【作者】 吕佳佳；

【导师】 张新；

【作者基本信息】 安徽理工大学 ， 机械电子工程， 2011， 硕士

【摘要】 设计了一种煤炭三维截割力传感器,通过传感器可以准确获得采煤机截齿在截割过程中的受力信息,从而为深入研究煤炭截割机理、定量评价煤炭截割特性、合理选择和使用采煤机和截齿提供依据。该传感器是采用电阻应变式的测量原理,通过黏贴在弹性体上应变片的受力变形测出三维力信号。传感器由弹性体、连接件和保护罩组成,弹性体是传感器的核心部分。本文设计的弹性体是一个圆环结构,在圆环的上下两层各开4个槽孔,槽孔是对称分布的,相位相差45°。上下层槽孔间和同层槽孔间的薄壁区为剪切应力敏感区,不同的剪切应变区对应测量由不同方向的力产生的应变。弹性体的结构尺寸通过正交试验方法进行优化设计得到。其次,采用有限元软件Ansys对传感器进行了静态分析、模态分析、谐响应分析和瞬态响应分析,得到了传感器在静态载荷作用下的应变变化规律,从而确定了应变片的贴片位置。同时得出了传感器的固有频率、振型和频响特性。再次,对弹性体进行贴片,通过合理的组桥方式,从理论上消除维间耦合。传感器的标定实验是采用加标准砝码的方法进行的。通过静态标定,得到了传感器的非线性度、灵敏度、回程误差等静态性能指标。最后对传感器进行解耦,本文采用最小二乘法对传感器进行静态线性解耦,采用BP神经网络方法和独立成分分析方法进行静态非线性解耦。证实了BP神经网络解耦的方法比最小二乘法和独立成分分析方法解耦的精度高。图[63]表[13]参[44]更多还原

【Abstract】 In this paper. a kind of three-axis coal-cutting force sensor is presented. We can obtain force information of shearer in the cutting process through the sensor, which provides a basis for in-depth research of cut coal mechanism, quantitative assessing of cut coal features, reasonable options and the use of coal cutters and cut teeth.The sensor measures three-axis force signals through stress deformation of the strain gauge in elastic body.Firstly, sensor is comprised of elastic body, fitting and a protective covering. The elastic body is the core part of the sensor. It is a torus structure, and four slots are opened in the torus structure of the above and below. Slot is symmetrical,45°phase difference.The thin-walled area between the above slot and the below slot is named shear stress area.Different shear strain area corresponding measure different direction of force. The size of elastic body structure is optimized through orthogonal tests.Secondly, the static analysis, modal analysis, harmonic response analysis and transient response analysis of sensor had been run by finite element software Ansys. Strain changing regularity of the sensor in the static load was obtained, so that we can determine the position of the strain gauge patch was determine. Meanwhile, the natural frequency, vibration mode and frequency response characteristics of the sensor were derived.Thirdly, stain gauge was pasted on the elastic body. Through the reasonable Bridge circuit, coupling between dimensions was eliminated in theory. Sensor calibration experiment was carried out using the method of plus standard farmar. Nonlinearity, sensitivity, hysterisis error etc static performance index of the sensor were obtained by the static calibration. Lastly, the static linear decoupling was carried out by least square method, and BP neural network method and independent component analysis method was used for static nonlinear decoupling.What’s more, it was confirmed that BP neural network decoupling method was much more accurate than the least square method and the independent component analysis method.Figure [63] Table [13] Reference [44]更多还原