蜂窝式阵列菲涅尔透镜的配光设计

单元结构、阵列排布和偏转控制是阵列菲涅尔透镜的三个重要因素。单元结构采用了棱镜加菲涅尔透镜结构,排布方式引入蜂窝式阵列,构成蜂窝式阵列菲涅尔透镜。该透镜可以将准直光源发出的光线投射到1m远、直径为30mm的圆形区域内并形成特定图形。通过调整菲涅尔透镜的各个独立环带的倾角,以及每个菲涅尔透镜的背侧光面倾角的优化,实现了一款蜂窝式阵列菲涅尔透镜的光学设计。通过光学仿真分析,结果表明该设计方法灵活可靠,光线控制效果良好,具有实现图像级配光的能力。     

大直径菲涅尔透镜模具加工发展现状及展望

大直径菲涅尔透镜广泛应用于太阳能电站、背投显示等领域,但其模具加工制造难度大。针对大直径菲涅尔透镜模具加工设备及加工工艺的发展状况进行了深入的分析研究,总结了大直径菲涅尔透镜模具加工的研究现状,展望了其未来的发展方向。     

大直径菲涅尔透镜模具加工发展现状及展望

针对大直径菲涅尔透镜模具加工设备及加工工艺的发展状况进行了深入的分析研究,总结了大直径菲涅尔透镜模具加工的研究现状,展望了其未来的发展方向。     

菲涅耳透镜的通光分析及设计方法探讨

研究了菲涅耳透镜成像质量差的原因,提出一种改进的方法,即改善轴外点的成像质量以增大菲涅耳透镜的视场。分析了三种常用的设计菲涅耳透镜的方法,用光学设计软件Zem ax模拟设计结果,对三种设计方法进行比较。得出结论:像面为曲面时可校正场曲;基面和底面为曲面的菲涅耳透镜与平面型菲涅尔透镜相比彗差较小。     

沟槽变异对菲涅耳透镜光学效率的影响

菲涅耳透镜以其良好的成像功能和很高的光学效率,广泛应用于教育投影仪、背投电视等大型成像设备。然而,出射面环形沟槽轨迹的不连续性给菲涅耳透镜的加工带来了诸多困难。为此,本文提出用连续阿基米德螺旋沟槽代替传统的同心环形沟槽,并从光学效率方面对它们进行了比较,计算结果验证了用螺旋沟槽代替同心环带沟槽的可行性,为螺旋沟槽型菲涅耳透镜的设计和制造提供了理论依据。     

基于菲涅尔透镜的室内LED射灯配光设计

为了将LED发出的光均匀投射到3m远直径为1m的圆形区域内,设计了一种LED(发光二极管)射灯。采用菲涅尔透镜对射灯进行二次光学配光设计,并通过调整菲涅尔透镜的各个独立环带的倾角来优化投射光的分布。文中对菲涅尔透镜的投光效果、安全性和加工误差等方面进行了考察,结果表明设计是安全的、可靠的并具有良好的投光效果。     

大视场菲涅尔透镜的聚光效率模拟和分析

大视场菲涅尔聚焦透镜在180°的范围内各个视场下均能实现较理想的聚焦。根据现有的大视场菲涅尔聚焦透镜的设计结果,利用Zemax软件在非序列环境下自定义面型的功能对该设计进行模拟分析。模拟菲涅尔透镜在不同方向的平行光照射下的聚焦情况。通过对不同方向的光线照射下探测器接收到能量的情况进行分析,得到了相应的聚光效率,为设计的可行性提供了分析依据。     

大直径菲涅尔透镜模具加工工艺的模拟实验研究

通过大量实验,分析总结了主轴转速、进给量和切削速度对大直径菲涅尔透镜模具加工过程动态平稳性的影响规律;当切削速度Vc≥120 m/min,进给量f=1～1.5 μm/r时,形成带状切屑,容易断屑,工件一刀具相对振动幅值小,切削过程连续平稳,加工表面质量高.实验结果将为大直径菲涅尔透镜模具的实际生产提供重要依据.     

注射成型微流控芯片微沟槽成型质量的无损检测

针对注射成型的微流控芯片具有微沟槽深度成型质量好,宽度成型质量较差,而且各处微沟槽的宽度成型质量不均衡的特点,利用Matlab软件的图像处理工具箱开发了微流控芯片微沟槽显微平面图片的图像处理系统,实现了利用常用的光学显微镜对微沟槽的成型质量进行无损检测。引入人工干涉来进行高效图像去噪处理。根据提取的微结构轮廓点进行了微沟槽轮廓的曲线拟合,测量了微沟槽的开口宽度和底部宽度。对由微流控芯片微沟槽显微平面图片所得到的测量结果与由对微流控芯片进行切片检测所得到的测量结果进行比较,结果显示,两种方法得到的微沟槽开口宽度相差约4%,槽底部宽度相差约3%,说明微沟槽显微平面图片的测量结果能够满足注射成型工艺研究中微流控芯片微结构成型质量检测的要求。     

微热管轴向微沟槽高速充液旋压成形实验研究

对铜微热管内壁轴向微沟槽高速充液旋压成形工艺进行实验研究,研究关键工艺参数包括拉管速度、旋压钢球数量、多齿芯头位置,分析其对微沟槽成形的影响,优化加工工艺参数.通过观察微沟槽横截面显微照片,分析微沟槽旋压成形过程中的塑性变形,初步探讨轴向微沟槽高速充液旋压成形机理.实验结果表明,拉管速度应在48 cm/min～64cm...     

智能注射成形中工艺参数的多目标自学习优化

注射成形工艺参数是保障产品质量的关键因素。传统试错法严重依赖工艺人员的试模经验,随着注射成形工艺广泛应用于电子、航空航天等国家战略领域,产品的高端化对工艺参数智能化设置水平提出更高的要求。由于成形产品存在多方面的质量要求,且不同质量指标间可能相互制约,因此亟需一种工艺参数多目标智能优化方法,以获得不同优化目标间的帕累托最优。已有学者利用智能优化方法,如非支配排序遗传算法等,对多目标优化问题进行求解,但是此类方法需大量样本数据对质量-参数关系进行建模,存在试验次数多、且对不同材料及模具的适应性较差等问题。为解决上述问题,提出一种注射成形工艺参数多目标自学习优化方法,在优化过程中实时计算并更新各个工艺参数的梯度,并由不同质量指标的多梯度下降算法对多个目标函数进行优化,在优化过程中实现各工艺参数对产品质量影响程度的自主学习,省去了采集大量数据来建立多个质量模型的过程,实现了注射成形工艺参数的高效智能优化。在基准测试函数实验中,所提方法的优化结果与理论解的相对误差小于2%。同时数值仿真与注射成形实验结果表明,所提方法能高效获得多个优化目标的帕累托最优。     

超声振动透镜辅助激光打孔实验研究

为研究超声振动透镜辅助对激光打孔加工质量和效率的影响，采用YLR-1000连续激光器和所设计的超声振动透镜辅助装置，以304不锈钢薄片为研究对象，分别在不同离焦量、激光功率、打孔时间、超声振幅情况下进行有无超声振动辅助激光打孔对比实验。实验采用VHX-1000E超景深显微系统对工件进行检测，并以孔径、热影响区半径、孔锥度、孔深和堆砌高度为主要指标分析实验结果。结果表明：超声振动透镜辅助可从总体上提高激光打孔的加工质量和效率。     

Multi-objective optimization of MIMO plastic injection molding process conditions based on particle swarm optimization

Determining optimal process parameter settings critically influences productivity, quality, and cost of production in the plastic injection molding industry. Selecting the proper process conditions for the injection molding process is treated as a multi-objective optimization problem, where different objectives, such as minimizing product weight, volumetric shrinkage, or flash present trade-off behaviors. As such, various optima may exist in the objective space. This paper presents the development of an experiment-based optimization system for the process parameter optimization of multiple-input multiple-output plastic injection molding process. The development integrates Taguchi’s parameter design method, neural networks based on PSO (PSONN model), multi-objective particle swarm optimization algorithm, engineering optimization concepts, and automatically search for the Pareto-optimal solutions for different objectives. According to the illustrative applications, the research results indicate that the proposed approach can effectively help engineers identify optimal process conditions and achieve competitive advantages of product quality and costs.     

红外光学镜头的调制传递函数测量

调制传递函数是评价红外光学镜头性能的最基本的技术指标之一。对刀口边缘扫描法测量红外光学镜头的调制传递函数(MTF)进行了研究,并对f=50mm,F=1.2波长范围为8~12μm的红外光学镜头的调制传递函数(MTF)进行了测量。得到了在几个特征频率下的调制传递函数(MTF)值,并给出了调制传递函数(MTF)的曲线图。     

基于BP-NSGA的注塑参数多目标智能优化设计

为获得成型性能最优的注塑参数设计方案,提出了基于BP神经网络和非支配排序遗传算法的注塑参数多目标优化方法。将注塑模结构尺寸参数和注塑工艺参数作为待优化的设计变量,建立了以高质量、低成本、高效率为优化目标的注塑参数优化设计模型。基于非支配排序遗传算法获取给定参数范围内的所有Pareto最优解,并通过建立多输入和多输出的BP神经网络来快速获得非支配排序遗传算法优化进程中所有个体的适应度值。开发了基于BP神经网络与非支配排序遗传算法集成的注塑参数智能优化设计系统,并通过鼠标注塑参数设计实例,验证了其适用性和有效性。     

Multi-objective optimization of injection molding process parameters in two stages for multiple quality characteristics and energy efficiency using Taguchi method and NSGA-II

In this paper, the parameters optimization of plastic injection molding (PIM) process was obtained in systematic optimization methodologies by two stages. In the first stage, the parameters, such as melt temperature, injection velocity, packing pressure, packing time, and cooling time, were selected by simulation method in widely range. The simulation experiment was performed under Taguchi method, and the quality characteristics (product length and warpage) of PIM process were obtained by the computer aided engineering (CAE) method. Then, the Taguchi method was utilized for the simulation experiments and data analysis, followed by the S/N ratio method and ANOVA, which were used to identify the most significant process parameters for the initial optimal combinations. Therefore, the range of these parameters can be narrowed for the second stage by this analysis. The Taguchi orthogonal array table was also arranged in the second stage. And, the Taguchi method was utilized for the experiments and data analysis. The experimental data formed the basis for the RSM analysis via the multi regression models and combined with NSGS-II to determine the optimal process parameter combinations in compliance with multi-objective product quality characteristics and energy efficiency. The confirmation results show that the proposed model not only enhances the stability in the injection molding process, including the quality in product length deviation, but also reduces the product weight and energy consuming in the PIM process. It is an emerging trend that the multi-objective optimization of product length deviation and warpage, product weight, and energy efficiency should be emphasized for green manufacturing.     

Constrained grinding optimization for time,cost, and surface roughness using NSGA-II

Selection of parameters in machining process significantly affects quality, productivity, and cost of a component. This paper presents an optimization procedure to determine the optimal values of wheel speed, workpiece speed, and depth of cut in a grinding process considering certain grinding conditions. Experimental studies have been carried out to obtain optimum conditions. Mathematical models have also been developed for estimating the surface roughness based on experimental investigations. A non-dominated sorting genetic algorithm (NSGA II) is then used to solve this multi-objective optimization problem. The objectives under investigation in this study are surface finish, total grinding time, and production cost subjected to the constraints of production rate and wheel wear parameters. The Pareto-optimal fronts provide a wide range of trade-off operating conditions which an appropriate operating point can be selected by a decision maker. The results show the proposed algorithm demonstrates applicability of machining optimization considering conflicting objectives.     

Multi-objective optimization of green sand mould system using evolutionary algorithms

The quality of cast products in green sand moulds is largely influenced by the mould properties, such as green compression strength, permeability, hardness and others, which depend on the input (process) parameters (that is, grain fineness number, percentage of clay, percentage of water and number of strokes). This paper presents multi-objective optimization of green sand mould system using evolutionary algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO). In this study, non-linear regression equations developed between the control factors (process parameters) and responses like green compression strength, permeability, hardness and bulk density have been considered for optimization utilizing GA and PSO. As the green sand mould system contains four objectives, an attempt is being made to form a single objective, after considering all the four individual objectives, to obtain a compromise solution, which satisfies all the four objectives. The results of this study show a good agreement with the experimental results.     

Optimization of cutting parameters for energy saving

Correct selection of cutting parameters is one of effective approaches to achieve optimum machining process, including reducing energy consumption. For the close relationship between cutting parameters and energy consumption in machining process, energy consumed is modeled and to be reduced based on analyzing the energy consumption in this paper. According to the different requirements in roughing process and finishing process, corresponding multi-objective optimization functions are formulated considering energy consumption. Taking the optimization of milling operations on aluminum alloy as an example, experiments are carried out to analyze the energy consumption and production rate with sets of optimized/un-optimized cutting parameters for different objectives. The experimental results show that the objectives of low consumed energy and high production rate can be simultaneously achieved by optimization of cutting parameters.     

Multi-objective optimization of process parameters for the helical gear precision forging by using Taguchi method

Precision forging of the helical gear is a complex metal forming process under coupled effects with multi-factors. The various process parameters such as deformation temperature, punch velocity and friction conditions affect the forming process differently, thus the optimization design of process parameters is necessary to obtain a good product. In this paper, an optimization method for the helical gear precision forging is proposed based on the finite element method (FEM) and Taguchi method with multi-objective design. The maximum forging force and the die-fill quality are considered as the optimal objectives. The optimal parameters combination is obtained through S/N analysis and the analysis of variance (ANOVA). It is shown that, for helical gears precision forging, the most significant parameters affecting the maximum forging force and the die-fill quality are deformation temperature and friction coefficient. The verified experimental result agrees with the predictive value well, which demonstrates the effectiveness of the proposed optimization method.