温压粉末冶金材料的性能与公差

为提高粉末冶金对其他制造技术的竞争性，需要适宜的合金系以及在降低加工费用的情况下提高力学性能并保持公差控制精度的工艺技术。在加热了的模具中压制加热的粉末是一种经一道工序就使密度提高到7.2～7.5g／cm3的新工艺。通过该工艺，可以得到相当于复压／复烧的密度水准与力学性能。然而，通过温压提高性能须兼而保持与传统粉冶工艺路线相同等级的公差猜度。本文介绍了这种特性粉末以及一些低合金粉冶材料（在温压与烧结后）的力学性能。同时也叙述了用温压工艺生产部件时有关公差精度的一些试验结果。     

连轧工作辊形位公差的合理选择

着重论述了连轧工作形位公差的合理选择,并科学地分折了不用部位形位公差的相互关系,提出的经验公式为连扎工作辊的设计及加工开拓了新的思路。     

形位公差的合理确定

在机械产品设计过程中，合理地选择形位公差以数是保证零件达到使用要求、提高产品质量、降低成本的重要措施，论述了形位公差各项目之间的关系及合理地选择标注的方法。     

连轧工作辊形位公差的探讨

本文着重论述了连轧工作辊形位公差的合理选择，并科学地分析了不同部位形位公差的相互关系，提出的经验公式为连扎工作辊的设计及加工开拓了新的思路。     

炼钢-连铸作业计划的遗传优化模型

  从提高炼钢 连铸作业计划可执行性的角度,研究了能适应动态生产环境变化的作业计划编制方法。在考虑连铸机的连浇约束、炼钢 连铸生产工艺要求和作业时间不确定性的条件下,建立了以最小化工位作业冲突时间和尽可能早的安排连铸机开浇时间为目标的炼钢-连铸生产作业计划优化模型,并构建了一种分段实数编码和基因分区交叉操作的改良遗传算法,将其与沿生产流程时间并行倒推算法相结合形成优化算法来求解模型。某钢厂8 h的炼钢 连铸作业计划编制案例表明：建立的模型与求解算法能较好地解决炼钢-连铸作业计划的时间不确定性优化问题,可快速生成炉次间作业无冲突的优化生产作业计划。     

烧结工序能耗预测与优化研究

烧结工序能耗预测与优化研究是确保生产有序合理、节能环保和低成本的重要手段.在烧结工序能耗定义分析及烧结工序能耗主要影响因素分析的基础上,建立了基于径向基神经网络-遗传算法(RBF-GA)的烧结能耗预测与优化模型.在神经网络模型对能耗高精度预报的基础上结合遗传算法求解优化模型,计算出最佳的输入参数组合.通过案例研究,验证了所提方法的正确性和有效性.     

基于混沌粒子群——专用遗传算法切换策略的移动机器人路径规划

为了发挥粒子群算法和专用遗传算法的各自优点,提出了一种将二者结合的切换优化策略.该策略前期采用一种基于种群最优个体混沌化的混沌粒子群算法,后期选用专用遗传算法.通过大量仿真实验确定了在迭代代数、种群标准差和最优个体适应度差三种切换指标下各自的最优切换条件.与单一专用遗传算法和单一混沌粒子群算法的仿真对比表明:本文提出的切换优化策略在综合路径长度、平滑性和规划时间三个性能指标后具有一定的优越性.      

起落架接头模锻件的尺寸和形位公差控制

文章以波音起落架接头为例，叙述了模锻件的尺寸和形位公差控制的主要方法。其一，是终锻模的尺寸控制，它由型腔尺寸、型腔制造公差和样板制造公差来控制。其二是形位公差的控制，它包括错移和直线度（翘曲）两个方面，错移由导柱和导壁的精度保证，翘曲则由模锻工序、热处理时装框方式及淬火后的矫直来控制，文中还介绍了用冷终锻模直薄壁高筋模锻件的方法。     

基于遗传算法优化BP神经网络的小球藻生长模型的建立与应用

[目的]探讨用遗传算法优化BP 神经网络对小球藻生长模型的建立与应用.[方法]使用遗传算法对BP 神经网络的权值和阈值进行优化,并使用该网络模型,以小球藻培养时间和残余葡萄糖为输入,菌体光密度值(OD<,680>)为输出,对小球藻在500 L多功能生物反应器中的生长情况进行了建模,还探讨了该模型的应用情况.[结果]经过遗传算法优化的BP 神经网络,其泛化值的误差平方和比BP神经网络的小,因而预测值更加接近实际值.t 检验表明,所建立的模型是可信的.验证表明,该模型具有良好的拟合度,能够很好地描述在500 L 多功能生物反应器中培齐的小球藻的生物量(OD<,680>)与残余葡萄糖和培养时间之间的关系.[结论]所建立的模型可用于试验结果的预测,对小球藻的培养控制具有指导意义.     

多核平台上基于可声明并行性的程序优化框架

针对多核体系平台上充分、有效地发掘目标程序中各种可用并行性的需求,通过引入"层次关系"、"等价关系"和"特性权重"的支持,提出了一种扩展的TStreams模型,并在此基础上实现了一个基于可声明并行性的程序并行优化框架(FAPOF).该框架支持用户对算法的并行特性进行多角度、多粒度的描述并指定适用的各类并行优化规则.基于用户描述,框架可以编译驱动的方式评估各种优化决策的组合,以半自动化的方式对目标程序进行并行优化.由此可将优化过程中程序员原本复杂、困难的并行优化的"决策"工作转化为可用并行性的"描述"工作.实验结果表明,此方法显著地降低了并行优化的难度,提高了并行优化的效率.     

Two-Stepped Evolutionary Algorithm and Its Application to Stability Analysis of Slopes

Based on genetic algorithm and genetic programming, a new evolutionary algorithm is developed to evolve mathematical models for predicting the behavior of complex systems. The input variables of the models are the property parameters of the systems, which include the geometry, the deformation, the strength parameters, etc. On the other hand, the output variables are the system responses, such as displacement, stress, factor of safety, etc. To improve the efficiency of the evolution process, a two-stepped approach is adopted; the two steps are the structure evolution and parameter optimization steps. In the structure evolution step, a family of model structures is generated by genetic programming. Each model structure is a polynomial function of the input variables. An interpreter is then used to construct the mathematical expression for the model through simplification, regularization, and rationalization. Furthermore, necessary internal model parameters are added to the model structures automatically. For each model structure, a genetic algorithm is then used to search for the best values of the internal model parameters in the parameter optimization step. The two steps are repeated until the best model is evolved. The slope stability problem is used to demonstrate that the present method can efficiently generate mathematical models for predicting the behavior of complex engineering systems.     

Optimization of Pile Groups Using Hybrid Genetic Algorithms

This paper presents an automated optimal design method using a hybrid genetic algorithm for pile group foundation design. The design process is a sizing and topology optimization for pile foundations. The objective is to minimize the material volume of the foundation taking the configuration, number, and cross-sectional dimensions of the piles as well as the thickness of the pile cap as design variables. A local search operator by the fully stressed design (FSD) approach is incorporated into a genetic algorithm (GA) to tackle two major shortcomings of a GA, namely, large computation effort in searching the optimum design and poor local search capability. The effectiveness and capability of the proposed algorithm are first illustrated by a five by five pile group subjected to different loading conditions. The proposed optimization algorithm is then applied to a large-scale foundation project to demonstrate the practicality of the algorithm. The proposed hybrid genetic algorithm successfully minimizes the volume of material consumption and the result matches the engineering expectation. The FSD operator has great improvement on both design quality and convergence rate. Challenges encountered in the application of optimization techniques to design of pile groups consisting of hundreds of piles are discussed.     

Fuzzy Logic Integrated Genetic Programming for Optimization and Design

A fuzzy logic integrated genetic programming (GP) based methodology is proposed to increase the performance of the GP based approach for structural optimization and design. Fuzzy set theory is employed to deal with the imprecise and vague information, especially the design constraints, during the structural design process. A fuzzy logic based decision-making system incorporating expert knowledge and experience is used to control the iteration process of genetic search. Illustrative examples have been used to demonstrate that, when comparing the proposed fuzzy logic controlled GP approach with the pure GP method, the proposed new approach has a higher search efficiency.     

Augmented Lagrangian Genetic Algorithm for Structural Optimization

This paper presents a robust hybrid genetic algorithm for optimization of space structures using the augmented Lagrangian method. An attractive characteristic of genetic algorithm is that there is no line search and the problem of computation of derivatives of the objective function and constraints is avoided. This feature of genetic algorithms is maintained in the hybrid genetic algorithm presented in this paper. Compared with the penalty function‐based genetic algorithm, only a few additional simple function evaluations are needed in the new algorithm. Furthermore, the trial and error approach for the starting penalty function coefficient and the process of arbitrary adjustments are avoided. There is no need to perform extensive numerical experiments to find a suitable value for the penalty function coefficient for each type or class of optimization problem. The algorithm is general and can be applied to a broad class of optimization problems.     

蒙古铁矿块矿直接入炉基础冶金性能

摘要：高炉装料制度是复杂高炉炼铁中调节炉况运行状态的重要上部调剂手段,炉料在布料矩阵操作参数下在炉喉处所形成的空间分布是影响炉内煤气流分布和高炉炉况波动的重要因素之一。合理的调控与优化高炉装料所产生的料面形状,给出布料矩阵优化计算的理论依据,是保证高炉稳定顺行,提高资源利用率和减少污染物排放的有效途径。结合无钟炉顶的设备结构与布料工艺,针对期望料层厚度分布研究布料矩阵的优化计算方法,进行完善与改进;同时以期望料面输出形状为设定目标,建立了期望料面输出形状优化模型并通过遗传算法实现对布料矩阵的优化计算。最后,通过工业过程的实测数据对PSO优化方法和遗传算法优化方法进行对比验证,结果表明,使用基于遗传算法的优化模型能够有效地制定布料矩阵,符合期望目标。     

基于遗传算法的高炉布料矩阵的优化计算

摘要：高炉装料制度是复杂高炉炼铁中调节炉况运行状态的重要上部调剂手段,炉料在布料矩阵操作参数下在炉喉处所形成的空间分布是影响炉内煤气流分布和高炉炉况波动的重要因素之一。合理的调控与优化高炉装料所产生的料面形状,给出布料矩阵优化计算的理论依据,是保证高炉稳定顺行,提高资源利用率和减少污染物排放的有效途径。结合无钟炉顶的设备结构与布料工艺,针对期望料层厚度分布研究布料矩阵的优化计算方法,进行完善与改进;同时以期望料面输出形状为设定目标,建立了期望料面输出形状优化模型并通过遗传算法实现对布料矩阵的优化计算。最后,通过工业过程的实测数据对PSO优化方法和遗传算法优化方法进行对比验证,结果表明,使用基于遗传算法的优化模型能够有效地制定布料矩阵,符合期望目标。     

Pipe Index Vector: A Method to Improve Genetic-Algorithm-Based Pipe Optimization

This paper proposes a methodology for the optimal design of water distribution systems based on genetic algorithms. The objective of the optimization is to minimize the capital cost, subject to ensuring adequate pressures at all nodes during peak demands. The proposed method is novel in that it involves the use of a pipe index vector to control the genetic algorithm search. The pipe index vector is a measure of the relative importance of pipes in a network in terms of their impact on the hydraulic performance of the network. By using the pipe index vector it is possible to exclude regions of the search space where impractical and infeasible solutions exist. By reducing the search space it is possible to generate feasible solutions more quickly and hence process much healthier populations than would be the case in a standard genetic algorithm. This results in optimal solutions being found in a fewer number of generations resulting in a substantial saving in terms of computational time. The method has been tested on several networks, including networks used for benchmark testing least cost design algorithms, and has been shown to be efficient and robust.     

多目标遗传算法在八辊五机架全连续冷连轧机轧制策略优化中的应用

针对八辊五机架全连续冷连轧机在轧制过程中的特点，提出一种多目标优化问题的求解策略——多目标遗传算法，并对其进行改进，提出基于多目标遗传算法的多辊冷连轧机轧制策略的优化。该方法具有计算精度高、速度快等特点，适合在线实时计算的要求。该优化算法在河北中钢1250mm八辊五机架全连续冷连轧机中的应用，证明了该算法的适用性和灵活性，为多辊全连续冷连轧机在计算机过程控制中进行轧制策略优化设计提供了一条新的途径。     

Object-Oriented Optimization Approach Using Genetic Algorithms for Lattice Towers

A new approach is presented for the optimization of steel lattice towers by combining genetic algorithms and an object-oriented approach. The purpose of this approach is to eliminate the difficulties in the handling of large size problems such as lattice towers. Improved search and rapid convergence are obtained by considering the lattice tower as a set of small objects and combining these objects into a system. This is possible with serial cantilever structures such as lattice towers. A tower consists of panel objects, which can be classified as separate objects, as they possess an independent property as well as inherent properties. This can considerably reduce the design space of the problem and enhance the result. An optimization approach for the steel lattice tower problem using objects and genetic algorithms is presented here. The paper also describes the algorithm with practical design considerations used for this approach. To demonstrate the approach, a typical tower configuration with practical constraints has been considered for discrete optimization with the new approach and compared with the results of a normal approach in which the full tower is considered.     

Object-Oriented Framework for Genetic Algorithms with Application to Space Truss Optimization

Genetic algorithms have been shown to be very effective optimization tools for a number of engineering problems. Since the genetic processes typically operate independent of the actual problem, a core genetic algorithm library consisting of all the genetic operators having an interface to a generic objective function can serve as a very useful tool for learning as well as for solving a number of practical optimization problems. This paper discusses the object-oriented design and implementation of such a core library. Object-oriented design, apart from giving a more natural representation of information, also facilitates better memory management and code reusability. Next, it is shown how classes derived from the implemented libraries can be used for the practical size optimization of large space trusses, where several constructibility aspects have been incorporated to simulate real-world design constraints. Strategies are discussed to model the chromosome and to code genetic operators to handle such constraints. Strategies are also suggested for member grouping for reducing the problem size and implementing move-limit concepts for reducing the search space adaptively in a phased manner. The implemented libraries are tested on a number of large previously fabricated space trusses, and the results are compared with previously reported values. It is concluded that genetic algorithms implemented using efficient and flexible data structures can serve as a very useful tool in engineering design and optimization.