共查询到20条相似文献,搜索用时 171 毫秒
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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。 相似文献
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注射成型微流控芯片微沟槽成型质量的无损检测 总被引:1,自引:1,他引:0
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。 相似文献
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针对聚合物熔体在微尺度通道中流动时的黏性耗散效应对其流动行为的影响,通过自行构建的带有温度传感器和超声振子的微注塑成型试验系统,采用单因素成型试验方法,对聚丙烯(Polypropylene, PP)和高密度聚乙烯(High-density polyethylene, HDPE)两种聚合物材料在不同工艺参数和超声外场作用下,流经矩形截面微通道时由黏性耗散效应引起的通道出口熔体温升进行试验测量。结果表明,微通道中熔体的黏性耗散效应随注射速度的增加而增强,随入口熔体温度和模具温度的升高而减弱;与不加超声振动相比,施加超声振动使两种材料的微通道出口熔体温升值明显升高;但材料自身的微观分子结构及其热物理性能不同,其温升增幅差别较大。试验注射速度下,施加超声振动比不加超声振动时的PP熔体温升增幅高出34.7%,而HDPE熔体的温升增幅则高达71.7%。当超声频率和工艺参数一定时,增大超声功率使PP熔体的微通道出口温升增加了24.8%,HDPE熔体的温升增加了83.6%。可见施加超声外场作用能使微通道中聚合物熔体的黏性耗散效应明显增强。 相似文献
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Gang Xu Zhi-tao Yang Guo-dong Long 《The International Journal of Advanced Manufacturing Technology》2012,58(5-8):521-531
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. 相似文献
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基于BP-NSGA的注塑参数多目标智能优化设计 总被引:1,自引:0,他引:1
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。 相似文献
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Maosheng Tian Xiaoyun Gong Ling Yin Haizhou Li Wuyi Ming Zhen Zhang Jihong Chen 《The International Journal of Advanced Manufacturing Technology》2017,89(1-4):241-254
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. 相似文献
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Mohammad Hadi Gholami Mahmood Reza Azizi 《The International Journal of Advanced Manufacturing Technology》2014,73(5-8):981-988
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. 相似文献
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B. Surekha Lalith K. Kaushik Abhishek K. Panduy Pandu R. Vundavilli Mahesh B. Parappagoudar 《The International Journal of Advanced Manufacturing Technology》2012,58(1-4):9-17
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. 相似文献
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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. 相似文献
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Jian-guang Li Yong Lu Hang Zhao Peng Li Ying-xue Yao 《The International Journal of Advanced Manufacturing Technology》2014,70(1-4):117-124
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. 相似文献