基于BP网络和遗传算法的蜗杆传动优化设计CAD系统

如何将优化方法和CAD建模过程无缝集成在一起是一个急需解决的有重大应用价值的课题。以蜗杆传动优化设计CAD系统的开发为例进行了初步探索。首先介绍了蜗杆传动优化设计的两大关键技术:基于BP网络实现图表的逼近,基于遗传算法求解混合离散变量优化问题。然后根据国家最新标准,建立蜗杆传动优化设计的规范化数学模型。最后介绍了Visual Studio 2008环境下采用Pro/Toolkit二次开发Pro/ENGINEER Wildfire 4.0软件开发蜗杆传动优化设计CAD系统的过程。

基于遗传算法的产品人机CAD研究

将产品形态的人机设计问题从概念设计到详细设计分为交互模式设计、关联形态设计和独立形态设计3个紧密联系的层。应用进化设计和遗传算法的基本原理,针对不同层次人机设计问题的特点分别进行具体研究,制订了相应的解决策略,分析了其编码方案与适应度函数的构造方式。最后构建了基于设计/制造一体化的人机进化设计过程原型系统。

基于Solidworks的人机工程学CAD二次开发

基于Solidworks造型平台,以VC++为二次开发工具研制了产品形态的人机工程学CAD软件。实现了人机工程学标准人体数据的查询、辅助设计人体模型的建立和人机信息的实时获取,并实现了通过人体模型对产品尺寸进行驱动设计的方法。文章介绍了该人机CAD软件模块的基本功能和结构,论述了各工具集具体功能的实现方法。

产品人机接触曲面形状的CAD研究

对产品形态设计中人机接触曲面的人机工程学性能的评价与设计方法进行了分析,研究了合理人机接触曲面设计的解决方案,应用遗传算法工具实现了对人机接触曲面形态解方案的随机搜索,以及通过程序进行自动评价与选择的机制,建立了基于Unigraphics造型平台的曲面人机设计过程机制与原型系统。

色彩配置方案的抽取与设计重用CAD方法研究

设计实例中的色彩配置方案往往代表某一类型的设计风格、惯例或趋势,这对新的设计有重要的参考价值和指导意义。论文基于遗传算法研究了从参考设计实例中抽取色彩配置方案、并将其运用于新设计的方法,同时建立了色彩方案的量化评价方法,应用VC++开发了色彩配置方案设计的原型系统。

基于均匀设计和NN-GA的CAD模型多目标直接优化方法

提出了一种基于试验数据的多目标直接优化方法,该方法采用均匀设计原理安排试验方案,利用三维CAD模型的参数化驱动特点,完成虚拟试验过程和数据获取,以试验数据为训练样本,建立了CAD模型参数与目标之间的非线性映射关系的神经网络模型,运用Pareto遗传进化算法和小生境技术对CAD模型进行多目标参数优化,可在指定区域内找出CAD模型的Pareto最优解集。

基于遗传-蚁群算法的CAD产品快速建模

为提高CAD造型的设计效率,提出一种基于遗传-蚁群算法的CAD产品快速建模方法,该方法采用遗传算法求得次优解,依据求得的次优解对蚁群算法进行初始信息素分布,在次优解中进一步寻优,最终搜索到产品造型设计的最优解。遗传算法和蚁群算法的有效结合,使算法具有较好的全局收敛效率和求解精度。实验结果表明,该算法搜索出来的造型包含造型的细节特征,更加满足用户的个性化需要。

智能化个人形象设计CAD系统

尝试在非专业人士的形象设计领域,采用结合专业知识编码的遗传算法来建立一个CAD系统,该算法通过使用人工神经网络逼近适应度函数的方法进行改进,取得了很好的效果。建立这样的形象设计CAD系统对智能设计领域的扩展具有深远意义。

利用遗传算法的减速器优化设计CAD软件开发

介绍了分级模块式减速器CAD软件的开发指导思想,分析了软件系统优化方案的实现;恰当地选择了遗传算法来处理连续、整型和离散变量的优化问题,并讨论了遗传算法实施过程中需要解决的几个关键问题。

MODIFIED GENETIC ALGORITHM APPLIED TO SOLVE PRODUCT FAMILY OPTIMIZATION PROBLEM

The product family design problem solved by evolutionary algorithms is discussed. A successful product family design method should achieve an optimal tradeoff among a set of competing objectives, which involves maximizing commonality across the family of products and optimizing the performances of each product in the family. A 2-level chromosome structured genetic algorithm （2LCGA） is proposed to solve this class of problems and its performance is analyzed in comparing its results with those obtained with other methods. By interpreting the chromosome as a 2-level linear structure, the variable commonality genetic algorithm （GA） is constructed to vary the amount of platform commonality and automatically searches across varying levels of commonality for the platform while trying to resolve the tradeoff between commonality and individual product performance within the product family during optimization process. By incorporating a commonality assessing index to the problem formulation, the 2LCGA optimize the product platform and its corresponding family of products in a single stage, which can yield improvements in the overall performance of the product family compared with two-stage approaches （the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage）. The scope of the algorithm is also expanded by introducing a classification mechanism to allow mul- tiple platforms to be considered during product family optimization, offering opportunities for superior overall design by more efficacious tradeoffs between commonality and performance. The effectiveness of 2LCGA is demonstrated through the design of a family of universal electric motors and comparison against previous results.

Multi-Objective Genetic Algorithm to Design Manufacturing Process Line Including Feasible and Infeasible Solutions in Neighborhood

This paper treats multi-objective problem for manufacturing process design. A purpose of the process design is to decide combinations of work elements assigned to different work centers. Multiple work elements are ordinarily assigned to each center. Here, infeasible solutions are easily generated by precedence relationship of work elements in process design. The number of infeasible solutions generated is ordinarily larger than that of feasible solutions generated in the process. Therefore, feasible and infeasible solutions are located in any neighborhood in solution space. It is difficult to seek high quality Pareto solutions in this problem by using conventional multi-objective evolutional algorithms. We consider that the problem includes difficulty to seek high quality solutions by the following characteristics： （1） Since infeasible solutions are resemble to good feasible solutions, many infeasible solutions which have good values of objective functions are easily sought in the search process, （2） Infeasible solutions are useful to select new variable conditions generating good feasible solutions in search process. In this study, a multi-objective genetic algorithm including local search is proposed using these characteristics. Maximum value of average operation times and maximum value of dispersion of operation time in all work centers are used as objective functions to promote productivity. The optimal weighted coefficient is introduced to control the ratio of feasible solutions to all solutions selected in crossover and selection process in the algorithm. This paper shows the effectiveness of the proposed algorithm on simple model.

Implementation of Hybrid Genetic Algorithm for CLSC Network Design Problem—A Case Study on Fashion Leather Goods Industry

The implementation of closed loop supply chain system is becoming essential for fashion leather products industry to ensure an economically sustainable business model and eco-friendly industrial practice as demanded by the environmental regulations, consumer awareness and the prevailing social consciousness. In this context, this research work addresses a closed loop supply chain network problem of fashion leather goods industry, with an objective of minimizing the total cost of the entire supply chain and also reducing the total waste from the end of life product returns. The research work commenced with a literature review on the reverse and closed loop supply chain network design problems of fashion and leather goods industry dealt in the past. Then, the identified CLSCND problem is solved using a mathematical model based on Mixed Integer Non-Linear Programme (MINLP) and then a suitable Hybrid Genetic Algorithm (HGA) developed for the CLSCND is implemented for obtaining optimum solution. Both the MINLP model and HGA are customized as per the CLSCND problem chosen and implemented for the industrial case of an Indian Fashion Leather Goods Industry. Finally, the solutions obtained for MINLP model in LINGO 15 and for HGA in VB.NET platform are compared and presented. The optimum solution obtained from the suitable HGA is illustrated as an optimum shipment pattern for the closed loop supply chain network design problem of the fashion leather goods industry case.

Construction Management Utilizing 4D CAD and Operations Simulation Methodologies

The paper presents applications of simplified discrete-event simulation (SDESA), and 4D-GCPSU, to the National Stadium of the Beijing 2008 Olympics. Taking into account influential factors, e.g., resource, spatial condition, and the randomness of the construction process, the installation process of the steel-structure was simulated and optimized by using genetic algorithm (GA) optimization methodology. The operations simulation shortened the installation duration by 39 days (about 16% of the original total duration), guided the manufacturers to plan the construction processes, and provided specific suggestions on the entry time of the installation components, resulting in resource allocation optimization, resource saving, and construction efficiency improvement. Combining with the optimized schedule, the 4D visualization environment can discover time-space conflicts timely, and may assist project managers to reschedule the construction activities in tune with the site layout and resource allocation.

众包制造环境下协同产品族设计与延迟决策的主从关联优化

针对众包制造环境下产品族设计与延迟决策之间的协同优化问题,建立了以产品族设计为主、延迟决策为从的混合整数非线性双层规划模型。模型上层由制造商设计产品族架构以最大化自身的期望利润,下层由多个分销商优化延迟产品模块的类型以最大化各自的期望利润,且各分销商之间的决策具有相互独立性。考虑到延迟产品模块事先不确定,提出虚拟延迟结构将该优化问题具体化,并证明需要被延迟的产品模块。开发了一个双层嵌套遗传算法对模型进行求解。用智能冰箱产品族延迟案例验证了所提模型和算法的有效性,并对所提延迟偏好参数进行了灵敏度分析。

基于主从关联优化的产品族参数规划方法研究

针对产品族优化设计中存在的共性与个性的相互依存、制约关系,研究面向产品族的主从关联的参数优化规划技术。建立基于灵敏度分析的产品族参数识别划分方法,根据产品族设计参数的主从关联特点,建立优化问题的双层规划模型。提出一种采用多项式响应面拟合和遗传算法相结合的混合算法实现双层规划,下层采用多项式响应面法将双层优化目标拟合成为单层优化目标,在上层实现产品族公共参数的优化,再通过上层可适应参数的松弛约束关系传递,在下层利用遗传算法实现变型产品的参数优化。以起重机卷筒组件产品族规划为例,基于所提出的主从关联优化方法,得到个性变型产品参数最优值,为起重机卷筒组件产品及类似产品的产品族优化设计提供一种方法借鉴。

产品复杂曲面人机工程学设计研究

对产品形态设计中复杂曲面的人机工程学性能的设计问题进行了分析,研究了产品曲面人机性能设计的解决方案,应用遗传算法工具实现了对复杂曲面形态解方案的随机搜索以及通过程序进行自动评价与选择的机制,建立了基于Unigraphics造型平台的曲面人机设计过程机制与原型系统。

支持外观造型创新设计的进化计算方法

采用二叉树结构表示数学函数,并对树结构表示的数学函数实施交叉、变异、算术运算、选择等操作,产生新的后代.数学函数对应的曲线可以形成二维草图,经计算机处理后的三维图,能产生造型各异的组件.把生成的组件和人工设计的组件统一保存到原型实例库中,对组件进行二进制编码,然后执行简单的遗传算法,能生成不同的组合方案.由于利用了CAD的可视化环境,并结合人类设计师的智慧及进化计算技术,因此可以产生很多新颖的外观造型.最后以台灯设计为例,介绍了文中算法的执行过程.

基于设计基因和人机工程的单轨吊造型设计

本文从工业设计思维和人机工程学等方法融入单轨吊司机室外观造型的设计上,综合运用了设计心理学、人机工程学以及色彩学等方面的应用原则。以单轨吊司机室外观为研究对象,得出了一套具有现代元素的外观造型设计方案,为单轨吊司机室的外观设计提供了理论指导和参考。

机械产品方案设计自动化研究

研究并提出了一种基于遗传算法的机械方案计算机辅助设计系统模型 ,该模型利用面向对象的方法将各个机构进行封装而形成机构类。通过把机械方案设计过程看作是一个状态空间的求解问题 ,用遗传算法控制其搜索过程 ,利用复制、交换和变异等操作进行一次次迭代 ,最终自动生成一组最优的设计方案。该系统经过了大量的实例验证 ,具有很高的有效性、准确性和实用性。

电力变压器优化设计系统构建

基于综合设计、类比设计、优化设计三种设计方式构建了电力变压器优化设计系统。分析了三种设计方式的特点和相互间的联系,并以工程数据库系统为支撑,实现了各设计模块之间的数据传递。将专家系统混合推理机制融合到综合设计模块中,并采用改进遗传算法实现了优化设计模块。三种设计方式配合使用,可以灵活、可靠地获得优化设计方案。并基于本文构建的软件系统对一台110kV三绕组电力变压器进行了类比设计、综合设计和优化设计,结果表明了该软件系统的有效性。