基于应力分布的3D打印壳模型构造与优化

近年来,愈发成熟的3D打印技术拉近了模型设计与产品制造的距离.但高昂的材料费用仍是制约其发展的重要因素.因此,如何在不改变模型外观的情况下进行模型结构的优化,以此来减少模型的打印体积、降低打印成本是亟待解决的问题.针对该问题,本文提出一种基于应力分布的壳模型构造和优化算法.该算法首先基于模型的体素化表示构造距离场,提取出初始的均匀厚度壳模型.然后基于顶点的von Mises应力,自适应的向外扩张内表面,调整各部位厚度,直到达到相关约束条件.优化得到的内表面与输入的外表面围成最终的优化模型.实验结果表明,在满足外观不变、力学稳定等约束的同时,优化得到的壳模型体积为输入模型体积的17.2%~24.4%,大幅减少了模型的打印体积,有效降低了打印成本.     

3D打印模型节材优化

针对3D打印材料费用昂贵问题,提出一种改进的蒙皮-桁架结构快速构建与优化方案,使打印模型的体积在满足产品物理机械性能、受力平衡性、自平衡性和可打印性等多约束条件下实现最小化,达到节省打印耗材、降低成本的目的.利用网格简化策略快速构建表层桁架结构,与原始网格模型在拓扑上保持相似性,避免桁架与蒙皮之间的冲突;基于实验分析,提出无内节点的内部桁架构建方法,可大幅缩短整体优化时间,且可在保证满足力学约束条件下获得更优的节材效果;将力学准则法与粒子群优化算法有机结合,利用满应力法计算桁架结构的支杆半径作为优化算法的初始值,提高算法在全局范围内搜索最优解的能力和效率,实现桁架结构支杆截面尺寸与系统拓扑结构协同优化的目标.实验结果表明,该方法健壮、有效,具有成本优势和效率优势.     

基于深度剥离的三维打印模型朝向优化算法

为了提高三维打印产品表面质量且有效节省材料,提出一种面向熔融挤压式三维打印机的模型朝向优化算法.该算法基于深度剥离技术,以打印朝向为自变量构建模型悬空面积及其支撑结构体积的双目标优化函数;利用模式搜索方法设计一个两步走的优化方案来确定最优的打印朝向.实验结果表明,利用深度剥离技术能够较为准确、高效地检测出打印模型悬空区域,并计算出相应的悬空面积和支撑体积;设计的优化方案也能够根据双目标的权重比确定一个较优的打印朝向,不但减少了支撑结构与模型表面的接触范围,还能降低支撑结构的体积.文中算法在提高模型表面质量以及减少支撑结构的材料消耗两方面具有优势.     

基于反应扩散方程的水平集结构拓扑优化方法与实现

为实现更加先进的拓扑优化算法,研究采用反应扩散方程的水平集结构拓扑优化方法,通过理论推导给出算法中的参数选择建议.该方法允许在拓扑优化过程中生成新的孔洞,初始结构无须包含孔洞,不需要重新初始化步骤,从而可提高算法的收敛性.针对传统拓扑优化中主要采用体积约束、以柔度最小为目标和体积保留率设定存在一定主观性的问题,探究不同体积保留率下的结构应力水平的变化规律,结果显示可以依据结构最大应力水平与体积保留率的变化规律确定最优体积保留率.     

基于改进粒子群算法的多机器多任务3D打印智能调度方法

针对批量3D打印成本高,多机器多任务的3D打印批次调度复杂的问题,建立以最小单位体积平均成本为目标的优化模型,并提出一种基于改进粒子群算法的智能调度方法求解该模型;首先,分析打印工场、生产流程,构建3D打印单位体积平均成本模型;之后基于改进粒子群算法,以单位体积平均成本为适应度,以调度序列为粒子的位置信息,采用十进制顺序二维编码方式表示问题的解,并在更新策略上应用线性递减权值的动态惯性因子来调整全局与局部的搜索能力;算法迭代后,得到目标函数最优值及对应解集;经实验算例结果表明,该方法较单独打印加工的单位体积平均成本降低了0.101 3GBP/cm3,有效地降低工厂生产的总成本,提高了3D打印机的利用效率。     

带有区域性惩罚因子的双向渐进结构优化方法

针对传统的拓扑优化方法欠缺考虑增材制造工艺约束,如果优化模型存在大面积悬垂、封闭空腔等工艺几何特征将增加打印及后处理操作的复杂度的问题,提出带有区域性惩罚因子的双向渐进拓扑优化方法.首先考虑增材制造工艺特征约束,使得优化结果具有良好的可打印性;然后对模型进行网格划分,根据单元体所在的区域定义区域性惩罚因子,保证区域性惩罚因子由外表面向内核逐渐增大;最后结合有限元分析,以区域性惩罚因子对单元体的计算应力进行惩罚,决策单元体的保留与删减.通过数值实验,与传统的双向渐进结构拓扑优化算法对比的结果表明,该方法可以改变空腔形貌特征、减少垂悬面积、均衡模型应力分布,提升了拓扑优化模型的可打印效果.     

基于自适应分层的3D打印成型方向优化

在3D打印中,成型方向的选择是影响产品成型质量的关键因素之一。针对目前算法仅考虑在等厚分层的前提下进行优化的问题,提出基于自适应分层的模型成型方向优化算法。首先结合3D打印层层叠加的特点,在分析不同成型方向对产品表面精度影响的基础上建立了以模型成型方向为变量、最小体积误差为目标的优化函数;然后通过STL模型的坐标变换、自适应分层等步骤并利用遗传算法在全局范围内搜索最优解,得到最佳成型方向。实验结果表明,该算法能够找到在自适应分层前提下的最佳成型方向,与现有算法相比能够进一步降低体积误差,提升表面质量。     

基于改进蚁群算法的网格组合拍卖资源分配

网格计算资源分配是一类组合优化问题,即如何将网格计算资源有效地分配到用户任务.针对当前网格环境中资源繁多的特点.为了更好地提高网格计算资源的利用率和资源分配的时间效率,提出了一种新的方法,采用一种通过改进转移概率准则和信息素的蚁群算法来解决网格组合拍卖模型中计算资源的优化分配问题.仿真结果表明,该算法能够保证在一个拍卖周期内满足服务的最大用户数和最短的平均等待时间,证明了改进蚁群算法在网格计算资源合理分配上的有效性.     

面向3D打印的结构优化研究进展

3D打印有效地填补了数字化模型设计与真实产品制造之间的鸿沟,将产品设计与制造两阶段更紧密地关联在一起.与传统制造技术相比,它有效地降低了制造约束条件与制造技能要求,因此它有力地推动了个性化产品的设计制造与相关研究.在此背景下,文中面向3D打印,围绕近年来3D模型设计的结构分析与优化大量相关工作,从节省材料、增加强度、提高稳定性和支撑优化4个方面分别介绍了这些工作的研究目标、算法思想及研究结果,最后对这4方面工作进行了总结,并结合当前的技术发展水平,从研究与应用的角度探讨了一些具有挑战性的未来工作.     

基于体素模型的3D打印支撑算法

3D 打印技术是通过对材料的逐层堆积来构建物体,但对模型悬空的区域需要添加 支撑结构。支撑结构不仅会造成打印材料的浪费,而且会延长打印时间并对模型外表有所损伤。 为此,提出一种基于体素模型的支撑算法,针对体素化后的模型,分析体素之间的相互支撑作用, 并引入体素支撑能量函数概念和计算方法,计算出需要添加支撑的体素,从而得到需要添加支撑 结构的区域,并由该区域生成支撑结构,之后通过实验对算法进行验证。实验结果显示该算法能 够更加准确地对模型生成支撑,同时,基于体素模型的支撑算法对于模型内部支撑计算,也具有 很好的适用性。     

Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization

Global derivative-free deterministic algorithms are particularly suitable for simulation-based optimization, where often the existence of multiple local optima cannot be excluded a priori, the derivatives of the objective functions are not available, and the evaluation of the objectives is computationally expensive, thus a statistical analysis of the optimization outcomes is not practicable. Among these algorithms, particle swarm optimization (PSO) is advantageous for the ease of implementation and the capability of providing good approximate solutions to the optimization problem at a reasonable computational cost. PSO has been introduced for single-objective problems and several extension to multi-objective optimization are available in the literature. The objective of the present work is the systematic assessment and selection of the most promising formulation and setup parameters of multi-objective deterministic particle swarm optimization (MODPSO) for simulation-based problems. A comparative study of six formulations (varying the definition of cognitive and social attractors) and three setting parameters (number of particles, initialization method, and coefficient set) is performed using 66 analytical test problems. The number of objective functions range from two to three and the number of variables from two to eight, as often encountered in simulation-based engineering problems. The desired Pareto fronts are convex, concave, continuous, and discontinuous. A full-factorial combination of formulations and parameters is investigated, leading to more than 60,000 optimization runs, and assessed by three performance metrics. The most promising MODPSO formulation/parameter is identified and applied to the hull-form optimization of a high-speed catamaran in realistic ocean conditions. Its performance is finally compared with four stochastic algorithms, namely three versions of multi-objective PSO and the genetic algorithm NSGA-II.     

Real-time dynamic optimization of batch systems

In this paper a methodology for designing and implementing a real-time optimizing controller for batch processes is proposed. The controller is used to optimize a user-defined cost function subject to a parameterization of the input trajectories, a nominal model of the process and general state and input constraints. An interior point method with penalty function is used to incorporate constraints into a modified cost functional, and a Lyapunov based extremum seeking approach is used to compute the trajectory parameters. The technique is applicable to general nonlinear systems. A precise statement of the numerical implementation of the optimization routine is provided. It is shown how one can take into account the effect of sampling and discretization of the parameter update law in practical situations. A simulation example demonstrates the applicability of the technique.     

Multiobjective optimization of trusses using genetic algorithms

In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of min–max optimum, a new GA-based multiobjective optimization technique is proposed and two truss design problems are solved using it. The results produced by this new approach are compared to those produced by other mathematical programming techniques and GA-based approaches, proving that this technique generates better trade-offs and that the genetic algorithm can be used as a reliable numerical optimization tool.     

An integrated approach to topology,sizing, and shape optimization

Topology optimization has become very popular in industrial applications, and most FEM codes have implemented certain capabilities of topology optimization. However, most codes do not allow simultaneous treatment of sizing and shape optimization during the topology optimization phase. This poses a limitation on the design space and therefore prevents finding possible better designs since the interaction of sizing and shape variables with topology modification is excluded. In this paper, an integrated approach is developed to provide the user with the freedom of combining sizing, shape, and topology optimization in a single process.     

Effects of dominant wolves in grey wolf optimization algorithm

Bio-inspired computation is one of the emerging soft computing techniques of the past decade. Although they do not guarantee optimality, the underlying reasons that make such algorithms become popular are indeed simplicity in implementation and being open to various improvements. Grey Wolf Optimizer (GWO), which derives inspiration from the hierarchical order and hunting behaviours of grey wolves in nature, is one of the new generation bio-inspired metaheuristics. GWO is first introduced to solve global optimization and mechanical design problems. Next, it has been applied to a variety of problems. As reported in numerous publications, GWO is shown to be a promising algorithm, however, the effects of characteristic mechanisms of GWO on solution quality has not been sufficiently discussed in the related literature. Accordingly, the present study analyses the effects of dominant wolves, which clearly have crucial effects on search capability of GWO and introduces new extensions, which are based on the variations of dominant wolves. In the first extension, three dominant wolves in GWO are evaluated first. Thus, an implicit local search without an additional computational cost is conducted at the beginning of each iteration. Only after repositioning of wolf council of higher-ranks, the rest of the pack is allowed to reposition. Secondarily, dominant wolves are exposed to learning curves so that the hierarchy amongst the leading wolves is established throughout generations. In the final modification, the procedures of the previous extensions are adopted simultaneously. The performances of all developed algorithms are tested on both constrained and unconstrained optimization problems including combinatorial problems such as uncapacitated facility location problem and 0-1 knapsack problem, which have numerous possible real-life applications. The proposed modifications are compared to the standard GWO, some other metaheuristic algorithms taken from the literature and Particle Swarm Optimization, which can be considered as a fundamental algorithm commonly employed in comparative studies. Finally, proposed algorithms are implemented on real-life cases of which the data are taken from the related publications. Statistically verified results point out significant improvements achieved by proposed modifications. In this regard, the results of the present study demonstrate that the dominant wolves have crucial effects on the performance of GWO.     

多元插值全局优化算法及其应用

本文介绍一种多元插值逼近和动态搜索轨迹相结合的全局优化算法.该算法大大减少了目标函数计算次数,寻优收敛速度快,算法稳定,且可获得全局极小,有效地解决了大规模非线性复杂动态系统的参数优化问题.一个具有8个控制参数的电力系统优化控制问题,采用该算法仅访问目标函数78次,便可求得最优控制器参数。     

云搜索优化算法

本文将云的生成、动态运动、降雨和再生成等自然现象与智能优化算法的思想融合,建立了一种新的智能优化算法-云搜索优化算法(CSO)。生成与移动的云可以弥漫于整个搜索空间,这使得新算法具有较强的全局搜索能力;收缩与扩张的云团在形态上会有千奇百态的变化,这使得算法具有较强的局部搜索能力;降雨后产生新的云团可以保持云团的多样性,这也是使搜索避免陷入局优的有效手段。实验表明,基于这三点建立的新算法具有优异的性能,benchmark函数最优值的计算结果以及与已有智能优化算法的比较展现了新算法精确的、稳定的全局求解能力。     

Value Creation in a Network: The Role of Pricing and Revenue Optimization and Enterprise Profit OptimizationTM

The Internet has created a virtual upheaval in the structural features of the supply and demand chains for most businesses. New agents and marketplaces have surfaced. The potential to create value and enhance profitable opportunities has attracted both buyers and sellers to the Internet. Yet, the Internet has proven to be more complex than originally thought. With information comes complexity: the more the information in real time, the greater the difficulty in interpretation and absorption. How can the value-creating potential of the Internet still be realized, its complexity notwithstanding? This paper argues that with the emergence of innovative tools, the expectations of the Internet as a medium for enhanced profit opportunities can still be realized. Creating value on a continuing basis is central to sustaining profitable opportunities. This paper provides an overview of the value creation process in electronic networks, the emergence of the Internet as a viable business communication and collaboration medium, the proclamation by many that the future of the Internet resides in “embedded intelligence”, and the perspectives of pragmatists who point out the other facet of the Internet—its complexity. The paper then reviews some recent new tools that have emerged to address this complexity. In particular, the promise of Pricing and Revenue Optimization (PRO) and Enterprise Profit Optimization^TM (EPO) tools is discussed. The paper suggests that as buyers and sellers adopt EPO, the market will see the emergence of a truly intelligent network—a virtual network—of private and semi-public profitable communities.     

粒子群算法在组合优化问题上的研究与发展

粒子群优化算法是一种新兴的基于群智能搜索的优化技术。该算法简单、易实现、参数少,具有较强的全局优化能力,可有效应用于科学与工程实践中。介绍了算法的基本原理和算法在组合优化上一些改进方法的主要应用形式。最后,对粒子群算法作了一些深入分析并在此基础上对粒子群算法应用于组合优化问题做了一些总结。     

SEO技术研究

为了利用搜索引擎优化SEO（Search Engine Optimization）技术给网站带来高质量的流量并将其转化为商业利益,理解搜索引擎的算法和排名原理十分必要。通过对网站的结构优化、关键词优化、单页优化、防止被搜索引擎惩罚和挽救被惩罚网站等技术的研究,达到提高网站排名,实现网站的价值目的。