Literature DB >> 31825905

Direct process feedback in extrusion-based 3D bioprinting.

Ashley A Armstrong1, Julian Norato, Andrew G Alleyne, Amy J Wagoner Johnson.   

Abstract

A major limitation in extrusion-based bioprinting is the lack of direct process control, which limits the accuracy and design complexity of printed constructs. The lack of direct process control results in a number of defects that can influence the functional and mechanical outcomes of the fabricated structures. The machine axes motion cannot be reliably used to predict material placement, and precise fabrication requires additional sensing of the material extrusion. We present an iteration-to-iteration process monitoring system that enables direct process control in the material deposition reference frame. To fabricate parts with low dimensional errors, we integrate a non-contact laser displacement scanner into the printing platform. After fabrication of the initial print using the as-designed reference trajectory, the laser scanner moves across the part to measure the material placement. A custom image processing algorithm compares the laser scanner data to the as-designed reference trajectory to generate an error vector. To compensate for the measured error, the algorithm modifies the axes reference trajectory for the second print iteration. We implement the in situ process monitoring and error compensation technique on an experimental platform to evaluate system performance and demonstrate improvement in spatial material placement.

Mesh:

Year:  2019        PMID: 31825905     DOI: 10.1088/1758-5090/ab4d97

Source DB:  PubMed          Journal:  Biofabrication        ISSN: 1758-5082            Impact factor:   9.954


  5 in total

1.  Continuous fiber extruder for desktop 3D printers toward long fiber embedded hydrogel 3D printing.

Authors:  Wenhuan Sun; Adam Feinberg; Victoria Webster-Wood
Journal:  HardwareX       Date:  2022-03-24

2.  Scalable Biofabrication: A Perspective on the Current State and Future Potentials of Process Automation in 3D-Bioprinting Applications.

Authors:  Nils Lindner; Andreas Blaeser
Journal:  Front Bioeng Biotechnol       Date:  2022-05-20

3.  Computer Vision-Aided 2D Error Assessment and Correction for Helix Bioprinting.

Authors:  Changxi Liu; Jia Liu; Chengliang Yang; Yujin Tang; Zhengjie Lin; Long Li; Hai Liang; Weijie Lu; Liqiang Wang
Journal:  Int J Bioprint       Date:  2022-02-07

Review 4.  Biomechanical factors in three-dimensional tissue bioprinting.

Authors:  Liqun Ning; Carmen J Gil; Boeun Hwang; Andrea S Theus; Lilanni Perez; Martin L Tomov; Holly Bauser-Heaton; Vahid Serpooshan
Journal:  Appl Phys Rev       Date:  2020-12       Impact factor: 19.162

Review 5.  Computer vision-aided bioprinting for bone research.

Authors:  Changxi Liu; Liqiang Wang; Weijie Lu; Jia Liu; Chengliang Yang; Chunhai Fan; Qian Li; Yujin Tang
Journal:  Bone Res       Date:  2022-02-25       Impact factor: 13.362

  5 in total

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