Advancing Multi-Axis Force/Torque Sensing via Beam Optimization, Self-Decoupling Mechanisms, and Morphing-Based Mechanical Intelligence
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Authors
peng, cong
Issue Date
2025
Type
Dissertation
Language
en_US
Keywords
Alternative Title
Abstract
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.