塑料透镜注射压缩成形新方法

塑料透镜的批量生产，通常采用注射成形法；透镜的精度要求较高时，则使用注射压缩成形技术。本文提出注射成形应注意的一些共通性问题、简述注射压缩成形及其实值，详细介绍最近开发成功的一种精密注射压缩成形技术。     

塑料透镜精密注射成形技术

阐述了塑料透镜注射充填成形过程中树脂 介绍度和压力之间的关系，推荐一种塑料透镜精密注成形技术 ，即封堵注口成形法；介绍了实际实验过程和实验结果；提出了实施这一成形的工艺方案。     

三维打印成形技术在制药工程中的应用

快速成形与制药工程的结合体现了先进制造技术的发展方向。介绍了采用三维打印成形技术制造先进可控释放和智能微晶片药物的方法，并分析了这种方法的优势和可行性，进一步讨论了三维打印成形技术应用于制药工程所涉及到的一些关键技术。     

金属注射成形技术与钟表零件加工

一、前言金属注射成形（Ｍｅｔａｌ Ｉｎｊｅｃｔｉｏｎ Ｍｏｌｄｉｎｇ,简称ＭＩＭ）是一种从塑料注射成形行业中引申出来的新型 粉末冶金近净成形技术。众所周知,塑料注射成形技术能以低廉的价格生产各种复杂形状的制品,但塑 料制品强度不高。为了改善其性能,可以在塑料中添加金属或陶瓷粉末以得到强度较高、耐磨性好的制 品。近年来,这一想法已发展演变为最大限度地提高固体粒子的含量并且在随后的烧结过程中完全除去 粘结剂并使成形坯致密化。这种新的粉末冶金成形方法称为金属注射成形。 二、金属注射成形技术的原理和基本工艺过…     

阵列式微流道的粉末微注射成形技术

作为一种近净成形技术,粉末微注射成形具有低成本、高精度、高效率的特点,在成形微小型复杂形状的零件方面优势突出。文中详述了粉末微注射成形制备工艺,采用纳米级粉末材料,通过优化成形参数,成功制备了满足实际使用要求的阵列式微流道零件,成形精度误差低,不超过1.5%,降低成本50%以上。此粉末微注射成形制备阵列式微流道技术对电子机械领域微小型电子元器件的设计和制造具有重要的借鉴意义。     

个性化钛合金颅骨修复体成形仿真

针对钛合金颅骨修复体的数字化制造，依据实际成形条件建立有限元模型，采用薄板成形数值模拟技术对颅骨修复体的成形过程进行仿真，从而事先预测可能出现的缺陷(拉裂、起皱与回弹等)，优化调整冲压工艺参数。实践表明，利用仿真结果指导实际制造过程，大大提高了修复体成形的一次成功率，可消除成形的不确定性，保证个性化制造的成形质量。     

复合材料预制体成形制造工艺与装备研究

从制造工艺和装备研制的角度总结了复合材料预制体成形技术发展历程,分析了复合材料预制体成形制造工艺的技术特点,列举了国内外复合材料预制体成形制造典型的工艺与装备现状以及存在的问题及差距,并分析展望了复合材料预制体成形技术的未来发展方向及趋势。发展高性能复合材料构件数字化精确成形制造技术及装备,能更好推动我国复合材料技术进步,并实现更加广泛的推广应用。     

光学塑料镜片的注射成形

本文介绍了具有广阔发展前途的光学塑料镜片的注射成形工艺和装置。其中包括注射成型的选择、模具的设计、成形工艺及镜片表面的硬化处理等。     

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。     

精密注塑成形技术

目前塑料己发展成为金属的代用材料。而在注塑成形中由于塑料的收缩特性,成形模具的设计和制造工艺,注塑成形参数的设置及注塑件几何形状设计等因素的影响,使注塑成形件与金属零件相比,较难获得高的精度。因此针对这些影响因素,应采取有效的措施提高注塑件的精度,加速精密零件的塑料化,以适应电子、家用电器等行业的需要。     

面向虚拟制造的塑料注射成形CAE模拟

虚拟制造是塑件及模具计算机辅助设计技术的发展方向,为了有效地将CAE集成到塑料注射件的虚拟制造系统中,建立了基于表面模型的智能CAE分析系统。通过引入表面模型取代传统的“中性层”模型,使CAE与CAD的集成成为可能,并突破了原有CAE软件的建模瓶颈。将人工智能和专家系统技术充分应用到CAE分析中,指导用户设计初始方案、评价分析结果并在此基础上优化设计,提高了CAE的应用价值和使用效率。     

Multiobjective optimization of injection molding parameters based on soft computing and variable complexity method

The objective of this study is to propose an intelligent methodology for efficiently optimizing the injection molding parameters when multiple constraints and multiple objectives are involved. Multiple objective functions reflecting the product quality, manufacturing cost and molding efficiency were constructed for the optimization model of injection molding parameters while multiple constraint functions reflecting the requirements of clients and the restrictions in the capacity of injection molding machines were established as well. A novel methodology integrating variable complexity methods (VCMs), constrained non-dominated sorted genetic algorithm (CNSGA), back propagation neural networks (BPNNs) and Moldflow analyses was put forward to locate the Pareto optimal solutions to the constrained multiobjective optimization problem. The VCMs enabled both the knowledge-based simplification of the optimization model and the variable-precision flow analyses of different injection molding parameter schemes. The Moldflow analyses were applied to collect the precise sample data for developing BPNNs and to fine-tune the Pareto-optimal solutions after the CNSGA-based optimization while the approximate BPNNs were utilized to efficiently compute the fitness of every individual during the evolution of CNSGA. The case study of optimizing the mold and process parameters for manufacturing mice with a compound-cavity mold demonstrated the feasibility and intelligence of proposed methodology.     

塑料注射成形过程仿真软件的开发和应用

智能型3维注射成形过程仿真系统HSCAE　3D解决了用3维实体／表面模型取代中性层模型的关键性技术难题，通过数值计算与人工智能技术的结合，使仿真软件由传统的被动式计算工具提升为主动式优化系统。实验和实践证明，HSCAE　3D为注塑制品与模具的虚拟制造奠定了坚实的理论和技术基础，构成了注塑制品成形质量全面控制的核心技术，已在模具行业中得到了很好的应用。     

Minimizing manufacturing costs for thin injection molded plastic components

Minimizing the cost of manufacturing a plastic component is very important in the highly competitive plastic injection molding industry. The current approach of R&D work focuses on optimizing the dimensions of the plastic component, particularly in reducing the thickness of the component during product design, the first phase of manufacturing, in order to minimize the manufacturing cost. This approach treats the component dimensions established in the product design phase as the given input, and uses optimization techniques to reduce the manufacturing cost of mold design and molding for producing the component. In most cases, the current approach provides the correct solution for minimizing the manufacturing cost. However, when the approach is applied to a thin component, typically when miniaturizing products, it has problems finding the true minimum manufacturing cost. This paper analyses the shortcomings of the current approach for handling thin plastic components and proposes a method to overcome them. A worked example is used to illustrate the problems and compare the differences when using the current approach and the new method proposed in the paper.     

Minimizing manufacturing costs for thin injection molded plastic components

Minimizing the cost of manufacturing a plastic component is very important in the highly competitive plastic injection molding industry. The current approach of R&D work focuses on optimizing the dimensions of the plastic component, particularly in reducing the thickness of the component during product design, the first phase of manufacturing, in order to minimize the manufacturing cost. This approach treats the component dimensions established in the product design phase as the given input, and uses optimization techniques to reduce the manufacturing cost of mold design and molding for producing the component. In most cases, the current approach provides the correct solution for minimizing the manufacturing cost. However, when the approach is applied to a thin component, typically when miniaturizing products, it has problems finding the true minimum manufacturing cost. This paper analyses the shortcomings of the current approach for handling thin plastic components and proposes a method to overcome them. A worked example is used to illustrate the problems and compare the differences when using the current approach and the new method proposed in the paper.     

[学科发展]大数据驱动的智能制造

数据是未来制造业的核心要素,工业大数据分析是赋予制造“智能”的关键。系统分析了大数据驱动的智能制造的科学范式、理论方法与使能技术,阐述了应用方向与工业实践;根据“第四范式：数据密集型科学发现”,提出了“关联-预测-调控”的大数据驱动智能制造科学范式;根据数据处理流程,总结了融合处理、关联分析、性能预测与优化决策四位一体的方法体系。围绕边缘层、平台层和应用层设计大数据平台,介绍了大数据驱动智能制造的使能技术;从智能设计、计划调度、质量优化、设备运维四个角度,综述工业大数据驱动的智能制造应用现状。     

Intelligent Manufacturing Systems in COVID-19 Pandemic and Beyond: Framework and Impact Assessment

Pandemics like COVID-19 have created a spreading and ever-higher healthy threat to the humans in the manufacturing system which incurs severe disruptions and complex issues to industrial networks. The intelligent manufacturing(IM) systems are promising to create a safe working environment by using the automated manufacturing assets which are monitored by the networked sensors and controlled by the intelligent decision-making algorithms. The relief of the production disruption by IM technologies facilitates the reconnection of the good and service flows in the network, which mitigates the severity of industrial chain disruption. In this study, we create a novel intelligent manufacturing framework for the production recovery under the pandemic and build an assessment model to evaluate the impacts of the IM technologies on industrial networks. Considering the constraints of the IM resources, we formulate an optimization model to schedule the allocation of IM resources according to the mutual market demands and the severity of the pandemic.     

浅析注射成型塑料件结构设计

从注射成型塑料件结构设计的角度出发,结合材料本身特性及注塑生产的工艺特点,总结了注射成型塑料件结构设计上的一些要点和注意事项,列举了若干设计中经常采取的结构形式和改进方法,为注射成型塑料件的结构设计给出了参考。     

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.     

Intelligent methods for the process parameter determination of plastic injection molding

Injection molding is one of the most widely used material processing methods in producing plastic products with complex geometries and high precision. The determination of process parameters is important in obtaining qualified products and maintaining product quality. This article reviews the recent studies and developments of the intelligent methods applied in the process parameter determination of injection molding. These intelligent methods are classified into three categories: Case-based reasoning methods, expert system- based methods, and data fitting and optimization methods. A framework of process parameter determination is proposed after comprehensive discussions. Finally, the conclusions and future research topics are discussed.