Xi Liang Chen, Zheng Yu Xie, Zhi Qiang Wang, Yi Wen Sun
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引用次数: 0
摘要
目的基于 Y 型结构的六轴力/力矩传感器具有结构简单、体积小、成本低、应用前景广等优点。为满足机器人工程应用中对整体结构刚度和传感器性能的要求,本文旨在提出一种 Y 型六轴力/力矩传感器。建立了多目标优化方程。利用中心复合设计实验方法,在 ANSYS Workbench 中建立了多重二次响应曲面模型。结果对传感器进行了优化,仿真结果表明,传感器的抗过载能力为 200%F.S.,轴向刚度、径向刚度、弧度刚度和弧度刚度均达到了最优、传感器的轴向刚度、径向刚度、弯曲刚度和扭转刚度分别为 14.981 kN/mm、16.855 kN/mm、2.0939 kN m/rad 和 6.4432 kN m/rad,满足设计要求,且优化后传感器各部件的灵敏度分别为初始结构参数传感器的 2.969 倍、2.762 倍、4.010 倍、2.762 倍、2.653 倍和 2.760 倍。优化参数后的传感器原型已经制作完成。根据传感器的校准实验,传感器各力/力矩分量的最大 I 级和 II 级误差及测量不确定度分别为 1.835%F.S.、1.018%F.S.和 1.606%F.S.。原创性/价值因此,可以得出结论,该传感器具有优异的综合性能,符合预期的实际工程要求。
Optimizations of structure stiffnesses and sensitivities of Y-type six-axis force/torque sensor
Purpose
The six-axis force/torque sensor based on a Y-type structure has the advantages of simple structure, small space volume, low cost and wide application prospects. To meet the overall structural stiffness requirements and sensor performance requirements in robot engineering applications, this paper aims to propose a Y-type six-axis force/torque sensor.
Design/methodology/approach
The performance indicators such as each component sensitivities and stiffnesses of the sensor were selected as optimization objectives. The multiobjective optimization equations were established. A multiple quadratic response surface in ANSYS Workbench was modeled by using the central composite design experimental method. The optimal manufacturing structural parameters were obtained by using multiobjective genetic algorithm.
Findings
The sensor was optimized and the simulation results show that the overload resistance of the sensor is 200%F.S., and the axial stiffness, radial stiffness, bending stiffness and torsional stiffness are 14.981 kN/mm, 16.855 kN/mm, 2.0939 kN m/rad and 6.4432 kN m/rad, respectively, which meet the design requirements, and the sensitivities of each component of the optimized sensor have been well increased to be 2.969, 2.762, 4.010, 2.762, 2.653 and 2.760 times as those of the sensor with initial structural parameters. The sensor prototype with optimized parameters was produced. According to the calibration experiment of the sensor, the maximum Class I and II errors and measurement uncertainty of each force/torque component of the sensor are 1.835%F.S., 1.018%F.S. and 1.606%F.S., respectively. All of them are below the required 2%F.S.
Originality/value
Hence, the conclusion can be drawn that the sensor has excellent comprehensive performance and meets the expected practical engineering requirements.
期刊介绍:
Sensor Review publishes peer reviewed state-of-the-art articles and specially commissioned technology reviews. Each issue of this multidisciplinary journal includes high quality original content covering all aspects of sensors and their applications, and reflecting the most interesting and strategically important research and development activities from around the world. Because of this, readers can stay at the very forefront of high technology sensor developments.
Emphasis is placed on detailed independent regular and review articles identifying the full range of sensors currently available for specific applications, as well as highlighting those areas of technology showing great potential for the future. The journal encourages authors to consider the practical and social implications of their articles.
All articles undergo a rigorous double-blind peer review process which involves an initial assessment of suitability of an article for the journal followed by sending it to, at least two reviewers in the field if deemed suitable.
Sensor Review’s coverage includes, but is not restricted to:
Mechanical sensors – position, displacement, proximity, velocity, acceleration, vibration, force, torque, pressure, and flow sensors
Electric and magnetic sensors – resistance, inductive, capacitive, piezoelectric, eddy-current, electromagnetic, photoelectric, and thermoelectric sensors
Temperature sensors, infrared sensors, humidity sensors
Optical, electro-optical and fibre-optic sensors and systems, photonic sensors
Biosensors, wearable and implantable sensors and systems, immunosensors
Gas and chemical sensors and systems, polymer sensors
Acoustic and ultrasonic sensors
Haptic sensors and devices
Smart and intelligent sensors and systems
Nanosensors, NEMS, MEMS, and BioMEMS
Quantum sensors
Sensor systems: sensor data fusion, signals, processing and interfacing, signal conditioning.