Automated calibration of 3D-printed microfluidic devices based on computer vision

Abstract

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.

Microsystem Advances through Integration with Artificial Intelligence

“…Moreover, machine learning techniques can also be applied to the fabrication of microsystems. For example, Wang et al adapted a fully automated CNN computer vision system to aid in calibration during 3D printing of microstructure dimensions [37]. Shchanikov et al used an ANN to design a bidirectional biointerface with nanoelectronics and microfluidics [38].…”

Application of Artificial Intelligence to In Vitro Tumor Modeling and Characterization of the Tumor Microenvironment

“…ML applications are increasingly deployed in 3D-printing processes, including (i) process optimization (effects of rheological properties, nozzle gauge, nozzle temperature, path height, and ink composition), (ii) the detection of manufacturing defects, (iii) assessments of dimensional accuracy, and (iv) predictions of material properties (130) [129,130] (Table 3). AI applications, such as a CNN-aided calibration method, [131] have also been used to optimize the design of microfluidic devices and minimize absolute errors in device fabrication. However, 3D-printed tumor modeling processes have been put to limited use so far, possibly due to the intrinsic complexity of the biomaterials involved and the lack of (training) data for this relatively new technology.…”

Organ-on-a-chip meets artificial intelligence in drug evaluation

“…During this process, AI could be applied to provide support to improve accuracy and efficiency, such as 1) the optimization of processes; 2) the detection of manufacturing defects; 3) the evaluation of dimensional accuracy; and 4) the prediction of material properties 246 . For instance, a computer vision-based (CNN-aided calibration) approach was used to rapidly and precisely design microfluidic devices and minimize absolute errors in device manufacturing, which offers a convenient, effective, and efficient solution for 3D printing of OoC platforms 247 .…”