基于MATLAB app designer的最优化方法辅助教学设计

针对"最优化方法"的优化算法理论内容抽象的特点,使用MATLAB的app designer开发了最优化算法可视化平台,实现了对课程中传统算法、程序组合、智能算法、其他典例的可视化.平台的可视化界面具体直观、交互性强,有助于学生理解优化算法迭代过程,掌握优化算法迭代理论,从而激发学生学习兴趣,提高课堂教学效率.     

结合机械设计约束处理的差分进化算法

在机械设计当中,因为非线性问题广泛的存在于材料性能设计、零件几何结构设计、尺寸设计以及传动干涉等机械设计当中,需要进行机械设计的最优化设计方案的选择。在机械的优化设计当中,有很多的设计方法被广泛的应用,但无论是那种设计方法,对于约束处理的优化设计则是影响整个优化设计的成功与否的关键所在。本文将结合机械设计中运用差分进化算法来处理约束处理问题进行研究。     

SolidWorks自上而下设计方法初探

一、引言SolidWorks是一款优秀的机械设计软件,在国内外已经广泛应用。本文以一款离心机构的开发过程为例来讲述SolidWorks自上而下设计技术在新产品开发中的应用。SolidWorks的设计方法有两种,自下而上设计和自上而下设计。     

优化后的网格时间最优化算法

Nimrod-G是在计算经济网格体系结构（GRACE）下开发的网格资源管理系统,它提供了一套基于期限和预算的调度策略DBC（Deadline and Budget Constrained）,以优化任务调度过程中的时间和费用问题。其中,时间最优化算法是DBC策略中最主要的算法之一,该算法的主要目标是对任务的处理时间进行优化。但该算法在预算分配和预算使用方面存在着不足,以致在任务预算较少时出现任务完成率低的现象。该文针对该算法的不足提出了一个改进方法,有效地改进了传统的时间最优化算法。     

“人工智能及其工程应用”讲座：第十一讲 人工智能在优化中的应用

一、引言在优化过程中,人们常常会遇到如下问题:①有些优化问题难以用精确的数学模型描述;②对于复杂的优化问题的求解,往往受到计算机的存贮容量、计算时间以及资金等经济因素的制约;③有些优化问题要求在很短的时间内得到近似最优解;④传统的最优化方法未必能够有效地提供符合精度要求的解;⑤有些最优化方法在解决某些问题时比较呆板,诸如多峰值寻优、克服死循环等。为了解决上述问题,人们探讨采用人工智能、启发式技术等来改进目前的最优化方法。 50年代,人们在最优化方法中设入了启发式技术,以解决运筹学中的组合优化问题。70年代,Simon提出了"满意原则",指出许多实际问题往往只需要满意解,而不是最优解。进入80年     

探讨计算机辅助技术与机械设计制造的结合

随着技术的不断更迭与进步,计算机辅助设计的技术在当前机械设计制造行业中得到了广泛的而应用,它对于提高机械制造的工作效率有较大的帮助,并优化了机械设计的质量水平.因此,本文将会对计算机辅助技术和机械设计制造进行结合,探讨两者联合的重要性,并分析计算机辅助技术在机械设计制造过程中的作用,进而阐述当前的应用情况,希望能够为机械设计制造行业的发展提供新的方向.     

机械设计和评估专家系统的设计与实现

本文阐述了专家系统技术在机械设计和评估中的应用研究情况，主要内容包括如何针对机械设计的特点构造其专家系统的体系结构，如何利用人工智能领域中开发知识表示方法表达机械设计和知识和建立相应的知识库，以及设计推理机。     

AutoCAD三维技术在机械设计中的应用分析

近年来鉴于对机械设计工作过程中的AutoCAD三维技术实际应用的相关研究,发现AutoCAD在机械设计中有着不可忽视的作用,本文对该问题进行了相应研究。文章将从四个大的方面来说明该问题,首先是在机械设计中应用AutoCAD三维技术的好处,其次是AutoCAD三维技术在机械设计应用过程中所产生的影响,然后谈谈AutoCAD三维技术在机械设计应用过程中的缺陷,最后介绍该技术在机械设计应用过程中的发展前景。     

最优化技术在精馏过程综合中的应用及研究进展

数学最优化技术在过程系统综合中起着重要作用。本文综述了混合整数非线性规划(MINLP)和随机优化两类最优化方法在多组分精馏过程综合中的应用及取得的新成果。首先对离散流程结构的表达方法、MINLP模型的建立和求解方法、以及各种方法的优缺点等方面进行了比较分析；随后提出了一种新的可包括复杂精馏结构的编码表达法及一个求解混合变量问题的随机优化策略；最后对该领域发展提出展望。     

基于计算机技术的机械设计方法研究

随着社会的不断进步,计算机技术广泛的运用到了机械设计方面,计算机技术的应用使得机械设计在市场方面的竞争不断增加,而计算机技术的不断提高便使得机械设计不断优化,保证了机械制造业的竞争水平,机械制造业的设计核心是计算机技术,而计算机技术的提高需要技术人员的深入讨论与研究,这是一个艰难的过程,因此基于计算机技术的机械设计方法研究具有十分重要的意义.     

基于可靠性的机动雷达升降机构优化设计

可靠性设计与优化设计在机械设计领域产生巨大的作用,但单方面进行可靠性设计或优化设计,都无法发挥各自的全部潜力.针对这一问题,将二者进行有机结合,开展机械产品的可靠性优化设计成为必然.案例中,以某型机动雷达的升降机构为研究对象,基于Adams对其进行参数化建模,在此基础上,对其开展可靠性优化设计.通过与传统优化设计的对比分析,显示该方法的优越性.     

Robust optimization of constrained mechanical system with joint clearance and random parameters using multi-objective particle swarm optimization

Joint clearance and uncertainty are inevitable in mechanical systems due to design tolerance, abrasion, manufacture error, assembly error and imperfections. In this study, kinematic analysis and robust optimization of constrained mechanical systems with joint clearance and random parameters were performed. Joint clearance was modeled by Lankarani-Nikravesh contact force model, and probability space was applied for characterizing uncertain parameters. A kinematic analysis method based on Baumgarte approach and confidence region method was presented to predict kinematic error of the mechanical system. Slider-crank mechanism, an illustrative example was presented to show the influence of clearance and uncertainty on the kinematic accuracy. Then, a novel multi-objective robust optimization methodology was presented for kinematic accuracy robust optimization design of the constrained mechanical system. In this approach, a multi-objective robust optimization model derived from 95% confidence region is constructed to reduce the effects of clearance and parameter uncertainty on 95% confidence region of kinematic error. The robust optimization model is a double-loop process. A multi-objective robust optimization strategy, combing Kriging surrogate model, multi-objective particle swarm optimization, confidence region and Monte Carlo methods, was proposed to search the design variables for minimizing the optimization objectives derived from confidence region while balancing computational accuracy and efficiency of the optimization process. The optimal results of the slider-crank mechanism demonstrated the validity and feasibility of the proposed robust optimization method.     

Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems

A new efficient optimization method, called ‘Teaching–Learning-Based Optimization (TLBO)’, is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the ‘Teacher Phase’ and the second part consists of the ‘Learner Phase’. ‘Teacher Phase’ means learning from the teacher and ‘Learner Phase’ means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems.     

Implementing a structural continuity constraint and a halting method for the topology optimization of energy absorbers

This study investigates topology optimization of energy absorbing structures in which material damage is accounted for in the optimization process. The optimization objective is to design the lightest structures that are able to absorb the required mechanical energy. A structural continuity constraint check is introduced that is able to detect when no feasible load path remains in the finite element model, usually as a result of large scale fracture. This assures that designs do not fail when loaded under the conditions prescribed in the design requirements. This continuity constraint check is automated and requires no intervention from the analyst once the optimization process is initiated. Consequently, the optimization algorithm proceeds towards evolving an energy absorbing structure with the minimum structural mass that is not susceptible to global structural failure. A method is also introduced to determine when the optimization process should halt. The method identifies when the optimization method has plateaued and is no longer likely to provide improved designs if continued for further iterations. This provides the designer with a rational method to determine the necessary time to run the optimization and avoid wasting computational resources on unnecessary iterations. A case study is presented to demonstrate the use of this method.     

An optimal design of magnetic actuators using topology optimization and the response surface method

The magnetic actuator is a device that transforms electric energy to mechanical energy. By mechanical energy transformation, some part of the electric energy creates force, and the other part is stored within the ferrous material. An actuator with improved magnetic force can be designed by reducing the stored energy within the ferrous material at the core or the armature. Topology optimization based on the homogenization design method (HDM) is used for the initial design by determining the porous hole size of each element created. The homogenized magnetic permeability is applied in calculation of the magnetic energy stored. The magnetic energy is calculated by finite element analysis and the sensitivity is calculated mathematically by determining the effects of the magnetic energy according to the permeability change at each element. Repeating the process of the porous hole size determination by the sequential linear programming (SLP), eventually leads to a design of an actuator that makes the most improved magnetic force within the limited volume. The initial actuator model derived from topology optimization uses parameter optimization for detail designs. In parameter optimization design, the response surface method (RSM) based on the central composite design is used to obtain a clear final design.     

Structural optimization system based on trabecular bone surface adaptation

In the paper the structural optimization system based on trabecular bone surface adaptation is presented. The basis of the algorithm formulation was the phenomenon of bone adaptation to mechanical stimulation. This process, called remodeling, leads to the optimization of the trabecular network in the bone. The simulation system, as well as the finite element mesh generation, decision criteria for structural adaptation, and the finite element analysis in a parallel environment are described. The possibility of applying the system in mechanical design is discussed. Some computation results using the developed system are presented, including the comparison to the topology optimization method.     

Factors that affect planning in a knowledge-based system for mechanical engineering design optimization with application to the design of mechanical power transmissions

Intelligent computer-aided design (CAD) emulates the human activity of design so that production planning, decision making, and inventive design can be performed by computers.Based on the history of human experience in engineering design, a formalized and systematic approach to design should include procedures from (1) conceptual design, (2) layout design, and (3) numerical optimization. The highest level within such a system should be responsible for specifying and symbolically optimizing skeleton structures of generic (nonspecific) elements within the design process that are eventually to be specified uniquely (pinned down) and ultimately optimized numerically. Planning plays a key role in such a system.Planning has been utilized as a tool for process organization within the knowledge domains of chemical engineering, electrical engineering, manufacturing, as well as for general problem formulation and solution. State estimation, subtask scheduling, and constraint propagation have been found to be factors of prime importance in this type of problem.Problems associated with the implementation of a planning strategy within a knowledge-based system for mechanical engineering design optimization are discussed. A hypothesis for planning is put forth and examined within the context of a model of the mechanical design optimization process. An example that demonstrates the applicability of this approach to mechanical power transmission design is considered.     

A novel CACD/CAD/CAE integrated design framework for fiber-reinforced plastic parts

This work presents a novel CACD/CAD/CAE integrated framework for design, modeling, and optimization of fiber-reinforced plastic parts, which can greatly enhance the current design practice by realizing partial automation and multi-stage optimization. To support this framework, a new heterogeneous feature model (HFM) has been developed to model the fiber-reinforced objects and to be transferred between engineering modules. To be specific, the CACD (computer-aided conceptual design) module employs the level-set structure and material optimization to produce the initial design with thickness control, and also the initial HFM; the CAD (computer-aided design) module allows manual editing on the HFM to reflect various design intents; then, the injection molding CAE (computer-aided engineering) simulates the manufacturing process, and the response surface method (RSM) is applied to optimize the process parameters of gate location, injection flow rate, mold temperature and melt temperature, to approach the manufactured fiber orientation distribution close to the optimized result produced by the CACD module; besides, the structural analysis CAE module generates the mechanical performance result to support the CACD module, as well as to validate the final design. By applying this framework, the final structural design including the fiber orientation distribution, will perform better in mechanical properties, and consume less matrix and fiber materials; besides, the design maturity can be approached in shorter time. To prove the effectiveness, a plastic gripper design will be comprehensively studied.     

Design sensitivity and reliability-based structural optimization by univariate decomposition

This paper presents a new univariate decomposition method for design sensitivity analysis and reliability-based design optimization of mechanical systems subject to uncertain performance functions in constraints. The method involves a novel univariate approximation of a general multivariate function in the rotated Gaussian space for reliability analysis, analytical sensitivity of failure probability with respect to design variables, and standard gradient-based optimization algorithms. In both reliability and sensitivity analyses, the proposed effort has been reduced to performing multiple one-dimensional integrations. The evaluation of these one-dimensional integrations requires calculating only conditional responses at selected deterministic input determined by sample points and Gauss–Hermite integration points. Numerical results indicate that the proposed method provides accurate and computationally efficient estimates of the sensitivity of failure probability, which leads to accurate design optimization of uncertain mechanical systems.