Error assessment and correction for extrusion- based bioprinting using computer vision method

¹ 1 State Key Laboratory of Metal Matrix Composites, School of Material Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

² National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai 200240, China

³ Department of Orthopaedics, Affiliated Hospital of Youjiang Medical University for Nationalities, Guangxi Key Laboratory of Basic and Translational Research of Bone and Joint Degenerative Diseases, Baise, 533000, Guangxi, China

⁴ 3D Printing Clinical Translational and Regenerative Medicine Center, Shenzhen Shekou People’s Hospital, Shenzhen, 518060, China

⁵ Department of Stomatology, Shenzhen Shekou People’s Hospital, Shenzhen, 518060, China

IJB 2023, 9(1), 644 https://doi.org/10.18063/ijb.v9i1.644

Submitted: 14 July 2022 | Accepted: 30 August 2022 | Published: 16 November 2022

(This article belongs to the Special Issue Related to 3D printing technology and materials)

© 2022 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

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Abstract

Bioprinting offers a new approach to addressing the organ shortage crisis. Despite recent technological advances, insufficient printing resolution continues to be one of the reasons that impede the development of bioprinting. Normally, machine axes movement cannot be reliably used to predict material placement, and the printing path tends to deviate from the predetermined designed reference trajectory in varying degrees. Therefore, a computer vision-based method was proposed in this study to correct trajectory deviation and improve printing accuracy. The image algorithm calculated the deviation between the printed trajectory and the reference trajectory to generate an error vector. Furthermore, the axes trajectory was modified according to the normal vector approach in the second printing to compensate for the deviation error. The highest correction efficiency that could be achieved was 91%. More significantly, we discovered that the correction results, for the first time, were in a normal distribution instead of a random distribution.

Keywords

Bioprinting

Computer vision

Error detection

Sobel operator

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