Warpage phenomenon of thin-wall injection molding

This study investigates warpage of electronic dictionary battery covers fabricated using thin-wall injection molding as a replacement for conventional insert molding. The primary concern is the molding window in thin-wall injection molding for acrylonitrile?Cbutadiene styrene (ABS) and polycarbonate (PC)+ABS plastics. Finally, the process parameters for thin-wall injection molding that eliminate warpage of electronic dictionary battery covers are identified. Experimental results demonstrate that the area of the molding window for ABS exceeds that for PC+ABS. Analysis of the molding window reveals that ABS is more appropriate than PC+ABS for battery covers. Low melt temperature, high injection speed, and high packing pressure eliminate battery cover warpage. Melt temperature is the most important process parameter for eliminating warpage when using both ABS and PC+ABS.     

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.     

基于类神经网络注塑成型翘曲和收缩值之预测

以熔融温度、模具温度、射出时间、保压压力、保压时间等5个制程参数作为控制因子。利用Moldflow来模拟塑料薄壳挡板不同的成型制程参数下的翘曲与收缩值。基于仿真所得翘曲及收缩值数据,使用田口方法结合倒传递神经网络5-14-14-2建立预测模型。再利用测试样本来验证的倒传递神经网络模型的准确性。运用所建立的倒传递神经网络模型预测其他成型制程参数的翘曲及收缩值。结果证明,田口法结合倒传递神经网络,不仅可以有效的优化倒传递神经网络,而能成功的预测翘曲及收缩值,与Moldflow仿真值相比平均误差都在±1%内。     

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.     

大型薄壁塑件翘曲变形分析及工艺参数优化

在注塑成型大型薄壁塑料产品过程中,由于熔料流动路程长,流动阻力大,产品极易产生翘曲变形,从而影响到使用性能.应用Moldflow软件分析了影响产品翘曲变形的主要原因是收缩率不均引起.通过优化工艺参数,即优化保压曲线,减小了产品翘曲变形.方珐是将保压设置由恒压保压调整为先恒压再线性递减的两段保压,然后再多次调整保压曲线各...     

薄壁注塑件保压工艺的CAE分析及优化

保压阶段是注塑成型工艺的重要环节,保压工艺设置不恰当就会引起模腔中的压力分布不均匀,引起制件的翘曲变形、尺寸精度下降等严重的质量问题。介绍了薄壁注塑成型的定义,分析了保压工艺对薄壁制件成型的影响以及常见的保压方式对模腔压力分布的影响,利用Moldflow软件进行数值模拟,调整保压曲线,均衡模腔中的压力分布,并进行了注塑实验验证,结果表明：保压工艺对注塑件的翘曲变形有着显著的影响,与恒定保压相比,先恒压后线性递减的保压方式可获得较均匀的模腔压力分布,制件的体积收缩较均匀,制件的成型质量较好。     

<Emphasis Type="Bold">Cumulative Heat Effect in Excimer Laser Ablation of Polymer PC and ABS</Emphasis>

Analysis of the thermal effect and machining properties of PC (polycarbonate) and ABS (polyacrylonitrilebutadienestyrene) polymers when ablated using a KrF excimer laser is described. PC has less thermal effect on the geometric distortion than ABS in laser ablation. The cumulative heat during laser ablation results in geometric deformation in ABS micromachining. The number of laser pulses generates a greater geometric deformation in ABS than in PC according to experimental laser ablation observations. The PC ablation rate is proportional to the laser fluence, whereas ABS shows an exponential profile. The pulse repetition rate has no significant influence on PC during laser ablation, but affects the ablated patterns in ABS. During laser ablation, PC does not produce debris on the machining patterns, but debris is produced on the machining patterns with ABS.     

Mold design and forming process parameters optimization for passenger vehicle front wing plate

The main objective of the present article is to solve the problems of poor molding quality, large warpage, inadequate cooling effect and unsuitable selection of process parameters, in the injection molding process for passenger vehicle front-end plastic wing plate. The thickness and parting surface of the vehicle front-end fender were determined, the injection mold and its cooling system were designed. The relevant process parameters, affecting the product molding quality, were tested, according to orthogonal experimental approach, while their influence on the warpage was obtained, by analyzing the data. Finally, the BP neural network of warpage model was established and globally optimized using genetic algorithm. The optimal parameter combination of the injection molding process was derived as: melt temperature 236 °C, mold temperature 51 °C, cooling time 32 s, packing pressure 97 MPa and packing time 16 s.

     

Experimental-based optimization of polymer injection molding process parameters using anfis-ga method

Compared with ordinary injection-molded parts, the slender, cantilevered, and thin-walled plastic parts are harsh on the injection molding process conditions. For complexity and particularity, it is difficult to form such parts. It is also more likely to cause excessive warpage deformation, affecting the molding quality and performance. The automobile audio shell is a typical slender, cantilevered, thin-walled plastic part. When the mold structure and material are determined, optimizing its injection molding process is the most economical and effective method to manufacture the products with the optimum properties. In order to minimize the warpage deformation, the adaptive network based fuzzy inference system (ANFIS) and genetic algorithm (GA) were adopted to optimize the injection molding process parameters. In particular, considering the high-dimensional nonlinear relationship between the process parameters and the warpage, the ANFIS is constructed as the prediction model of the warpage. Then, the GA is used to globally optimize the prediction model to determine the optimal process parameters. The results show that the optimization method based on ANFIS-GA has a good performance. The warpage is reduced to 0.0925 mm while reduced by 88.25 %. The optimal injection molding process parameters are used for simulation and manufacture, verifying the effectiveness and reliability of the optimization method.

     

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.     

Optimization of machining parameters in the abrasive waterjet turning of alumina ceramic based on the response surface methodology

A study on the radial-mode abrasive waterjet turning (AWJT) of 96 % alumina ceramic is presented and discussed. An experimental investigation is carried out to explore the influence of process parameters (including water pressure, jet feed speed, abrasive mass flow rate, surface speed, and nozzle tilted angle) on the material removal rate (MRR) when turning 96 % alumina ceramic. The experiments are conducted on the basis of response surface methodology (RSM) and sequential approach using face-centered central composite design. The quadratic model of RSM associated with the sequential approximation optimization (SAO) method is used to find optimum values of process parameters in terms of surface roughness and MRR. The results show that the MRR is influenced principally by the water pressure P and the next is abrasive mass flow rate m _a . The optimization results show that the MRR can be improved without increasing the surface roughness when machining 96 % alumina ceramic in the radial-mode abrasive waterjet turning process.     

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.     

A universal slip-line model with non-unique solutions for machining with curled chip formation and a restricted contact tool

A universal slip-line model and the corresponding hodograph for two-dimensional machining which can account for chip curl and chip back-flow when machining with a restricted contact tool are presented in this paper. Six major slip-line models previously developed for machining are briefly reviewed. It is shown that all the six models are special cases of the universal slip-line model presented in this paper. Dewhurst and Collins's matrix technique for numerically solving slip-line problems is employed in the mathematical modeling of the universal slip-line field. A key equation is given to determine the shape of the initial slip-line. A non-unique solution for machining processes when using restricted contact tools is obtained. The influence of four major input parameters, i.e. (a) hydrostatic pressure (P_A) at a point on the intersection line of the shear plane and the work surface to be machined; (b) ratio of the frictional shear stress on the tool rake face to the material shear yield stress (τ/k); (c) ratio of the undeformed chip thickness to the length of the tool land (t₁/h); and (d) tool primary rake angle (γ₁), upon five major output parameters, i.e. (a) four slip-line field angles (θ, η₁, η₂, ψ); (b) non-dimensionalized cutting forces (F_c/kt₁w and F_t/kt₁w); (c) chip thickness (t₂); (d) chip up-curl radius (R_u); and (e) chip back-flow angle (η_b), is theoretically established. The issue of the “built-up-edge” produced under certain conditions in machining processes is also studied. It is hoped that the research work of this paper will help in the understanding of the nature and the basic characteristics of machining processes.     

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.

     

Optimization and Simulation of Plastic Injection Process using Genetic Algorithm and Moldflow

The use of plastic-based products is continuously increasing. The increasing demands for thinner products, lower production costs, yet higher product quality has triggered an increase in the number of research projects on plastic molding processes. An important branch of such research is focused on mold cooling system. Conventional cooling systems are most widely used because they are easy to make by using conventional machining processes.However, the non-uniform cooling processes are considered as one of their weaknesses. Apart from the conventional systems, there are also conformal cooling systems that are designed for faster and more uniform plastic mold cooling. In this study, the conformal cooling system is applied for the production of bowl-shaped product made of PP AZ564. Optimization is conducted to initiate machine setup parameters, namely, the melting temperature, injection pressure, holding pressure and holding time. The genetic algorithm method and Moldflow were used to optimize the injection process parameters at a minimum cycle time. It is found that, an optimum injection molding processes could be obtained by setting the parameters to the following values: T_M= 180 °C; P_(inj)= 20 MPa;P_(hold)= 16 MPa and t_(hold)= 8 s, with a cycle time of 14.11 s. Experiments using the conformal cooling system yielded an average cycle time of 14.19 s. The studied conformal cooling system yielded a volumetric shrinkage of 5.61% and the wall shear stress was found at 0.17 MPa.The difference between the cycle time obtained through simulations and experiments using the conformal cooling system was insignificant(below 1%). Thus, combining process parameters optimization and simulations by using genetic algorithm method with Moldflow can be considered as valid.     

基于Moldflow的薄壁件翘曲变形研究

翘曲变形是注塑件的主要缺陷,利用电器后盖对薄壁成型工艺进行研究。采用Moldflow软件对塑件成型过程进行数值模拟,研究了保压压力、塑件材料对注塑件翘曲变形的影响。对薄壁注塑件的数值仿真模拟结果进行统计分析,并且对影响注塑翘曲变形量的工艺参数进行综合分析,得到最优的工艺参数组合。研究结果表明:最佳的工艺参数组合可以使得塑件翘曲量变得最小。     

Multi-objective optimization of injection molding process parameters in two stages for multiple quality characteristics and energy efficiency using Taguchi method and NSGA-II

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.     

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。     

Analysis of force patterns and tool life in milling operations

The paper describes the practical effects of the operating parameters in the milling operation. Experiments have been conducted to measure cutting force and tool life under dry conditions. Based on the experimental results, three mathematical models have been developed: Force, TLife and Force/TLife. Further analyses have been conducted on the cutting force patterns: seasonal pattern and nonlinear trend. A process optimisation that is based on the minimum production cost has been applied to relate Force model, TLife model and machinability criteria, such as power consumption, cutting parameters and surface roughness.Nomenclature C _w cost of workpiece ($) - C _s set-up cost ($) - C _m machining cost ($) - C _o overhead cost ($) - C _r tool replacement cost ($) - C _t tool cost ($) - D diameter of the cutter (inch) - d depth of cut per pass (inch) - d ₀ required depth (inch) - e _t random error attth sample - F cutting force (N) - f feedrate (ipm) - L length of workpiece (inch) - N spindle speed (r.p.m.) - n number of teeth - P power of the motor (h.p.) - R surface roughness (µm) - R _e real part of a complex function - T tool life (min) - t sample number - t _m machining time (s) - t ₀ overhead time (s) - t _r tool replacement time (s) - t _s set-up time (s) - U _i unit cost of itemi ($/unit)v - v cutting speed (i.p.m.)     

Analysis and modeling of effective parameters for dimension shrinkage variation of injection molded part with thin shell feature using response surface methodology

The injection molded housing part with thin shell feature could be produced to increase the internal space for packing more components. In this study, injection velocity, packing pressure, mold temperature, and melt temperature were selected as effective parameters for injection molding process. For the purpose of reducing dimension shrinkage variation of thin shell molded part, the response surface methodology was utilized to determine the relationship between input parameters and responses. Then the optimization condition was obtained according to the desirability function. Results show that melt temperature is the most significant factor on dimension shrinkage variation in transverse direction, followed by packing pressure, mold temperature, and injection velocity. However, in the longitudinal direction, packing pressure has the greatest influence on the dimension shrinkage variation, followed by injection velocity, melt temperature, and mold temperature. In accordance with verification experiments, the difference between the experimental data and predicted values ranges from ?9.8% to 1.8%. To obtain the optimal condition, the overall desirability must be larger than 0.9. Based on analysis of variance, the proposed models look reasonably accurate.