Sink-mark minimization in injection molding through response surface regression modeling and genetic algorithm

This paper deals with minimization of sink depths in injection-molded thermoplastic components by integrating finite element (FE) flow analysis with central composite design (CCD) of experiments and genetic algorithm (GA). Sink-mark depth depends on various process and design variables. Out of all, four most influential variables viz. melt temperature, mold temperature, pack pressure, and rib-to-wall ratio were used for optimization. A set of FE analyses were conducted at various combinations of variables based on the CCD array. A second-order-response surface regression model (RSRM) was developed based on the CCD. The second-order model was effectively coupled with GA for optimization of variables to minimize the sink depth. Results are encouraging and the proposed methodology could be used effectively in minimizing sink-mark depths.     

Conditions of rib design for polycarbonate resin with high glossy surfaces

Much attention has been being given to the importance of product surfaces in the field of plastic parts, as industrial design has become one of the key elements of product success. These plastic parts incorporate rib-like geometries on the non-appearance surfaces of plastic in order to increase the stiffness of rigidity of the section, but they often cause appearance problems of the product’s surface overall by making a sink mark on that surface. The thickness, height and draft-angle of the rib are generally known as major parameters influencing the sink mark on the appearance surface. Therefore, designers of plastic parts must determine the variables of reinforcing ribs. The goal of this study is to find the optimum design variables in the mixing conditions of the thickness, the height and the draft angle of reinforcing ribs so that designers of plastic parts can easily determine the conditions of the reinforcing ribs as the part’s section thickness varies within an objective limit in polycarbonate plastic resin and a high glossy surface that are widely applied in the creation of plastic products. We investigated the actual depths of sink marks on the surface of a specimen that was manufactured with an injection mold specifically for this study. Response surface methodology with the Box-Behnken design was used to analyze the regression curve of real depths with combinations of the thickness, height and draft angle of the ribs. The result shows that the most influential factor to increase the shrinkage is the thickness of ribs and that the optimum value of the rib thickness is a range from multiple of 0.25 to 0.34 of the section thickness. Also, the rib height and rib draft angle are not major factors that can change the sink amount.     

Prediction of sink depths using nonlinear modeling of injection molding variables

This paper deals with prediction of sink mark defects and its intensities on injection-molded thermoplastics components. A nonlinear mathematical model, in terms of injection molding variables, was developed using response surface methodology. Fractional factorial design (FFD) of experiments was used for initial screening of variables. Based on FFD, the four most influential and controllable injection molding variables were selected. Central composite design (CCD) of experiments was structured and conducted using flow simulation to formulate the predictive nonlinear model. Statistical analysis and experimental results suggest that the proposed model could be used for predicting sink mark depths with adequate accuracy. It indicates that this predictive model can be used for drawing tailor-made guidelines for designing as well as for processing. If it is applied at design stage, corrective and iterative design steps can be initiated and implemented for better quality of products without resorting to physical trials on molds. Proposed methodology can also be effectively employed in controlling the quality of products throughout the product life cycle.     

响应面法与遗传算法相结合的注塑工艺优化

应用田口方法进行试验设计,应用计算机辅助工程技术对注塑成形过程进行了分析,建立了注塑成形工艺参数与翘曲度关系的代理模型——响应面模型,对模型进行了验证研究,将响应面法与遗传算法相结合进行了注塑工艺参数优化。结果表明,响应面模型是准确可靠的,将响应面法和遗传算法相结合,可有效提高运算速度和优化效率。     

矩形肋片散热器几何参数对散热的影响分析

为考察矩形肋片散热器几何参数对散热效果的影响规律,文中应用热仿真分析软件Flotherm对矩形肋片散热器在不同结构参数下的模型进行了自然对流散热计算,通过对比分析不同模型的温度和热阻计算结果,探讨了散热器基板参数和肋片参数对其散热性能的影响。分析表明,改变散热器肋片的高度、长度和间距可有效降低散热器的热阻。这些几何参数可以作为散热器热设计变量,以进一步对散热器进行优化设计。     

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.     

水介质单体液压支柱Y型密封圈的CAE优化分析

介绍了水介质单体液压支柱采用的密封元件———Y型密封圈的结构与成型;针对注射成型过程中Y型密封圈出现的主要成型缺陷———缩痕,分析了其产生的原因,提出了解决措施;运用Moldflow对Y型密封圈的注射成型过程进行CAE分析;从改进注射成型工艺参数和模具结构2个方面着手,提出3种优化方案进行CAE优化分析,并将所得之最优方案付诸于实际成型过程。依据最优方案成型的Y型密封圈其缺陷得以消除,符合水介质单体液压支柱对密封元件的要求。     

Minimization of variation in volumetric shrinkage and deflection on injection molding of Bi-aspheric lens using numerical simulation

The profile of a bi-aspheric lens is such a way that the thickness narrows down from center to periphery (convex). Injection molding of these profiles has high shrinkage in localized areas, which results in internal voids or sink marks when the part gets cool down to room temperature. This paper deals with the influence of injection molding process parameters such as mold surface temperature, melt temperature, injection time, V/P Switch over by percentage volume filled, packing pressure, and packing duration on the volumetric shrinkage and deflection. The optimal molding parameters for minimum variation in volumetric shrinkage and deflection of bi-aspheric lens have been determined with the application of computer numerical simulation integrated with optimization. The real experimental work carried out with optimal molding parameters and found to have a shallow and steep surface profile accuracy of 0.14 and 1.57 mm, 21.38-45.66 and 12.28-26.90 μm, 41.56-157.33 and 41.56-157.33 nm towards Radii of curvatures (RoC), surface roughness (Ra) and waviness of the surface profiles (profile error Pt), respectively.

     

Geometry optimization of a cylindrical fin heat sink using mine blast algorithm

The heat sinks are utilized in electronic devices to eliminate heat from the chip and efficiently transmit it to the environment. Therefore, the optimal geometry sizes of fin heat sinks are the point of concern for manufacturers and designers. For this reason, the importance of optimization techniques particularly metaheuristics is understood. The design variables are width of heat sink, number of fins, fin height, and fin diameter. The various responses that have been considered are electromagnetic emitted radiations, thermal resistance, and mass of the heat sink investigated separately and simultaneously (multi-objective). Mine blast algorithm (MBA), as a recently developed optimizer, is inspired from explosion of mines. The optimum dimensions and values for each response have been obtained by the MBA and have been compared with other optimization methods in the literature. In terms of thermal resistance and mass responses, the MBA has offered better values, while for the emitted radiations, the obtained results obtained by Taguchi-based gray relational analysis (TGRA) was preferred. For manufacturing point of view, the MBA and TGRA both suggested better and efficient design. In addition, the value path analysis has been carried out to compare the trade-off among the considered responses. Finally, parametric sensitivity analyses have been implemented for design parameters, and discussions and comparisons have been carried out for the effects of each decision variable. By considering all responses, width of heat sink and fin height are considered as the most important and effective design parameters, respectively.     

Shape Optimization of a plate-fin type heat sink with triangular-shaped vortex generator

In this study the optimization of plate-fin type heat sink with vortex generator for the thermal stability is performed numerically. The optimum solutions in the heat sink are obtained when the temperature rise and the pressure drop are minimized simultaneously. Thermal performance of heat sink is influenced by the heat sink shape such as the base-part fin width, lower-part fin width, and basement thickness. To acquire the optimal design variables automatically, CFD and mathematical optimization are integrated. The flow and thermal fields are predicted using the finite volume method. The optimization is carried out by means of the sequential quadratic programming (SQP) method which is widely used for the constrained nonlinear optimization problem. The results show that the optimal design variables are as follows ; B₁=2.584 mm, B₂=1.741 mm, and t=7.914 mm when the temperature rise is less than 40 K. Comparing with the initial design, the temperature rise is reduced by 4.2 K, while the pressure drop is increased by 9.43 Pa. The relationship between the pressure drop and the temperature rise is also presented to select the heat sink shape for the designers.     

Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm

During the production of thin shell plastic parts by injection molding, warpage depending on the process conditions is often encountered. In this study, efficient minimization of warpage on thin shell plastic parts by integrating finite element (FE) analysis, statistical design of experiment method, response surface methodology (RSM), and genetic algorithm (GA) is investigated. A bus ceiling lamp base is considered as a thin shell plastic part example. To achieve the minimum warpage, optimum process condition parameters are determined. Mold temperature, melt temperature, packing pressure, packing time, and cooling time are considered as process condition parameters. FE analyses are conducted for a combination of process parameters organized using statistical three-level full factorial experimental design. The most important process parameters influencing warpage are determined using FE analysis results based on analysis of variance (ANOVA) method. A predictive response surface model for warpage data is created using RSM. The response surface (RS) model is interfaced with an effective GA to find the optimum process parameter values.     

Applying the noising method to find the best regression model

Regression analysis is one of the most applicable methods in statistical methodology used to find the best regression model according to the relationship among several variables in a system. The estimation of regression model, which is solved as a formulate optimization problem and making use of heuristic algorithms, is much simpler and faster than classic methods. Genetic algorithm (GA) as one of the heuristic algorithms had been used to solve this problem. In this paper, we extend the noising method as a recent combinatorial optimization problem to estimate the best regression model and evaluate its performances compared to GA. Also, in order to enhance the performance of our GA, we apply the Taguchi experimental design method to tune the parameters of the algorithm.     

Process parameters optimization of injection molding using a fast strip analysis as a surrogate model

Injection molding process parameters such as injection temperature, mold temperature, and injection time have direct influence on the quality and cost of products. However, the optimization of these parameters is a complex and difficult task. In this paper, a novel surrogate-based evolutionary algorithm for process parameters optimization is proposed. Considering that most injection molded parts have a sheet like geometry, a fast strip analysis model is adopted as a surrogate model to approximate the time-consuming computer simulation software for predicating the filling characteristics of injection molding, in which the original part is represented by a rectangular strip, and a finite difference method is adopted to solve one dimensional flow in the strip. Having established the surrogate model, a particle swarm optimization algorithm is employed to find out the optimum process parameters over a space of all feasible process parameters. Case studies show that the proposed optimization algorithm can optimize the process parameters effectively.     

[成形过程仿真优化与集成计算材料工程]限定加热管布局的快速热循环注塑成形模具电加热系统优化

针对管道布局、最大允许能耗给定条件下快速热循环注塑成形（RHCM）注塑模具型腔表面快速均匀加热的问题,提出以单根加热棒热流密度为设计变量,以模具型腔表面升温效率和温度分布均匀性为目标,结合有限元模拟、响应面设计以及多目标粒子群优化技术来优化RHCM模具电加热系统。与优化前相比,加热系统优化后,模具型腔表面最大温差降低63.4%,加热系统总能耗降低9%。对比了不同注塑成形工艺条件下成形的平板塑件表面质量,结果表明,相对传统注塑成形（CIM）工艺,RHCM工艺将制品表面粗糙度Ra从320 nm降低到118 nm,并有效抑制了制品表面熔接痕、缩痕等缺陷;发现制品表面粗糙度与型腔表面对应点温度成负相关,说明优化后的型腔表面温度分布更有利于提升制品表面质量。     

基于遗传算法的采煤机螺旋滚筒多目标优化设计

为实现采煤机螺旋滚筒截煤和装煤时综合性能最优,基于虚拟样机技术和离散元理论,得到了滚筒各截割性能指标与装煤率随不同结构及运动参数的变化规律,依据机械优化设计理论建立了各性能指标的评价函数。选取螺旋升角、截线距、转速和牵引速度为设计变量,建立了以不同性能指标为分目标的多目标优化模型,利用遗传算法求解得到了最优的结构参数和运动参数。结果表明,利用遗传算法优化后滚筒的最大切削面积增大247mm2,截割比能耗减小0.014kW·h/m3,截割功率减小10.8kW,截割阻力减小7085kN,装煤率提高1.7%,有效地提升了滚筒的综合性能。研究结果为滚筒结构参数和运动参数的选取提供了数据支撑,具有一定的工程应用价值。     

基于遗传算法的散热器翅片优化设计

为了设计工程约束下的高性能散热器,基于散热器热阻网络模型,建立了散热器优化目标函数,利用遗传算法对目标函数进行优化,获取了工程条件约束下散热性能最优的散热器几何参数。计算结果与商用软件仿真结果符合较好,表明文中所建模型的准确性和有效性。文中的研究为提高高性能散热器的设计能力及新产品的研发效率提供了有益参考。     

基于Kriging模型的微夹持器优化设计

为了综合平衡一种新型微夹持器的张合量、夹持力灵敏度与快速响应,提出一种Kriging模型的优化方法。采用拉丁超立方抽样方法确定试验点,采用ANSYS计算各试验点对应的响应值。进行相关性分析以确定对性能影响较大的结构参数,并将其作为优化设计变量。采用Kriging理论建立能反映性能指标与设计变量之间关系的非线性模型,并建立多目标优化模型。比较分析优化前与优化后的各性能指标可知,放大倍数增大了7.4%,固有频率增大了16.46%,输出刚度增大了9.84%,最大应力减小了5.75%,说明所提出的性能优化方法有效。     

Intelligent Optimization Methods for the Design of an Overhead Travelling Crane

In design optimization of crane metal structures, present approaches are based on simple models and mixed variables, which are difficult to use in practice and usually lead to failure of optimized results for rounding variables. Crane metal structure optimal design(CMSOD) belongs to a constrained nonlinear optimization problem with discrete variables. A novel algorithm combining ant colony algorithm with a mutation-based local search(ACAM) is developed and used for a real CMSOD for the first time. In the algorithm model, the encoded mode of continuous array elements is introduced. This not only avoids the need to round optimization design variables during mixed variable optimization, but also facilitates the construction of heuristic information, and the storage and update of the ant colony pheromone. Together with the proposed ACAM, a genetic algorithm(GA) and particle swarm optimization(PSO) are used to optimize the metal structure of a crane. The optimization results show that the convergence speed of ACAM is approximately 20% of that of the GA and around 11% of that of the PSO. The objective function value given by ACAM is 22.23% less than the practical design value, a reduction of 16.42% over the GA and 3.27% over the PSO. The developed ACAM is an effective intelligent method for CMSOD and superior to other methods.     

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.     

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.