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1.
Experiments are described in which plasticine strips are rolled using elastomer rolls. Conditions cover the range from “thick strip” behaviour, in which roll elastic deformations are small, to “thin strip rolling”, in which elastic deformations of the rolls are very significant. Results provide the first direct experimental confirmation of the thin strip rolling model proposed by Fleck et al. (Proc. Instn Mech. Engrs B206 (1992) 119–131). Strip profiles clearly show a short region of reduction at the inlet to the bite and a central region which is relatively flat, in accord with the theory. The profiles do not however show a short region of reduction at the exit as predicted. For intermediate strip thicknesses the measured loads are in reasonable agreement with theory. For the thinnest strips, although the form of the dependence of load on reduction and inlet strip thickness is as predicted by theory, the measured loads are almost an order of magnitude lower than predicted. It is suggested that this is caused either by differences between the assumed rigid–perfectly plastic strip and the real constitutive behaviour of the plasticine, or by errors in treating the rolls as elastic half-spaces, an approximation which is accurate for industrial metal rolling, but is not good for the conditions of these experiments. 相似文献
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Lee Sang-Ho Song Gil-Ho Lee Sung-Jin Kim Byung-Min 《Journal of Mechanical Science and Technology》2011,25(8):2101-2109
The quality requirements for thickness accuracy in cold rolling continue to become more stringent. In cold rolling mill, it
is very important that the rolling force calculation considers rolling conditions. The rolled strip thickness was predicted
using calculated rolling force. However, the prediction of strip thickness in cold rolling is very difficult; in particular,
for 6-high mill with shifted intermediate roll (IMR), the accuracy of thickness is not good. In this study, to improve the
accuracy of rolled strip thickness, the roll gap flattening can be given based on Hertz contact theory, with contact between
rolls and the smooth cylindrical rolls for the rolling elastic deformation. Also, the distribution of the roll gap flattening
may be calculated using the contact force of unit transverse length. The strip profile at the continuous cold rolling is calculated
by using the numerical analysis model considering the initial strip profile before cold rolling. Hence, we propose that the
numerical model can predict the rolled strip profile more quickly and accurately and be applicable to the field. The results
of the proposed numerical model were verified by FE-simulation and cold rolling experiments of 6-high mill with five stands. 相似文献
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几种典型板带轧机板型控制技术简介 总被引:1,自引:0,他引:1
带钢板型是冷轧板带及其涂镀产品最重要的质量指标,近年来出现了一大批可选择的具有良好板型控制功能的轧机和技术,本文将对UCM、CVC、Smart—Crown、DSR及六辊3C这几种新型轧机的板型控制技术做一下简要介绍。 相似文献
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Road profile estimation using neural network algorithm 总被引:1,自引:0,他引:1
Mahdi Yousefzadeh Shahram Azadi Abbas Soltani 《Journal of Mechanical Science and Technology》2010,24(3):743-754
This paper more specifically focuses on the estimation of a road profile (i.e., along the “wheel track”). Road profile measurements
have been performed to evaluate the ride quality of a newly constructed pavement, to monitor the condition of road networks
in road management systems, as an input to vehicle dynamic systems, etc. The measurement may be conducted by a slow-moving
apparatus directly measuring the elevation of the road or using a means that measures surface roughness at highway speeds
by means of accelerometers coupled with high speed distance sensors, such as laser sensors or using a vehicle equipped with
a response-type road roughness measuring system that indirectly indicate the user’s feelings of the ride quality. This paper
proposes a solution to the road profile estimation using an artificial neural network (ANN) approach. The method incorporates
an ANN which is trained using the data obtained from a validated vehicle model in the ADAMS software to approximate road profiles
via the accelerations picked up from the vehicle. The study investigates the estimation capability of neural networks through
comparison between some estimated and real road profiles in the form of actual road roughness and power spectral density. 相似文献
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An automotive brake's performance results from the complex interrelated phenomena occurring at the contact of the friction pair. These complex braking phenomena are mostly affected by the tribochemical properties of the friction material's ingredients, the brake disc properties, and the brake's operating regimes. In this paper, the synergistic effects of the friction material's properties, defined by its composition and manufacturing conditions, and the brake's operating regimes on the disc brake factor C variation have been modelled by means of artificial neural networks. The influences of 26 input parameters, determined by the friction material composition (18 ingredients), its manufacturing conditions (5 parameters), and the brake's operating regimes (3 parameters) on the brake factor C variation, have been predicted. The neural model of the disc brake cold performance has been developed by training 18 different neural network architectures with the five different learning algorithms. The optimal neural model of disc brake operation has been shown to be valid for predicting the brake factor C variation of the cold disc brake over a wide range of brake's operating regimes and for different types of friction material. 相似文献
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The twin-roll strip casting process is a steel-strip production method which combines continuous casting and hot rolling processes.
The production line from molten liquid steel to the final steel-strip is shortened and the production cost is reduced significantly
as compared to conventional continuous casting. The quality of strip casting process depends on many process parameters, such
as molten steel level in the pool, solidification position, and roll gap. Their relationships are complex and the strip casting
process has the properties of nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate
process model for designing a model-based controller to monitor the strip quality. In this paper, a model-free adaptive neural
network controller is developed to overcome this problem. The proposed control strategy is based on a neural network structure
combined with a sliding-mode control scheme. An adaptive rule is employed to on-line adjust the weights of radial basis functions
by using the reaching condition of a specified sliding surface. This surface has the on-line learning ability to respond to
the system’s nonlinear and time-varying behaviors. Since this model-free controller has a simple control structure and small
number of control parameters, it is easy to implement. Simulation results, based on a semiexperimental system dynamic model
and parameters, are executed to show the control performance of the proposed intelligent controller. In addition, the control
performance is compared with that of a traditional PID controller. 相似文献
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通过实际模型及其分析提出了几种快速称重方法和提高传感器性能的原理,着重阐述了基于神经网络理论,设计出称重传感器神经网络动态补偿器和提高传感器性能。仿真研究表明有效的提高了传感器动态响应的快速性,且对系统参数变化具有鲁棒性。将先进控制理论应用于实际传感器系统中,为提高传感器性能开辟了新途径,促进了称重传感器行业的发展。 相似文献
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Dr Shiuh-Jer Huang Chih-Feng Hu 《The International Journal of Advanced Manufacturing Technology》1996,12(6):450-454
Since a robotic manipulator has a complicated mathematical model, it is difficult to design a control system based on the complicated multi-variable nonlinear coupling dynamic model. Intelligent controllers using fuzzy and neural network approaches do not need a real mathematical model to design the control structure and have attracted the attention of robotic control researchers recently. A traditional fuzzy logic controller does not have learning capability and it needs a lot of effort to search for the optimal control rules and the shapes of membership functions. Owing to the time-varying behaviour of the system, the required fine tracking accuracy is difficult to achieve by adjusting the fuzzy rules only. The implementation problems of neural network control are the initial training and initial transient stability. In order to improve the position control accuracy and system robustness for industrial applications, a neural controller is first trained off-line by using the input and output (I/O) data of a traditional fuzzy controller. Then the neural controller is implemented on a five-degrees-of-freedom robot with a back propagation algorithm for online adjustment. The experimental results show that this neural network controller achieved the required trajectory tracking accuracy after 15 on-line operations. 相似文献
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《Flow Measurement and Instrumentation》1999,10(1):35-43
Mathematical models and numerical methods offer a flexible tool to investigate flow disturbance effects on flowmeters of different types. In this paper a simple neural network based approach has been used to study the velocity profile dependence of ultrasonic flowmeters. Neural networks have been used in two ways: to interpolate the velocity profiles in the points needed for the modelling of ultrasonic flow measurement, and to compute the weights for different paths of multipath ultrasonic flowmeters. In the former case two types of neural networks, multilayer perceptron networks and radial basis function networks, have been investigated. In the latter case, a single layer neural network with linear neurons is first trained with known velocity profiles, and the weights determined by the network have then been used in the computation of the errors in other piping configurations. The results have been compared with the errors computed with the weights for different paths given in Pannel CN, Evans WAB, Jackson DA. A new integration technique for flowmeters with chordal paths, Flow Measurement and Instrumentation 1990;1:216–224. 相似文献
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Dragan Aleksendrić 《Wear》2010,268(1-2):117-125
Wear of brake friction materials depends on many factors such as temperature, applied load, sliding velocity, properties of mating materials, and durability of the transfer layer. Prediction of friction materials wear versus their formulation and manufacturing conditions in synergy with brakes operating conditions can be considered as a crucial issue for further friction materials development. In this paper, the artificial neural network abilities have been used for predicting wear of the friction materials versus influence of all relevant factors. The neural model of friction materials wear has been developed taking into account: (i) complete formulation of the friction material (18 ingredients), (ii) the most important manufacturing conditions of the friction material (5 parameters), (iii) applied load and sliding velocity of the friction material both represented by work done by brake application, and (iv) brake interface temperature. 相似文献
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A new analysis for cold rolling of thin strip and foil is developed. This model follows the approach of Fleck et al. [8], but relaxes their assumption of a central flat neutral zone. Instead of following their inverse method to obtain the pressure distribution in this neutral zone, an explicit equation for the contact pressure variation is obtained from the sticking condition in this region. This significantly simplifies the solution method, leading to a much more robust algorithm. Moreover the method treats the cases either where the roll retains its circular arc or where there is very significant roll deformation in the same way, greatly simplifying the method of obtaining solutions. This will facilitate the incorporation of other effects such as the friction models currently being developed. Results are in line with the theory of Fleck et al. [8]. The effect of entry and exit tensions on the non-dimensional load and forward slip is investigated. It is found that the effect of equal entry and exit tensions is equivalent to reducing the yield stress of the strip by this tension stress. 相似文献
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Jean-Luc Marcelin 《The International Journal of Advanced Manufacturing Technology》2007,32(7-8):711-718
This work examines the possibility of using some classical optimization methods (augmented Lagrange multiplier method) or genetic algorithms and neural networks to optimize the cooling regime in hot-rolled complex beam processing. The objective is to optimize the cooling conditions so as to minimize the residual stresses. A detailed example shows that using this stochastic method and neural networks can be efficient. In other words, this work involves finite element analyses of both mechanical and thermodynamic properties of cooling beams, neural networks, genetic algorithms, and optimization to find a cooling regime with minimum residual stresses. 相似文献
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This paper starts with a profound analysis of rolling-force calculation models of two hot strip rolling product lines. According to classical theory of rolling-force calculation, the structure of rolling-force calculation model is firstly optimized. Then with application of data mining techniques, including clustering sampling approach and optimization algorithm, to the field data from product line, the value of key coefficients of the model is adjusted. By the proposed optimization scheme, a better rolling-force prediction is achieved. 相似文献
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运用神经网络处理非线性问题的优势,将其应用于带钢表面缺陷的识别与分类研究。本文采用灰度共生矩阵的特征提取,提出了基于BP神经网络进行缺陷识别与分类的方法,编制了带钢表面缺陷的识别与分类软件。分类测试表明,该软件有较好的识别与分类效果。 相似文献
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压电陶瓷驱动器的滑模神经网络控制 总被引:3,自引:3,他引:0
由于压电陶瓷驱动器的迟滞非线性严重影响其定位精度,本文提出了一种滑模神经网络控制方法来改善它的性能.用径向基函数神经网络的输出作滑模控制的等价控制量,由迟滞补偿器估计控射器参数误差、外部扰动和近似计算所造成的不确定量对神经网络的输出控制量进行补偿,从而使驱动器系统状态保持在滑模平面上.基于Lyapunov稳定性理论推导了控制器和补偿器的自适应调节律,分析了控制系统的收敛性和稳定性,以可变幅值的低频三角波为参考位移量对控制系统进行了实验测试与分析,结果表明,只采用神经网络控制时的平均定位误差为0.43 μm,最大误差为0.77 μm,而采用滑模控制方法对神经网络控制量进行补偿后,平均定位误差减小为0.27 μm,最大误差减小为0.49μm,定位精度有了显著的提高. 相似文献
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A method is presented to measure the depth-averaged horizontal velocity in distorted physical models, i.e., the depth-averaged horizontal velocity can be equivalent to a point velocity measured at approximately 0.6h below the water surface (h is the water depth). Further a scale for the Chézy coefficient in distorted models is deduced, and the velocity distributions in the vertical in prototype and distorted models are analyzed. By comparing data of the vertical variation of the horizontal velocity measured in a physical distorted model (with distortion ratio of 4.0) with prototype data, inaccuracies via the one-point method, the three-point method and the five-point method are analyzed. It is concluded that it is more accurate to measure the depth-averaged velocity via the three-point method than via the one-point method in the distorted model. What's more, the point velocity at 0.6h below the water surface approximates the depth-averaged velocity in the distorted physical model and the one-point method is proposed to be used in practice. 相似文献