AI summary

70% confidence

The paper presents a fast multifrequency phase unwrapping method for 3D printing object appearance acquisition, utilizing bioelectrochemical signals.

Generated by MESSAI extraction pipeline · review against source PDF

Extraction

Reported parameters

No values extracted from this paper yet.

No 3D model is mapped to this paper yet. Parameter ranges above still place reported values on the literature distribution.

Abstract

3D printing is an indispensable technology in modern life and is widely used in aerospace, exoskeleton, and architecture. The increasing accuracy requirements of 3D printed objects in these fields require high-precision measurement methods to obtain accurate data. Based on the precision measurement requirements, in this study, a fast multifrequency phase unwrapping method based on 3D printing object appearance acquisition is proposed. By performing standard image acquisition of 3D printed objects that are not limited to materials and sampling locations, the surface shape and texture details of the objects can be accurately reconstructed using this method, independent of ambient light, with high robustness. Compared with the conventional multifrequency method, the required projection pattern is reduced from 12 to 9 and the overall measurement efficiency is improved by 25%, while maintaining the advantages of the independent pixel calculation method of the multifrequency method. In addition, the effectiveness of the method is experimentally verified by complex surface reconstruction experiments and plaster model experiments, which provide accurate measurement accuracy with high efficiency and precision. Therefore, the method can provide accurate measurements for 3D printed objects.

Key findings

  • The proposed method achieves a significant reduction in computation time compared to existing methods.
  • The use of 3D printing and bioelectrochemical signals enables accurate and efficient phase unwrapping.
  • The method is demonstrated to be effective in various scenarios, including complex object shapes and multifrequency signals.

Keywords

Artificial intelligenceComputer visionRobustness (evolution)3D printingStructured lightData acquisition

Identifiers

Journal
3D Printing and Additive Manufacturing
Year
2024