Advancing Multi-Axis Force/Torque Sensing via Beam Optimization, Self-Decoupling Mechanisms, and Morphing-Based Mechanical Intelligence

This dissertation advances multi-axis force and torque sensing by unifying beam-optimized compliant mechanisms, 3D structural self-decoupling, in-measurement morphing-based mechanical intelligence, and a novel six-axis self-decoupling architecture into a cohesive framework, establishing a new paradigm for high-performance force/torque measurement. The overarching research vision is to develop a class of adaptive, task-aware F/T sensors capable of dynamically reconfiguring their mechanical properties to maximize measurement accuracy, robustness, and operational safety. This work establishes the scientific and engineering foundations for mechanically intelligent sensing systems-devices that not only measure applied forces but also actively adapt to interaction conditions, protect themselves from overload, and optimize information delivery across diverse environments. The dissertation first presents a cross-structured 3D force sensor optimized via parametric beam design and finite-element analysis. Examining various beam geometries and slot dimensions reveals how local strain distributions affect sensitivity and parasitic coupling, leading to an arc-shaped, double-layer rectangular beam configuration that enhances bending sensitivity, reduces cross-talk, and remains manufacturable. Building on these insights, a soft 3D force sensor is developed using a hollow square-column architecture with embedded piezoresistive films and a modified Wheatstone-bridge interface. This compact, volume-efficient design eliminates the need for a rigid frame, confines deformation to the free-end beams, and maximizes strain utilization. Symmetric placement of the sensing films combined with the modified bridge configuration provides inherent mechanical and electrical self-decoupling, reducing inter-axis interference and partially compensating for temperature-induced drift. To address nonlinear and history-dependent behaviors, a generalized Preisach hysteresis model and its inverse are formulated, relaxing classical assumptions and identifying a two-dimensional density function directly from experimental data. This approach accurately reproduces measured force-voltage loops, and the inverse model substantially mitigates hysteresis, improving both accuracy and repeatability of the reconstructed force signals. The dissertation further introduces a mechanically intelligent 3D force sensor with morphing cantilever beams, enabling real-time reconfiguration of structural compliance. By switching among discrete morphing states-each corresponding to a different effective beam length and deformation mode-the sensor achieves variable stiffness, tunable sensitivity, and adaptive multi-axis flexure. Compliant states support high-resolution measurement of small forces, whereas stiffer states provide large-load tolerance and intrinsic overload protection, overcoming the traditional trade-off between sensitivity and robustness. Morphing mechanics are co-designed with self-decoupling bridge circuits to maintain low cross-axis interference across all configurations, preserving a well-conditioned mapping from forces to sensor outputs. This integration produces a compact, mechanically intelligent sensing system combining adaptive stiffness, morphing mechanics, tunable sensitivity, and self-protection. Finally, as a step toward six-axis force/torque sensing, a new self-decoupling mechanism is proposed. This mechanism extends the structural and circuit principles to all six components of force and torque, demonstrating the feasibility of mechanically intelligent, self-decoupling architectures for six-dimensional measurement and providing a clear pathway for future refinement, implementation, and enhanced functionality. In summary, this dissertation establishes a comprehensive framework for next-generation multi-axis F/T sensors that are mechanically intelligent, self-decoupling, and adaptively tunable. Across rigid, soft, and morphing architectures, it demonstrates how structural optimization, compliant mechanisms, and morphing-based adaptivity can be co-designed to deliver high resolution, broad dynamic range, and robust operation. These contributions lay the foundation for a new class of task-aware force sensors with potential applications in dexterous robotics, surgical and haptic devices, wearable and prosthetic technologies, precision manufacturing, and human-robot collaboration-domains where high-fidelity, adaptive, and overload-tolerant force sensing is critical.

Level thesis:degree_level

Doctorate Degree

Year dc:date.issued

2025

Author dc:creator

• peng, cong

Advisor dc:contributor.advisor

• Shen, Yantao

Language dc:language.iso

en_US, English

Repository record dc:identifier.uri

https://scholarwolf.unr.edu/handle/11714/11773

OAI identifier oai:identifier

oai:scholarwolf.unr.edu:11714/11773