Vision-based FDM Printing for Fabricating Airtight Soft Actuators (2312.01135v2)

Abstract: Pneumatic soft robots are typically fabricated by molding, a manual fabrication process that requires skilled labor. Additive manufacturing has the potential to break this limitation and speed up the fabrication process but struggles with consistently producing high-quality prints. We propose a low-cost approach to improve the print quality of desktop fused deposition modeling by adding a webcam to the printer to monitor the printing process and detect and correct defects such as holes or gaps. We demonstrate that our approach improves the air-tightness of printed pneumatic actuators without fine-tuning printing parameters. Our approach presents a new option for robustly fabricating airtight, soft robotic actuators.

• The paper introduces a real-time vision-based feedback mechanism that detects and corrects defects during FDM printing.
• It demonstrates a significant reduction in air leakage for soft actuators by adjusting printing parameters based on image analysis.
• The system integrates pre-processing, detection, and correction modules to enhance the airtightness of TPU-based soft robotic components.

Introduction

Pneumatic soft robots, which rely on air pressure to move, are often fabricated using complex processes like silicone molding, demanding skilled labor and significant time. To make these devices more accessible, researchers have focused on using additive manufacturing (AM), specifically Fused Deposition Modeling (FDM). Although FDM offers advantages in cost efficiency and the ability to create complex geometries, achieving airtight structures with soft materials, such as thermoplastic polyurethanes (TPUs), is challenging due to inconsistencies in material extrusion. This paper introduces a low-cost, vision-based feedback approach for FDM printers that monitors and corrects defects in real-time during the printing process, aiming for better-quality, airtight soft robotic actuators.

System Design

The system is composed of three primary modules: Print pre-processing, Detection, and Correction. Before each layer is printed, the pre-processing phase manages the G-code modifications and orchestrates communication between the printer, camera, and computer. As for Detection, a camera attached to the printer capture images of the completed layer, which are then assessed to identify any flaws, such as holes that could cause air leakage. If defects are spotted, the Correction subsystem jumps into action, adjusting the printer's G-code to rectify these imperfections before the next layer is added.

Experimentation

Experiments were set up to test the effectiveness of this vision-based feedback system. The team printed soft bellow actuators with varying print quality parameters and then submerged them in water under constant pressure to measure any air leakage. Across different parameter settings, the closed-loop system showed a reduction in leakage, demonstrating its capability to improve the airtightness of 3D-printed soft actuators.

Discussion and Conclusion

The results suggest that this approach can significantly enhance the impermeability of soft structures printed using FDM. However, the successful implementation requires that previously attempted printing parameters must be reasonably close to producing airtight structures. The paper also acknowledges the system's limitations, which include its dependency on initial printing proficiency and its focus on randomly occurring defects. Despite these limitations, the proposed vision-based FDM printing system illustrates a novel and practical method for improving the air-tight quality of soft robotic components.