基于SolidWorks二次开发的牙嵌式离合器参数化建模技术

针对牙嵌式离合器的快速设计建模问题,以SolidWorks 2010为开发平台,运用VB编程语言对矩形牙嵌式离合器参数化设计进行了编程。详细阐述了基于VB语言对SolidWorks二次开发的方法,建立了矩形牙嵌式离合器的尺寸参数数据库,研究了以DLL开发方式进行矩形牙嵌式离合器参数化设计的方法,完成了用户界面的开发。以程序驱动为特征,通过参数化驱动实现了矩形牙嵌式离合器的快速建模,在SolidWorks平台中生成了一个具有牙嵌式离合器尺寸全参数化驱动的自动建模模块。研究结果表明,该功能模块可以快速地从数据库读取参数和根据离合器传递最大转矩计算尺寸参数,并输出牙嵌式离合器的实体模型,增强了SolidWorks对特定零件的快速化建模能力,减少了设计工作量,提高了对牙嵌式离合器智能化和自动化设计的能力。     

A Transient Thermal Model for Mixed-Film Contacts

A thermal model is presented in this paper for deterministic calculations of the contact temperatures in mixed-film lubrication. A first-principal approach is taken to obtain the temperature results by solving transient energy equations in a domain consisting of three solid-film-solid regions. Various modeling techniques are used to feature model simplicity and to achieve a proper balance among solution accuracy, model robustness and computational efficiency. This thermal model may be incorporated into other deterministic isothermal mixed-film models to form a complete base model for mixed-film lubrication. It provides a platform upon which to model other effects such as lubricant additives, asperity plastic deformation and surface coating.     

STOCHASTIC PROGNOSTICS FOR ROLLING ELEMENT BEARINGS

The capability to accurately predict the remaining life of a rolling element bearing is prerequisite to the optimal maintenance of rotating machinery performance in terms of cost and productivity. Due to the probabilistic nature of bearing integrity and operation condition, reliable estimation of a bearing's remaining life presents a challenging aspect in the area of maintenance optimisation and catastrophic failure avoidance. Previous study has developed an adaptive prognostic methodology to estimate the rate of bearing defect growth based on a deterministic defect-propagation model. However, deterministic models are inadequate in addressing the stochastic nature of defect-propagation. In this paper, a stochastic defect-propagation model is established by instituting a lognormal random variable in a deterministic defect-propagation rate model. The resulting stochastic model is calibrated on-line by a recursive least-squares (RLS) approach without the requirement of a priori knowledge on bearing characteristics. An augmented stochastic differential equation vector is developed with the consideration of model uncertainties, parameter estimation errors, and diagnostic model inaccuracies. It involves two ordinary differential equations for the first and second moments of its random variables. Solving the two equations gives the mean path of defect propagation and its dispersion at any instance. This approach is suitable for on-line monitoring, remaining life prediction, and decision making for optimal maintenance scheduling. The methodology has been verified by numerical simulations and the experimental testing of bearing fatigue life.     

A Multiscale Approach to the Mixed Lubrication Regime: Application to Mechanical Seals

This paper presents a multiscale approach to solve the problem of mixed lubrication in mechanical seals. In fact, the lubricating fluid film developed between the faces of mechanical seals is usually a fraction of a micron in thickness, leading to a mixed lubrication regime. However, over a velocity threshold the fluid film can completely separate the faces because of the hydrodynamic effect due to the surface roughness, even if the surfaces are nominally parallel. To study this phenomenon, a deterministic model is preferable because the stochastic theory based on flow factors is unable to reproduce this effect. Unfortunately, a deterministic approach needs a prohibitive amount of nodes and computation time. This is why a multiscale model is proposed. It is composed of a micro-deterministic model working on a small area coupled with a macro model giving the pressure distribution on a macro-mesh. The results of the multiscale model are compared to those of a pure deterministic model in terms of accuracy and computation time when the area of the macro-cells is varied.     

考虑结构参数不确定性的随机模型修正方法

提出了一种随机模型修正方法以确定结构不确定性参数的概率统计特性,使得模型修正的应用更符合工程实际。将随机模型修正过程分解为一组确定性修正过程,利用蒙特卡罗仿真得到的响应样本并结合响应面模型的快速运算特性,构造优化反演过程来求得各个样本所对应的一组参数值,进而基于大量样本统计得到参数的均值和方差。所提出方法经过一组试验钢板的验证,准确求得了钢板厚度和材料参数的均值和方差,说明了方法的可行性和可靠性。     

Micropitting Modelling in Rolling–Sliding Contacts: Application to Rolling Bearings

In this article an engineering approach is described to model micropitting in rolling–sliding, heavily loaded lubricated contacts. The competitive mechanism between surface fatigue and mild wear is captured in the present approach as well as the effects of deterministic surface microgeometry (e.g., roughness). The fatigue model is based on the Dang Van fatigue criterion and the mild wear model uses a modified Archard approach. The complete modeling scheme is validated experimentally first using laboratory-controlled conditions, where the surface topography is varied as well as the operating conditions in the contact. Then the model is applied to describe the behavior of full-bearing tests. The behavior of the model agrees well with the experimental observations, qualitatively.     

采用Cellular Automaton法模拟铝合金的微观组织

采用Cellular Automaton微观模型，并与宏观的传热计算相结合，对砂型铸造铝合金铸件的凝固组织形成进行了模拟。在模拟过程中，采用连续形核的方法处理液态金属的异质形核现象。通过高斯分布函数描述表核质点密度随温度的分布关系，在给定过冷度时对分布函数求积分可得该时刻的形核密度。晶粒生长模型则考虑枝晶尖端生长动力学和择优生长方向（100）晶向，模拟计算结果表明，在冷却速度不变的情况下，随着形核分布参数△TN增加，所得到的晶粒尺寸增大。从数学角度对模拟结果进行了分析。     

A genetic algorithm approach to optimization of asynchronous automatic assembly systems

This paper presents the application of genetic algorithms to the performance optimization of asynchronous automatic assembly systems (AAS). These stochastic systems are subject to blocking and starvation effects that make complete analytic performance modeling difficult. Therefore, this paper extends genetic algorithms to stochastic systems. The performance of the genetic algorithm is measured through comparison with the results of stochastic quasi-gradient (SQM) methods to the same AAS. The genetic algorithm performs reasonably well in obtaining good solutions (as compared with results of SQM) in this stochastic optimization example, even though genetic algorithms were designed for application to deterministic systems. However, the genetic algorithm's performance does not appear to be superior to SQM.     

Local flexibility of an elliptical cracked shaft under bending and tension

Compared with the widely used straight front crack model, an elliptical front crack has been found to be more accurate and realistic for modeling the transverse surface crack in rotating machinery. Therefore, based on the strain energy release rate (SERR) approach and the closed-form results of SIFs, the local flexibilities of an elliptical cracked shaft submitted to bending, axial tension and combination of both are derived and solved by means of numerical integration technique. Once the elliptical front and shape variation are considered, the obtained flexibility values are reduced obviously in contrast with the current results, especially for the deeper surface cracks.     

Flight control of a hexa-rotor airship: Uncertainty quantification for a range of temperature and pressure conditions

The present paper is concerned with the dynamic modeling and design of control laws for a small non-rigid multi-rotor airship constituted of an oblate-spheroid helium balloon coupled with an electric-powered hexa-rotor airframe. The vehicle is assumed to operate in windless and low-speed conditions. A six-degree-of-freedom nonlinear dynamic model is derived for it using the Newton–Euler approach and considering, among other efforts, a restoring torque due to the displacement of the balloon’s center of buoyancy above the vehicle’s center of mass and the added-mass effect resulting from the air–structure interaction. Using the derived model and assuming a time-scale separation between the translational and rotational dynamics, the attitude and position control laws are designed separately from each other. Both laws are formulated using feedback linearization combined with control input saturation within appropriate parallelepipedal sets, which are carefully chosen to respect pre-defined bounds on the control torque, control force and maximum inclination angle. The effect of temperature and pressure fluctuations is taken into account through a parametric probabilistic approach, where Maximum Entropy Principle is used to construct a physically consistent stochastic model and Monte Carlo method is used as the stochastic solver to propagate the uncertainties through the system. Extensive simulation results show the effectiveness of the proposed control system and quantify the uncertainty of its performance over a wide range of local temperature and pressure.     

Dither signals and quantization

The accuracy performance that an be achieved by post-processing the samples provided by a dithered quantizer is analysed in this paper for both stochastic and deterministic dithering. In the former case the quantization error average and variance are considered and the number of effective bits of the system output is evaluated for uniformly and Gaussian distributed dither signals. Then the accuracy achieved by using a deterministic dithering is analysed and a new approach for optimizing the system performance is proposed. All the reported results have been validated by means of computer simulations.     

OPTIMAL CONTROL APPLIED IN AUTOMATIC CLUTCH ENGAGEMENTS OF VEHICLES

Start-up working condition is the key to the research of optimal engagement of auomaticclutch for AMT. In order to guarantee an ideal dynamic performance of the clutch engagement, anoptimal controller is designed by considering throttle angle, engine speed, gear ratio, vehicle accelera-tion and road condition. The minimum value principle is also introduced to achieve an optimal dy-namic performance of the nonlinear system compromised in friction plate wear and vehicle drive qual-ity. The optimal trajectory of the clutch engagement can be described in the form of explicit and ana-lytical expressions and characterized by the deterministic and accurate control strategy in stead ofindeterministic and soft control techniques which need thousands of experiments. For validation of thecontroller, test work is carried out for the automated clutch engagements in a commercial car with antraditional mechanical transmission, a hydraulic actuator, a group of sensors and a portable computersystem. It is shown through     

电控液动气助力离合器操控系统温度模型辨识

为了提高电控液动气助力离合器自动操控系统对温度的自适应能力,从理论和实验两个方面研究了该系统的温敏特性。在试验数据的基础上建立了离合器自动操控系统接合速度与温度、占空比之间的经验公式,并在不同温度下通过试验对该公式的正确性和有效性进行了验证。试验结果表明：离合器操控系统温度模型建模的理论和方法正确,所得经验公式在实际运用过程中取得了理想的效果。     

离合器摩擦副表面温度对摩擦因数的影响

通过对某型离合器摩擦副的摩擦学小样试验,研究了离合器在结合的滑动摩擦过程中,摩擦面温度对离合器摩擦材料摩擦因数的影响.采用扫描电子显微镜(SEM),分析了样件的摩擦表面形貌,探讨了产生影响的机制,并从摩擦因数角度探讨了微车离合器起步发抖和烧蚀的主要原因.微车离合器摩擦材料摩擦因数随着摩擦面温度先升高,然后趋于稳定,最后再降低,其稳定工作的温度区间为130～220℃;在摩擦面温度较低的工况下,摩擦因数较低,微车起步时,离合器传递的扭矩不足以克服道路阻力,引起微车起步发抖的现象;而在摩擦面温度过高的工况下,离合过程中,摩擦因数较低,传递扭矩效率低,导致离合器滑磨时间过长,引起烧蚀现象.     

3D multiscale segmentation and morphological analysis of x‐ray microtomography from cold‐sprayed coatings

X‐ray microtomography from cold‐sprayed coatings brings a new insight on this deposition process. A noise‐tolerant segmentation algorithm is introduced, based on the combination of two segmentations: a deterministic multiscale segmentation and a stochastic segmentation. The stochastic approach uses random Poisson lines as markers. Results on a X‐ray microtomographic image of aluminium particles are presented and validated.     

Surface generation modeling of micro milling process with stochastic tool wear

Micro milling is widely used to manufacture miniature parts and features at high quality with low set-up cost. To achieve a higher quality of existing micro products and improve the milling performance, a reliable analytical model of surface generation is the prerequisite as it offers the foundation for surface topography and surface roughness optimization. In the micro milling process, the stochastic tool wear is inevitable, but the deep influence of tool wear hasn't been considered in the micro milling process operation and modeling. Therefore, an improved analytical surface generation model with stochastic tool wear is presented for the micro milling process. A probabilistic approach based on the particle filter algorithm is used to predict the stochastic tool wear progression, linking online measurement data of cutting forces and tool vibrations with the state of tool wear. Meanwhile, the influence of tool run-out is also considered since the uncut chip thickness can be comparable to feed per tooth compared with that in conventional milling. Based on the process kinematics, tool run-out and stochastic tool wear, the cutting edge trajectory for micro milling can be determined by a theoretical and empirical coupled method. At last, the analytical surface generation model is employed to predict the surface topography and surface roughness, along with the concept of the minimum chip thickness and elastic recovery. The micro milling experiment results validate the effectiveness of the presented analytical surface generation model under different machining conditions. The model can be a significant supplement for predicting machined surface prior to the costly micro milling operations, and provide a basis for machining parameters optimization.     

Wet Clutch Friction Interfaces under Water-Contaminated Lubricant Conditions

The performance of wet clutches used for automatic transmissions or other applications usually includes the desired positive friction characteristics and a shudder-free torque generation. Changes in the operating variables such as the lubricant conditions influence the formation of a tribofilm, and friction characteristics and can alter the degradation of the friction interfaces. In this work, the friction characteristics and degradation of the paper–steel friction interfaces were monitored when a commercial fully formulated automatic transmission fluid (ATF) was contaminated with water. It was found that water in ATF influenced the clutch stability by increasing the mean coefficient of friction (µ) and the negative friction–velocity slope. Surface studies of the posttest friction interfaces clearly indicated reduced surface porosity and permeability, increased wettability, and changed elemental composition on the contacting surfaces after tested with water-contaminated ATF. Moreover, water-contaminated paper liners' thermal decomposition shifted to a lower temperature compared to an uncontaminated liner during thermal analyses. These results displayed faster degradation and reduced service life of the clutch friction interfaces due to water contamination. The resultant surface condition can be associated with the observed unstable friction and negative friction–velocity slopes.     

Statistical evaluation of forming limit in hydroforming process using plastic instability combined with FORM

Deviation of raw material parameters, such as work hardening, anisotropy, yield stress, etc., leads to an uncertainty to the position of the forming limit curve (FLC). This paper presents a novel approach to statistically evaluate the forming limit in hydroforming processes when taking into account the variations in the material parameters. First, plastic instability based on the Hill’s quadratic plastic potential is employed to construct the deterministic FLC. Then, with using the assumption that all material parameters are normally distributed random variables, stochastic modeling of the FLC with a confidence level is carried out, and statistical evaluation of the FLC is performed. In this work, a first-order reliability method is adopted for the reliability assessment of the FLC, and this is verified with the Monte-Carlo simulation method.     

Surface roughness modeling and optimization of tungsten–copper alloys in micro-milling processes

Nowadays, the micrometric and nanometric dimensional precision of industrial components is a common feature of micro-milling manufacturing processes. Hence, great importance is given to such aspects as online metrology and real-time monitoring systems for accurate control of surface roughness and dimensional quality. A real-time monitoring system is proposed here to predict surface roughness with an estimation error of 9.5%, by using the vibration signal that is emitted during the milling process. In the experimental setup, the z-axis component vibration is measured using two different diameters under several cutting conditions. Then, an adaptive neuro-fuzzy inference system model is implemented for modeling surface roughness, yielding a high goodness of fit indices and a good generalization capability. Finally, the optimization process is carried out by considering two contradictory objectives: unit machining time and surface roughness. A multi-objective genetic algorithm is used to solve the optimization problem, obtaining a set of non-dominated solutions. Pareto front representation is a useful decision-making tool for operators and technicians in the micro-milling process. An example of the Pareto front utility-based approach that selects two points close to both extreme ends of the frontier is described in the paper. In the first case (point 1), machine time is of greater importance, and in the second case (point 2), importance is attached to surface roughness. In general terms, users can select different combinations, at all times moving along the Pareto front.