Startseite Design and application of soft robot grippers using low-viscosity silicone by lost core injection molding manufacturing method
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Design and application of soft robot grippers using low-viscosity silicone by lost core injection molding manufacturing method

  • Helmy Dewanto Bryantono ORCID logo , Meng-Hsun Tsai ORCID logo und Shi-Chang Tseng EMAIL logo
Veröffentlicht/Copyright: 3. Februar 2025
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Abstract

This project aims to develop a manufacturing method for a newly designed soft robot gripper in one shot by utilizing a low-viscosity liquid silicone rubber (LSR) lost-core injection molding embedded with a polyvinyl alcohol (PVA) water-soluble core inside. Due to the relatively high viscosity of LSR, higher injection pressures were needed to complete the mold-filling process. This, in turn, resulted in a washout of the PVA lost core. Therefore, this study used a lower viscosity of LSR and pressure to avoid this problem. Ansys structural analysis simulation was used to get the experiment variables and then compare them with the real experiment results. The maximum pressure employed in the simulation of the gripper bending is 30 kPa with 119.38 mm, while the experimental is 112.65 mm total deformation. Finally, the washout of the lost core, the bending restriction problem, and the complicated manufacturing problems in this area were tackled in this study. The design of a finger with a greater angle at the edge and the use of low-viscosity LSR as the primary material in a one-shot lost core LSR injection molding method are extensions from previous studies that are believed to be valuable inventions for academic and practical applications.


Corresponding author: Shi-Chang Tseng, Department of Mechanical Engineering, National Yunlin University of Science and Technology, Douliu, 64002, Taiwan, E-mail:

Award Identifier / Grant number: MOST-107-2221-E-224-045

  1. Research ethics: The research work is original by the authors.

  2. Informed consent: Not applicable.

  3. Author contributions: Helmy Dewanto Bryantono: conceptualization, formal analysis, methodology, project administration, writing – original draft, visualization. Meng-Hsun Tsai: data curation, investigation, resources, software, writing – review & editing. Shi-Chang Tseng: validation, supervision, funding acquisition.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors have no conflicts to disclose.

  6. Research funding: This work has been developed within the project by the Ministry of Science and Technology (MOST) in Taiwan, funded from grant MOST-107-2221-E-224-045.

  7. Data availability: The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Received: 2024-10-27
Accepted: 2025-01-01
Published Online: 2025-02-03
Published in Print: 2025-03-26

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 3.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/polyeng-2024-0223/html
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