Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method

In this study, an adaptive optimization method based on artificial neural network model is proposed to optimize the injection molding process. The optimization process aims at minimizing the warpage of the injection molding parts in which process parameters are design variables. Moldflow Plastic Insight software is used to analyze the warpage of the injection molding parts. The mold temperature, melt temperature, injection time, packing pressure, packing time, and cooling time are regarded as process parameters. A combination of artificial neural network and design of experiment (DOE) method is used to build an approximate function relationship between warpage and the process parameters, replacing the expensive simulation analysis in the optimization iterations. The adaptive process is implemented by expected improvement which is an infilling sampling criterion. Although the DOE size is small, this criterion can balance local and global search and tend to the global optimal solution. As examples, a cellular phone cover and a scanner are investigated. The results show that the proposed adaptive optimization method can effectively reduce the warpage of the injection molding parts.     

基于过程的优化算法的研究及应用

针对传统机械优化算法所存在的问题，提出了一种基于过程的优化算法。它利用人工神经网络，构造过程系统结构空间，采用随机涨落机制，以模拟退火方式，模拟自然界演化过程进行优化，以过程的收敛性作为全局最优性判据。最终，过程的收敛点即优化问题的最优解。     

An effective warpage optimization method in injection molding based on the Kriging model

In this paper, an effective optimization method using the Kriging model is proposed to minimize the warpage in injection molding. The warpage deformations are nonlinear, implicit functions of the process conditions, which are typically evaluated by the solution of finite element (FE) equations, a complicated task which often involves huge computational effort. The Kriging model can build an approximate function relationship between warpage and the process conditions, replacing the expensive FE reanalysis of warpage in the optimization. In addition, a “space-filing” sampling strategy for the Kriging model, named rectangular grid, is modified. Moldflow Corporation’s Plastics Insight software is used to analyze the warpage deformations of the injection-molded parts. As an example, the warpage of a cellular phone cover is investigated, where the mold temperature, melt temperature, injection time, and packing pressure are regarded as the design variables. The result shows that the proposed optimization method can effectively decrease the warpage deformations of the cellular phone cover and that the injection time has the most important influence on warpage in the chosen range.     

隐式极限状态方程的非概率可靠性分析

针对凸集模型比例因子的非概率可靠度指标相对隐式极限状态方程难以求解问题,提出一种基于支持向量机回归的非概率可靠度指标分析方法.所提方法用支持向量机回归拟合极限状态方程,通过优化算法获得可靠度指标和设计点,用设计点更新支持向量机训练样本的抽样中心,并重复计算过程直至收敛.由于构造合适的迭代格式可以有效地近似结构的真实失效域边界,故求解精度好,又由于使用极限状态方程的代理措施,使得计算效率高.文中用四个数值算例证明方法的精度和效率,并将文中方法用于实际的飞机机翼可靠性分析中.     

An integrated blade optimization approach based on parallel ANN and GA with hierarchical fair competition dynamic-niche

This work presented an available multi-point blade optimization procedure for better aerodynamic performances. Based on the proposed Parallel ANN and GA with hierarchical fair competition dynamic-niche (GA-HFCDN), an integrated approach for the blade optimization design was put forward by combining Bezier parameterization with FINE/TURBO solver. In the optimization design, parallel ANN was employed to build a more proper approximate model. GA-HFCDN was proposed to obtain the global optimization solution more efficiently for the blade design. Two research cases, including plane cascades blade optimization and the optimization and experimental study of a low specific speed centrifugal blower blade, were performed by using the above approach. The conclusions showed the rationality and validity of the optimization approach.     

An enhanced optimization approach based on Gaussian process surrogate model for process control in injection molding

A stepwise optimization approach based on Gaussian process (GP) surrogate model is proposed to determine the process parameters and improve the quality control for injection molding. In order to improve the global performance in this optimization, an enhanced probability of improvement criterion is also introduced. Firstly, GP surrogate model is constructed with the initial samples which are obtained from an optimal design of experiment method. GP is capable of giving both a prediction and an estimate of the confidence for the prediction simultaneously. Secondly, an enhanced probability of improvement criterion is used to find the direction of adding training samples and optimize the surrogate model. Since the global optimal region of the model become accurate efficiently after steps of optimizing the surrogate model, the proposed enhanced probability of improvement criterion can switch more swiftly to global optima compared with other improvement criterion. Finally, an auto front grille molding process is taken as an example to illustrate the method. The results show that the proposed optimization method can effectively decrease the warpage of injection-molded parts.     

A modified global optimization method based on surrogate model and its application in packing profile optimization of injection molding process

This article introduces a step-by-step optimization method based on the radial basis function (RBF) surrogate model and proposes an improved expected improvement selection criterion to better the global performance of this optimization method. Then it is applied to the optimization of packing profile of injection molding process for obtaining best shrinkage evenness of molded part. The idea is first, to establish an approximation function relationship between shrinkage evenness and process parameters by a small size of design of experiment with RBF surrogate model to alleviate the expensive computational expense in the optimization iterations. And then, an improved criterion is used to provide direction in which additional training samples could be added to better the surrogate model. Two test functions are investigated and the results show that stronger global exploration performance and more precise optimal solution could be obtained with the improved method at the expense of increasing the infill data properly. Furthermore the optimal solution of packing profile is obtained for the first time which indicates that the type of optimal packing profile should be first constant and then ramp-down. Subsequently, the discussion of this result is given to explain why the optimal profile is like that.     

基于随机模型的动车组制动模块稳健优化设计

针对目前产品优化设计中未考虑设计变量随机性和灵敏度指数对产品质量特性的影响及优化求解效率低等问题,提出一种综合考虑灵敏度指数与质量损失函数的稳健优化设计方法。通过假设设计变量的分布类型,得到设计变量的概率分布特征,将设计变量对产品质量特性的灵敏度指数与质量损失函数加权整合,并以此作为优化目标,以设计变量的容差作为约束条件,构建随机稳健优化模型。通过扩大种群数目、改进控制参数及增加惩罚因子的方法对遗传算法进行改进,结合改进的遗传算法对优化模型进行求解,得到优化模型的全局稳健最优解。以某动车组制动模块为例,采用给出的方法对其进行稳健优化设计,验证该方法的有效性与合理性。优化结果表明,该方法能够实现动车组制动模块的稳健优化设计,提高了制动模块的抗干扰能力。     

Hybrid FE/ANN and LPR approach for the inverse identification of material parameters from cutting tests

Accuracy of numerical models based in finite elements (FE), extensively used for simulation of cutting processes, depends strongly on the identification of proper material parameters. Experimental identification of the constitutive law parameters for simulation of cutting processes involves unsolved problems such as the complex testing techniques or the difficulty to reproduce the stress triaxiality state during cutting. This work proposes a methodology for the inverse identification of the material parameters from cutting test. Two hybrid approaches are compared. One of them based on FE and artificial neural networks (ANN). The other one based on FE and local polynomial regression (LPR). Firstly, a FE model is validated with experimental data. Then, ANN and LPR are trained with FE simulations. Finally, the estimated ANN and LPR models are used for the inverse identification of material parameters. This identification is solved as an optimization problem. The FE/LPR approach shows good performance, outperforming the FE/ANN approach.     

Metamodel-based global optimization using fuzzy clustering for design space reduction

High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization methods must be developed to relieve the computational burden.A new metamodel-based global optimization method using fuzzy clustering for design space reduction(MGO-FCR) is presented.The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel,whose accuracy is improved with increasing number of sample points gradually.Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively.Modeling efficiency and accuracy are directly related to the design space,so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms.The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated.The first pseudo reduction algorithm improves the speed of clustering,while the second pseudo reduction algorithm ensures the design space to be reduced.Through several numerical benchmark functions,comparative studies with adaptive response surface method,approximated unimodal region elimination method and mode-pursuing sampling are carried out.The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions.And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems.Based on this global design optimization method,a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms.This method possesses favorable performance on efficiency,robustness and capability of global convergence and gives a new optimization strat     

函数优化的通用SA算法及其在电动滚筒优化设计中的应用

对模拟退火算法用于函数优化时的计算流程、状态产生函数的设置原则、离散变量的优化、计算终止准则等问题进行深入分析,在此基础上提出一种适用于求解各类数学规划问题全局最优解的有记忆功能、带返回搜索的模拟退火算法,用可视化程序设计语言Visual Basic6.0开发相应的模块化计算机程序,并给出YT型电动滚筒优化设计的应用实例.该程序不仅使用方便,而且对各类数学规划问题具有普适性.     

Dynamic modeling and control of flexible space structures

This paper presents a global mode modeling of space structures and a control scheme from the practical point of view. Since the size of the satellite has become bigger and the accuracy of attitude control more strictly required, it is necessary to consider the structural flexibility of the spacecraft. Although it is well known that the finite element (FE) model can accurately model the flexibility of the satellite, there are associated problems: FE model has the system matrix with high order and does not provide any physical insights, and is available only after all structural features have been decided. Therefore, it is almost impossible to design attitude and orbit controller using FE model unless the structural features are in place. In order to deal with this problem, the control design scheme with the global mode (GM) model is suggested. This paper describes a flexible structure modeling and three-axis controller design process and demonstrates the adequate performance of the design with respect to the maneuverability by applying it to a large flexible spacecraft model.     

混合遗传-模拟退火算法应用于液压集成块优化设计的研究

以液压集成块布局布孔优化设计问题为背景,给出了在立体空间中带性能约束的多目标优化数学模型。针对遗传算法在求解该组合优化问题时局部搜索能力不强的情况,将模拟退火算法与之结合,设计了多参数级联的编码方法,且加入记忆装置,构造了一种具有记忆功能的混合遗传-模拟退火算法。实例计算表明,改进后的新算法能很好地改善对解空间的局部搜索能力,从而得到较高质量的解。     

约束阻尼变密度材料属性合理近似插值模型全域灵敏度减振优化

针对圆柱壳约束阻尼拓扑减振优化问题,基于变密度材料属性合理近似插值模型,构建以模态损耗因子最小为优化目标的拓扑减振模型,引入振型控制因子MAC评价振型的阶跃状态,寻优迭代轻量化约束设计域内的减振优化解。推导目标灵敏度的插值模型,演算全域优化求解算法,建立∞-范数的全域灵敏度拓扑变量优化准则,避免非凸优化函数的部分目标灵敏度参与优化迭代的局部优化解和跳跃。编程实现了壳构件约束阻尼结构的减振优化分析。结果表明：全域优化算法寻优域广,迭代历程稳定时间短、敷设部分阻尼材料易获得有效的减振效果。     

基于Kriging函数的序贯近似建模方法

多学科设计优化过程中需要多次调用高精度学科分析模型,从而造成计算复杂性问题.为解决上述计算复杂性问题,首先提出基于最优LHD的逆序贯试验设计方法,在此基础上结合模型验证,建立了基于Kriging函数的序贯近似建模方法,提出了序贯近似建模收敛准则,通过权衡训练样本数据数量和近似精度,得到精度与效率折衷的近似模型.数学算例证明了该方法的有效性.     

大型直缝焊管预弯成形工艺稳健性设计

为提高预弯成形质量,采用有限元法分析了预弯成形过程,并验证了计算结果的准确性。在此基础上,考虑板料的材料性能参数等不确定因素对成形质量的影响,并基于随机模型的稳健性设计方法获得了预弯成形技术参数的稳健性优化设计方案。研究结果表明:稳健性优化使成形质量的性能指标得到了改善,将可靠性由4.2%提高到97.7%,显著提高了焊管成形质量可靠性。     

Kriging和距离因子辅助的全局优化方法

基于Kriging元模型的优化方法通常存在优化效率较低、收敛速度较慢、昂贵估值次数较多且难以有效平衡Kriging模型的局部与全局搜索行为等弊端。为此,提出一种序列Kriging和距离因子辅助的全局优化方法。其实现过程主要包含两个阶段:一是利用Kriging模型近似复杂昂贵的黑箱函数;二是利用Kriging模型与距离因子的乘积构造加点采样法则,并通过免于求导的DIRECT算法优化该法则,以获取新的昂贵估值点。六个数值函数测试和一个摆线泵仿真实例验证所提出方法的有效性。     

Global optimization method using SLE and adaptive RBF based on fuzzy clustering

High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually unacceptable.In order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation time.An efficient heuristic global optimization method using adaptive radial basis function(RBF) based on fuzzy clustering(ARFC) is proposed.In this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration(SLE) is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels accuracy.RBF method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually enhanced.The fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design space.The numerical benchmark examples are used for validating the performance of ARFC.The results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method(ARSM) proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design optimum.This method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.     

在Multi-Agent环境下机械产品协同优化

根据协同优化算法的思想,提出了一种Multi-Agent模型,利用神经网络建立子系统优化Agent的近似响应面。子任务规划Agent进行传统的优化进程;任务调度Agent根据结果分析Agent计算的样本集近似满意度自主选择不同的执行路径;近似响应面Agent通过使用更新准则,逐步获取满足子系统级约束和目标的精确的响应面。当子系统获取指标变量后,通过子系统优化Agent内部的响应面快速获取优化解向量,并将该向量返回到全局黑板数据结构中,系统级优化Agent可以利用该结构,协调各个子Agent不一致的信息,从而有效地提高了复杂机械产品协同优化中的效率和精度。     

Geometric optimization and performance analysis of radial MR valve using Taguchi orthogonal experiment method

Aiming at the problems of increasing external dimensions and deteriorating key performance indicators in the design process of magnetorheological (MR) valve by using structural optimization method, a geometric optimization design methodology for the optimal design of a MR valve structure under specific volume constraints is proposed in this article. The optimization methodology couples the finite element model (FEM) constructed in COMSOL software with the Taguchi orthogonal experiment and response surface technology to build an approximate response surface function for the identified independent variables. Suitable optimization algorithms are then utilized to determine the optimal geometry of the MR valve, thereby maximizing the valve performance. Firstly, a radial MR valve with a single excitation coil was presented, and its structure and working principle were also elaborated. A mathematical model of the pressure drop was derived on the basis of the Bingham-Papanastasiou non-Newtonian constitutive model of MR fluid and the magnetic circuit had been analyzed with the FE analysis methodology. Then, a second-order response surface model (RSM) had been fitted for the magnetic flux density in the radial flow channel and spool region of the radial MR valve based on the Taguchi orthogonal experimental design. The fitted model was a function of the four independent variables of the radial MR valve, and the accuracy of the developed response surface function over the entire design space had also been estimated. Meanwhile, predictions made by the RSM and FE models were evaluated by analysis of variance and it was exhibited that the RSM model’s results agree with FE result fairly. Subsequently, the geometric optimization problem had been formulated for the constructed RSM exploiting the genetic algorithm to find the global optimum geometrical parameters of the radial MR valve. Furthermore, the experimental test rig was setup to explore the pressure drop and the response time characteristics of the initial and optimal radial MR valve as well as the dynamic performance of the MR valve controlled cylinder system under different excitation conditions. The experimental results show that under the applied current of 2 A, the pressure drop and adjustable coefficient of the optimal radial MR valve observably increased with values of 3.15 MPa and 5.40, respectively, when compared to 2.11 MPa and 4.22 of their respective initial values. Also, at the applied current of 1.25 A, the damping force of the MR valve controlled cylinder system enlarged by 46 %, with its optimal value being 3.65 kN and initial value as 2.50 kN, which was an excellent verification of the correctness of the RSM and the effectiveness of the optimal design.