Investigation of Optimum Geometry for Robotics Torque Sensor
Purpose: This study aims to optimise the geometry of joint torque sensors to enhance sensitivity and precision while minimising stress. Design/Methodology/Approach: The research involves designing and evaluating three-ring geometries (Ring A, Ring B, and Ring C) under varying rib angles (30°, 45°, 60°, and 90°) using Finite Element Analysis (FEA) in ANSYS. Experimental validation was performed on the geometry with the highest stress (Ring C, 60° rib angle), utilising strain gauges for stress measurement. Findings: FEA revealed that Ring A and B geometries exhibited lower stress levels, with Ring A being the most favourable. While showing higher stress, Ring C demonstrated suitability for applications requiring precise strain measurements. Research Limitation: The study focuses on aluminum alloys 6061 and 7075-T6. Practical Implication: Optimised torque sensors can enhance robotic applications requiring precise force measurement and control, improving operational safety and precision. Social Implication: Enhanced torque sensor design supports safer and more efficient robotics and benefits society by enabling technological advancements. Originality/Value: This work comprehensively evaluates torque sensor geometries, combining FEA and experimental validation to identify designs that balance stress minimisation and measurement precision, contributing to advancements in robotic sensor technology.
Authors
- Vikas Radhakrishna Deulgaonkar (ORCID: https://orcid.org/0000-0001-7778-3929)
- S. H. Sarje
- Sushma M. Bhosle (ORCID: https://orcid.org/0000-0003-4356-4239)
- Shankar C. Vetal (ORCID: https://orcid.org/0000-0001-9946-4478)
- Shrikant C. Mahadik (ORCID: https://orcid.org/0000-0001-8807-6234)
- Dinesh Washimkar
Institutions
- Marathwada Agricultural University (IN)
- International Institute of Information Technology (IN)
- Vidya Prathishthan (IN)
Publication Details
- Journal
- African Journal Of Applied Research
- Published
- 2025-01-09
- DOI
- https://doi.org/10.26437/ajar.v11i1.861
- Citations
- 2
- Primary Topic
- Industrial Vision Systems and Defect Detection
- Type
- article
- Field-Weighted Citation Impact
- 2.48