With the development of 3D printing techniques, the application of it in microfluidic/Lab-on-a-Chip (LoC) fabrication is becoming more and more attractive. However, to achieve a satisfying printing quality of the target devices, researchers usually require quite an amount of work in calibration trials even for high-end 3D printers. To increase the calibration efficiency of the average priced printers and promote the application of 3D printing technology in the microfluidic community, this work has presented a computer vision (CV)-based method for rapid and precise 3D printing calibration with examples on cylindrical hole/post diameters of 0.2–2.4 mm and rectangular hole/post widths of 0.2–1.0 mm by a stereolithography-based 3D printer. Our method is fully automated, which contains five steps and only needs a camera at hand to provide photos for convolutional neural network recognition. The experimental results showed that our CV-based method could provide calibrated dimensions with just one print of the specific calibration ruler to meet user desire. The higher resolution of the photo provides a higher precision in calibration. Subsequently, only one more print for the target device is needed after the calibration process. Overall, this work has provided a quick and precise calibration tool for researchers to apply 3D printing in the fabrication of their microfluidic/LoC devices with average price printers. Besides, with our open source calibration software and calibration ruler design file, researchers can modify the specific setting based on customized needs and conduct calibration on any type of 3D printer.

Microfluidics has shown great potential in cell analysis, where the flowing path in the microfluidic device is important for the final study results. However, the design process is time-consuming and labor-intensive. Therefore, we proposed an ANN method with three dense layers to analyze particle trajectories at the critical intersections and then put them together with the particle trajectories in straight channels. The results showed that the ANN prediction results are highly consistent with COMSOL simulation results, indicating the applicability of the proposed ANN method. In addition, this method not only shortened the simulation time but also lowered the computational expense, providing a useful tool for researchers who want to receive instant simulation results of particle trajectories.