变速器传动系统齿轮敲击噪声参数优化

为了获得变速器敲击噪声产生机理和参数影响规律,针对国产某型机械式变速器敲击噪声问题,建立传动系统空转齿轮敲击动力学模型。基于齿轮敲击噪声与敲击强度关系模型,以变速器整体敲击噪声最小化为目标函数,利用直接积分和遗传算法对各挡齿轮等效质量和齿侧间隙进行优化,优化后整体敲击噪声最大降幅达到4.0d B,变速器系统NVH性能得到明显改善。该研究对指导变速器齿轮传动系统低噪声设计具有理论意义和实用价值。     

齿轮实体参数化造型的新途径

提出了逆向二次开发CAD软件的理念,并通过应用Visual Basic程序语言,基于三维机械CAD软件SolidWorks97平台,开发齿轮三维实体参数化造型设计系统的实例,介绍了应用该方法逆向开发三维CAD软件的全过程.解决了工业生产中齿轮三维实体造型设计的难题,并提供了一种对三维CAD软件开发的方法,使设计者掌握运用该方法进行实际开发,提高开发CAD软件的能力.     

基于实例推理的小型液压机参数化

结合小型液压机企业的实际需求,充分利用现有资源,基于实例推理的思想,引入参数化理论和有限元技术,采用vc++对SolidWorks进行二次开发,开发了一个由参数化CAD模块、数据库模块和有限元分析与优化模块构成的小型液压机参数化CAD系统.使用结果表明该系统大大提高了设计效率.     

变压器CAD系统的设计

介绍了变压器CAD系统开发的必要性 ,阐述了所开发的变压器CAD系统的总体设计 ,主要包括两大模块设计 ,即变压器的电磁优化计算模块和结构设计模块 .其中电磁优化计算模块又包括整体和部分优化两个子模块以适应不同产品开发的需要 ,结构设计模块也包括三个子模块以完成绘图、图纸管理的需要 .并对开发环境的选择进行了讨论 .     

基于遗传算法汽车动力总成悬置系统解耦优化

为避免传统优化算法在对汽车动力总成悬置系统优化中陷入局部最优解,采用遗传算法对其进行优化。在深入分析设计变量选取、约束函数的提取及目标函数的选取原则基础上,以悬置刚度为优化变量、固有频率的范围和固有频率之差为约束函数、六自由度方向的解耦率为目标函数,利用MATLAB平台的遗传算法进行优化。开发基于遗传算法汽车动力总成悬置系统解耦优化系统,并对某型号汽车动力总成系统优化。优化结果表明:系统的固有频率的分配和解耦率得到极大的改善,效率和精度都得到很大的提升。     

基于AutoCAD2000的量具CAD系统的研发

通过对批生产中量规设计的特点分析 ,利用VLISP编程环境 ,开发了基于AutoCAD2 0 0 0的量具CAD系统 ,该系统实现了量规的自动设计     

基于SolidWorks的挤压模CAD系统

CAD技术已逐渐从二维系统向三维实体造型方面转变.以Solidworks作为支撑平台,以Visual Basic和Access为开发工具,应用面向对象的程序开发方法,开发了一个挤压模CAD系统.对该系统的设计做了概括性的描述,并介绍了该系统的软件开发过程中的关键技术.     

用遗传算法计算设计多薄层雷达吸波材料的程序实现技术

本研究探索一种集成式多薄层吸波材料的优化设计系统的方法及程序实现技术.该设计系统采用传输线理论和跟踪计算方法来计算多层吸波材料的反射率,以遗传算法作为优化引擎.利用开放源代码数据库服务器软件MYSQL实现了材料电磁参数数据库.在开放源代码软件平台下,创建了多薄层RAMs计算设计和性能预报智能系统.对几种典型的实际材料,进行了多代遗传、进化等优化计算,给出了比较理想的优化设计结果,并对优化结果和材料智能预报系统功能进行了技术评价.     

求解约束优化问题的退火遗传算法

针对基于罚函数遗传算法求解实际约束优化问题的困难与缺点,提出了求解约束优化问题的退火遗传算法。对种群中的个体定义了不可行度,并设计退火遗传选择操作。算法分三阶段进行,首先用退火算法搜索产生初始种群体,随后利用遗传算法使搜索逐渐收敛于可行的全局最优解或较优解,最后用退火优化算法对解进行局部优化。两个典型的仿真例子计算结果证明该算法能极大地提高计算稳定性和精度。     

汽车减振器的CAD系统

文章建立了汽车减振器的非线性数学模型，并用Matlab对减振器系统进行了仿真，开发和设计，介绍了汽车减振器CAD系统的结构和主要功能模块，并把该系统应用到红旗汽车的减振器中，由实验和系统的仿真数据的比较得出，该CAD系统有广阔的应用前景。     

Optimization of noisiness of mechanical system by using a pneumatic tuner during a failure of piston machine

The stage of construction and operation of the machines requires monitoring of the steps causing negative effects on humans and environment in the form of noise during their use. Current technologies allow for appropriate optimization of mechanical systems as part of these machines. One possible optimization of the mechanical systems is considered to be the use of pneumatic tuners. The aim of this article is to demonstrate the effect of the pneumatic tuner on noisiness of mechanical system even in case of failure of mechanical drive part. Performed experimental measurement presents the change of noise in mechanical system by changing a pressure of gaseous medium in pneumatic tuner's compression space. The solution is an issue of appropriate mechanical tuning system when there is a change of dynamic parameters of the system and thus to change the entire mechanical system noise. Subsequent obtained results set out suggestions for the use of pneumatic tuners in mechanical systems in order to achieve the lowest possible noise during their operation mode.     

Optimal design of multi-state weighted k-out-of-n systems based on component design

This paper presents a study on design optimization of multi-state weighted k-out-of-n systems. The studied system reliability model is more general than the traditional k-out-of-n system model. The system and its components are capable of assuming a whole range of performance levels, varying from perfect functioning to complete failure. A utility value corresponding to each state is used to indicate the corresponding performance level. A widely studied reliability optimization problem is the “component selection problem”, which involves selection of components with known reliability and cost characteristics. Less adequately addressed has been the problem of determining system cost and utility based on the relationships between component reliability, cost and utility. This paper addresses this topic. All the optimization problems dealt with in this paper can be categorized as either minimizing the expected total system cost subject to system reliability requirements, or maximizing system reliability subject to total system cost limitation. The resulting optimization problems are too complicated to be solved by traditional optimization approaches; therefore, genetic algorithm (GA) is used to solve them. Our results show that GA is a powerful tool for solving these kinds of problems.     

Soft computing approach for reliability optimization: State-of-the-art survey

In the broadest sense, reliability is a measure of performance of systems. As systems have grown more complex, the consequences of their unreliable behavior have become severe in terms of cost, effort, lives, etc., and the interest in assessing system reliability and the need for improving the reliability of products and systems have become very important. Most solution methods for reliability optimization assume that systems have redundancy components in series and/or parallel systems and alternative designs are available. Reliability optimization problems concentrate on optimal allocation of redundancy components and optimal selection of alternative designs to meet system requirement. In the past two decades, numerous reliability optimization techniques have been proposed. Generally, these techniques can be classified as linear programming, dynamic programming, integer programming, geometric programming, heuristic method, Lagrangean multiplier method and so on. A Genetic Algorithm (GA), as a soft computing approach, is a powerful tool for solving various reliability optimization problems. In this paper, we briefly survey GA-based approach for various reliability optimization problems, such as reliability optimization of redundant system, reliability optimization with alternative design, reliability optimization with time-dependent reliability, reliability optimization with interval coefficients, bicriteria reliability optimization, and reliability optimization with fuzzy goals. We also introduce the hybrid approaches for combining GA with fuzzy logic, neural network and other conventional search techniques. Finally, we have some experiments with an example of various reliability optimization problems using hybrid GA approach.     

Reliability modeling and optimization of K/N phased mission system with backup missions and global redundancy strategy

The mission success probability (MSP) is a critical indicator for phased mission systems (PMSs). In the modern aerospace industry, redundancy techniques, including component/phase redundancy, are commonly seen to increase the MSP of the whole system. These component/phase redundancies make the reliability analysis more complex. Meanwhile, one or more components are required for normal working for different subsystems, called the K/N structure. In this article, a Markov-process method is proposed for PMS with K/N subsystems and different redundancy strategies. Then, a universal system optimization model is proposed to optimize system structure and redundancy strategies for all subsystems at the same time. Then, an improved genetic algorithm (GA) is used to resolve the optimization problem. At last, a propulsion system is used as an engineering case, showing the proposed binary decision diagram-based method.     

铸造工艺CAD组件集成环境的研究

以铸造工艺CAD为例介绍了组件集成环境的结构和设计方法。在一套基于通用的组件结构的铸造工艺设计组件库的基础上，通过组件的注册、对象的管理和对象属性的存取等方面，设计出一个易于扩充新设计方法的铸造工艺设计计算的组件集成环境。该方法不仅可用于铸件重量和模数计算及铸造工艺计算，还可用于进一步开发完善的各类专业工艺CAD或计算机图形软件。     

基于互联网WWW服务的气动仿真及选型系统

 为了能够方便快捷地进行气动系统的选型和设计，将气动技术和网络技术相结合，在互联网上实现了基于www 服务的气动系统单个元件选型和整个系统仿真的功能． 该系统由ASP和sQI 服务器构成，采用客户端和服务端共同仿真的方式实现．系统升级和更新只在服务器上进行，实现一元化管理．系统提供服务后可以使工作人员在任何地方只要连上互联网就能够对气动系统进行选型和仿真．     

Numerical studies on constrained venting system with reactive mufflers by GA optimization

While the space volume of mufflers in a venting system gets constrained, shape optimization to maximize the muffler's performance becomes important and essential. This paper presents a genetic algorithm (GA) for the optimal shape design of mufflers. The four‐pole matrix method which was adopted in evaluating the acoustic performance of sound transmission loss (STL) is used in conjunction with the GA techniques. Case studies of the full band noise inside a venting system are exemplified by the reactive mufflers. Before the GA operation, several examples are tested and compared with the experimental data for accuracy check of the mathematical models. Consequently, GA can provide a quick and effective way for a muffler design work. Copyright © 2005 John Wiley & Sons, Ltd.     

Surveillance test policy optimization through genetic algorithms using non-periodic intervention frequencies and considering seasonal constraints

In order to maximize systems average availability during a given period of time, it has recently been developed a non-periodic surveillance test optimization methodology based on genetic algorithms (GA). The fact of allowing non-periodic tests turns the solution space much more flexible and schedules can be better adjusted, providing gains in the overall system average availability, when compared to those obtained by an optimized periodic test scheme. This approach, however, turns the optimization problem more complex. Hence, the use of a powerful optimization technique, such as GA, is required.Considering that some particular features of certain systems can turn it advisable to introduce other specific constraints in the optimization problem, this work investigates the application of seasonal constraints for the set of the Emergency Diesel Generation of a typical four-loop pressurized water reactor in order to planning and optimizing its surveillance test policy. In this analysis, the growth of the blackout accident probability during summer, due to electrical power demand increases, was considered. Here, the used model penalizes surveillance test interventions when the blackout probability is higher.Results demonstrate the ability of the method in adapting the surveillance test policy to seasonal constraints. The knowledge acquired by the GA during the searching process has lead to test schedules that drastically minimize test interventions at periods of high blackout probability. It is compensated by more frequent redistributed tests through the periods of low blackout probability in order to improve on the overall average availability at the system level.     

Redundancy allocation for multi-state systems using physical programming and genetic algorithms

This paper proposes a multi-objective optimization model for redundancy allocation for multi-state series–parallel systems. This model seeks to maximize system performance utility while minimizing system cost and system weight simultaneously. We use physical programming as an effective approach to optimize the system structure within this multi-objective optimization framework. The physical programming approach offers a flexible and effective way to address the conflicting nature of these different objectives. Genetic algorithm (GA) is used to solve the proposed physical programming-based optimization model due to the following three reasons: (1) the design variables, the number of components of each subsystems, are integer variables; (2) the objective functions in the physical programming-based optimization model do not have nice mathematical properties, and thus traditional optimization approaches are not suitable in this case; (3) GA has good global optimization performance. An example is used to illustrate the flexibility and effectiveness of the proposed physical programming approach over the single-objective method and the fuzzy optimization method.     

Optimization of imperfect preventive maintenance for multi-state systems

The paper generalizes a preventive maintenance optimization problem to multi-state systems, which have a range of performance levels. Multi-state system reliability is defined as the ability to satisfy given demand. The reliability of system elements is characterized by their hazard functions. The possible preventive maintenance actions are characterized by their ability to affect the effective age of equipment. An algorithm is developed which obtains the sequence of maintenance actions providing system functioning with the desired level of reliability during its lifetime by minimum maintenance cost.To evaluate multi-state system reliability, a universal generating function technique is applied. A genetic algorithm (GA) is used as an optimization technique. Basic GA procedures adapted to the given problem are presented. Examples of the determination of optimal preventive maintenance plans are demonstrated.