007/s00170-023-11736-z

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