A 3D Printing‐Enabled Artificially Innervated Smart Soft Gripper with Variable Joint Stiffness
Guo Liang Goh, Guo Dong Goh, Van Pho Nguyen, William Toh, Samuel Lee, Xin Li, Bohra Dhyan Sunil, Jian Yee Lim, Zhengchen Li, Anoop Kumar Sinha, Wai Yee Yeong, Domenico Campolo, Wai Tuck Chow, Teng Yong Ng, Boon Siew Han
- Year
- 2023
- Citations
- 92
- Access
- Open access
Abstract
Abstract The manufacturing industry has witnessed advancements in soft robotics, specifically in robotic grippers for handling fragile or irregular objects. However, challenges remain in balancing shape compliance, structural rigidity, weight, and sensor integration. To address these limitations, a 3D‐printed multimaterial gripper design is proposed. This approach utilizes a single, nearly fully automated 3D printing process to create a universal gripper with almost no assembly work. By processing functional polymer, polymer nanocomposite, and metal wire simultaneously, this technique enables multifunctionality. The gripper achieves different gripping configurations by adjusting joint stiffness through Joule heating of conductive polylactic acid material, ensuring shape conformance. Embedded metal wires, created using an in‐house wire embedding technique, form reliable high‐current‐loading interconnections for the conductive joints acting as the heater. Additionally, an integrated soft sensor printed in thermoplastic polyurethane (TPU) and conductive TPU detects compression levels and discerns handled samples. This study showcases the potential of 3D multimaterial printing for on‐demand fabrication of a smart universal gripper with variable stiffness and integrated sensors, benefiting the automation industry. Overall, this work presents an effective strategy for designing and fabricating integrated multifunctional structures using soft, rigid, and conductive materials, such as polymer, polymer nanocomposite, and metal through multimaterial 3D printing.
Keywords
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