The bioprinting literature currently lacks: (i) process sensing tools to measure material deposition, (ii) performance metrics to evaluate system performance, and (iii) control tools to correct for and avoid material deposition errors. The lack of process sensing tools limits in vivo functionality of bioprinted parts since accurate material deposition is critical to mimicking the heterogeneous structures of native tissues. We present a process monitoring and control strategy for extrusion-based fabrication that addresses all three gaps to improve material deposition. Our strategy uses a non-contact laser displacement scanner that measures both the spatial material placement and width of the deposited material. We developed a custom image processing script that uses the laser scanner data and defined error metrics for assessing material deposition. To implement process control, the script uses the error metrics to modify control inputs for the next deposition iteration in order to correct for the errors. A key contribution is the definition of a novel method to quantitatively evaluate the accuracy of printed constructs. We implement the process monitoring and control strategy on an extrusion-printing system to evaluate system performance and demonstrate improvement in both material placement and material width.
- 3D printing, extrusion printing
- additive manufacturing
- defect detection
- process monitoring and control, process feedback
- quality assurance
ASJC Scopus subject areas
- Biomedical Engineering