遗传算法与优化注射机合模机构运动行程

将遗传算法应用于注射机合模机构运动行程的最大化设计研究。优化中运动行程为目标函数，肘杆断面尺寸、肘杆起始与最终位置为设计变量，考虑了强度、稳定性、锁模力等约束条件。从研究结果来看，应用遗传算法进行运动行程的优化设计是可行且高效，可在注塑机合模机构运动行程最大化设计中推广应用。     

注塑机负后角型肘杆机构的优化及特性分析

讨论新型的注塑机负后角肘杆机构的特点,并通过MATLAB软件分别对正后角和负后角肘杆机构进行多目标规划问题的优化计算.分别利用MATLAB及Pro/E软件对肘杆机构进行运动仿真,并对比两种结构的运动与力学特性.对负后角型注塑机合模机构的性能分析、设计及优化方法均作了详细介绍.     

基于稳健性的肘杆传动机构的优化设计

采用复数矢量法建立了肘杆传动机构的运动方程,运用稳健设计原理,以肘杆传动机构的几何参数为设计变量,以满足曲柄摇杆机构及滑块位移条件为约束,以滑块冲压阶段的最大速度以及速度波动为目标函数,同时考虑杆件制造误差的影响,采用具有正态分布参数的蒙特卡洛法采样和多目标遗传算法对机构进行稳健性优化设计。优化结果表明,该方法更符合实际情况,且当设计变量发生变异时,能有效保证机构性能。     

机械压力机肘杆传动机构的优化设计

采用复数矢量法建立了机械压力机的运动方程,采用实数编码的遗传算法,并对主要参数的选取方法进行了改进,将违约解转化法和退火惩罚函数法相结合来处理约束函数,用改进后的遗传算法对机械压力机肘杆传动机构进行了优化设计.优化计算结果表明,改进后的遗传算法能够以较快的速度收敛到符合实际生产要求的全局最优解.     

注塑机肘杆合模机构弹性力学性能的探讨

从注塑机肘杆合模机构弹性变形的运行特征进行分析研究，介绍了作者对弹性力学在注望机肘杆合模机构中研究应用的一些成果。研究了拉杆和肘杆两者之间抗拉刚度的关系，提出了拉杆及肘杆根据拉伸刚度的设计方法及准则；研究了推力座导杆、销轴、模板支架、模板等抗弯挠度的特征及其相互之间的联系，提出了有关零件抗弯挠度的设计方法及准则；研究了有关零件的挠度和强度之间的内在联系，提出了根据挠度要求进行强度设计的准则。     

塑料成型机肘杆机构的优化设计

讨论优化设计塑料注射成型机肘杆机构的方法。用优化设计方法所获得的机构尺寸小,节能,提高了整机的工作速度,节省了大量的设计时间。     

纸板压痕机曲柄肘杆机构的优化设计

本文从多杆机构的运动分析和受力分析入手,对纸板压痕机曲柄肘杆机构进行优化设计,建立起三种目标函数,采用罚函数法进行优化计算,得到各构件的合理长度。     

J88型冷挤压机拉力肘杆机构的优化设计

本文在文献［１］基础上，对Ｊ８８型冷挤压机拉力肘杆机构的优化设计进行了全面的论述。     

油缸驱动肘杆机构合模部件开模瞬时停顿的研究

油缸驱动肘杆机构合模部件触动系统在注塑成型的开模过程中出现的瞬时停顿的不连续运动现象,分析了肘杆机构合模部件弹性变形能的实质,从理论和实例上对瞬时停顿的不连续运动现象进行论证,说明主要原因是由于油缸动力驱动系统与肘杆机构合模部件弹性力学的两者之间的动态性能不匹配所引起。提出的油马达—滚珠丝杆—肘杆机构等组合的传动系统,改变了合模部件弹性恢复能量转化型式,达到弹性恢复不干涉开模运动速度,实现开模的连续运动;传动系统组成型式的改变,打破传动的油缸驱动肘杆机构合模部件传动系统的增力比的设计极限,由传统的约20倍可增大到约100倍,实现肘杆机构合模部件节能降耗驱动的突破,拓展了运动性能优化设计空间。     

精密冲床肘杆机构刚柔耦合模型的仿真分析

精密冲床肘杆机构是一种多杆件机构,在运动过程中各杆件均存在弹性变形。肘杆机构的杆件多,各杆件的弹性变形严重降低了冲床的线刚度指标和滑块最终的下死点精度。试图通过将机构中柔性大的构件视为柔性体,建立刚柔耦合模型,并采用该刚柔耦合模型进行ADAMS运动过程的动态仿真来分析影响肘杆传动机构刚度的关键因素,探索改善其刚度的有效措施。通过ANSYS对肘杆机构模型进行了静力学分析,寻找弹性变形最大,对运动输出精度影响最大的构件,将其视为柔性体,建立刚柔耦合模型并在ADAMS进行仿真分析,验证建立刚柔耦合模型的必要性并指导后期的拓扑优化设计。     

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.     

Micro genetic algorithm based optimal gate positioning in injection molding design

The paper deals with the optimization of runner system in injection molding design. The design objective is to locate gate positions by minimizing both maximum injection pressure at the injection port and maximum pressure difference among all the gates on a product with constraints on shear stress and/or weld-line. The analysis of filling process is conducted by a finite element based program for polymer flow. Micro genetic algorithm (mGA) is used as a global optimization tool due to the nature of inherent nonlinearlity in flow analysis. Four different design applications in injection molds are explored to examine the proposed design strategies. The paper shows the effectiveness of mGA in the context of optimization of runner system in injection molding design.     

节能注塑机合模机构多目标优化设计研究

对节能注塑机合模机构进行了运动和动力学分析,以机构行程比较大、力放大比较大和机构尺寸较小为目标函数建立了多目标优化数学模型,使用Matlab优化工具箱对其进行了优化设计,通过实例给出了优化结果并进行比对,验证了优化模型的正确性,为注塑机合模机构的优化设计提供了一定的参考。     

基于遗传算法和AMI二次开发的注塑模具流道系统优化设计

针对注塑模具流道系统的特殊性,将数值模拟和遗传算法相结合,运用遗传算法作为全局性搜索工具,利用AMI软件二次开发技术在注塑模拟分析上的能力,搜索最优的流道几何参数,并求得符合熔接线生成位置、翘曲量和用料量要求的最佳优化工艺方案.     

基于遗传算法的工步优化排序方法

针对数控加工中心上零件加工工步的排序问题,以辅助加工时间最短为优化目标,使用遗传算法对零件在一次装夹情况下的加工工步进行优化排序。提出了使用特征关系图和特征高度描述待加工特征之间加工的优先顺序、采用工步优先关系矩阵校验工步序列合理性的方法。论述了初始群体的生成、遗传算子以及工步优化排序的过程和算法。实际应用表明,该方法可有效提高工艺规划系统中工步的优化排序能力。     

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。     

基于神经网络和遗传算法的薄壳件注塑成型工艺参数优化

建立基于神经网络和遗传算法并结合正交试验的薄壳件注塑成型工艺参数优化系统.正交试验法用来设计神经网络的训练样本,人工神经网络有效创建翘曲预测模型;遗传算法完成对影响薄壳塑件翘曲变形的工艺参数(模具温度、注射温度、注射压力、保压时间、保压压力和冷却时间等)的优化,并计算出其优化值.按该参数进行试验,效果良好,可以有效地减小薄壳塑件翘曲变形,其试验数值与计算数值基本相符,说明所提出的方法是可行的.     

Multi-objectives optimal model of heavy equipment using improved Strength Pareto Evolutionary Algorithm

The problem of injection molding machine’s multi-objective optimization is very important. A triple-objective optimization model with the largest mould moving speed and injecting capacities and the smallest injecting power has been created. The optimized design constraints of the optimal model are summarized. The computational efficiency of Strength Pareto Evolutionary Algorithm (SPEA) is improved by using rough set-based support vector clustering method. The number of external stocks is reduced. The optimal Pareto solution is determined by eliminating the uncertainty in the artificial priority election. The multi-objective optimization of the HT1600X1N injection molding machine is taken as an example. The SPEA-RSVC-II which is the mixed algorithm of Strength Pareto Evolutionary Algorithm and Ro′ugh-based Support Vector Clustering is applied. It shows that the new method could accelerate the population clustering operation effectively and improves the efficiency of optimized calculation.     

A study of optimization of injection molding process parameters for SGF and PTFE reinforced PC composites using neural network and response surface methodology

This study analyzed variations of mechanical characteristics that depend on the injection molding techniques during the blending of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites. A hybrid method including back-propagation neural network (BPNN), genetic algorithm (GA), and response surface methodology (RSM) are proposed to determine an optimal parameter setting of the injection molding process. The specimens are prepared under different injection molding processing conditions based on a Taguchi orthogonal array table. The results of 18 experimental runs were utilized to train the BPNN predicting ultimate strength, flexural strength, and impact resistance. Simultaneously, the RSM and GA approaches were individually applied to search for an optimal setting. In addition, the analysis of variance was implemented to identify significant factors for the injection molding process parameters and the result of BPNN integrating GA was also compared with RSM approach. The results show that the RSM and BPNN/GA methods are both effective tools for the optimization of injection molding process parameters.