{"id":{"repo_id":"unr","oai_identifier":"oai:scholarwolf.unr.edu:11714/11773"},"canonical_url":"https://search.dev.ndltd.org/etd/unr/oai:scholarwolf.unr.edu:11714/11773","repository":{"repo_id":"unr","name":"University of Nevada - Reno","base_url":"https://scholarwolf.unr.edu/server/oai/request"},"display":{"title":"Advancing Multi-Axis Force/Torque Sensing via Beam Optimization, Self-Decoupling Mechanisms, and Morphing-Based Mechanical Intelligence","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.","abstract_html":"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.","abstract_has_math":false,"creators":["peng, cong"],"institution":null,"degree_name":null,"degree_level":"Doctorate Degree","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Shen, Yantao"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-27T21:47:03Z","subjects":[],"languages":["en_US","English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarwolf.unr.edu/handle/11714/11773","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Shen, Yantao"]},{"key":"dc:creator","label":"Author","values":["peng, cong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["01/01/2026"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-24T01:44:13Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctorate Degree"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://scholarwolf.unr.edu/handle/11714/11773"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:format","label":"Dc Format","values":["PDF"]},{"key":"dc:title","label":"Title","values":["Advancing Multi-Axis Force/Torque Sensing via Beam Optimization, Self-Decoupling Mechanisms, and Morphing-Based Mechanical Intelligence"]}]}],"canonical_facts":{"dc:contributor.advisor":["Shen, Yantao"],"dc:creator":["peng, cong"],"dc:date":["01/01/2026"],"dc:date.accessioned":["2026-01-24T01:44:13Z"],"dc:date.issued":["2025"],"dc:description.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."],"dc:format":["PDF"],"dc:identifier.uri":["https://scholarwolf.unr.edu/handle/11714/11773"],"dc:language":["English"],"dc:language.iso":["en_US"],"dc:title":["Advancing Multi-Axis Force/Torque Sensing via Beam Optimization, Self-Decoupling Mechanisms, and Morphing-Based Mechanical Intelligence"],"dc:type":["Dissertation"],"thesis:degree_level":["Doctorate Degree"]},"updated_at":"2026-07-27T21:47:03Z"}