电话听筒上盖的塑料注塑模具设计【三维PROE图】【12张CAD图纸+WORD毕业论文】【斜顶抽芯】

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电话听筒盖的塑料模具设计【CAD图纸+WORD毕业论文】【注塑模具类】.rar

毕业设计论文.docx---(点击预览)

开题报告.doc---(点击预览)

中期报告.doc---(点击预览)

3D图

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CAD图

电话听筒盖装配图A0.dwg---(点击预览)

电话听筒盖上壳A4.dwg---(点击预览)

外文翻译

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【温馨提示】购买原稿文件请充值后自助下载。全部文件那张截图中的文件为本资料所有内容，下载后即可获得。预览截图请勿抄袭，原稿文件完整清晰，无水印，可编辑。有疑问可以咨询QQ：414951605或1304139763电话听筒盖的塑料模具设计摘要模具是工业生产中使用极为广泛的主要工艺装备，它是当代工业生产的重要手段和工艺发展方向，许多现代工业的发展和技术水平的提高，在很大程度上取决于模具工业的发展水平。本文介绍了注射模具的特点及发展趋势，叙述了电话机听筒注射模具设计与计算的详细过程，介绍了该塑件成型工艺、注射模具的结构特点与工作过程，阐述了在有斜滑杆抽芯的注射模设计中应注意的事项。采用此模具能够保证塑件尺寸外形以及表面要求，而且成本低、结构简单、开模容易、效率高，具有较强的实用性。关键词：电话听筒；注射模具；抽芯The Plastic Mold Design of Telephone HandsetCoverAbstractMold is widely used in industrial production the main technological equipment, It is an important means of modern industrial production and process development direction ,Many modern industrial development and the improvement of the technical levels ,Largely depends on the development of die and mold industry level.The characteristics and developments of injection mold will be introduced in this paper. The designing and calculating of injection mold of microphone will be stated in detail. The forming process of the product and the structure characteristics as well as working process of the injection mold will be introduced .The attention should be paid in the design of the injection mold for the part with lifters also will be stated.Using this mold can guarantee plastics dimension appearance and surface requirements,And low cost, simple structure and easy to open mold, high efficiency, with strong practicability.Key Words: microphone；injection mold；core pulling目录1 绪论 11.1前言 11.2塑料模具的特点 11.3塑料模具设计的发展概况 21.4课题的研究方向 32 塑件的材料选择及工艺分析 42.1塑件材料选择 42.2塑件工艺性 42.3塑件的结构设计 52.4塑件尺寸及精度 62.5塑件表面粗糙度 72.7塑件的体积和质量 72.8塑件分型面的确定 73 注射成型工艺方案及模具结构的分析和确定 83.1注射成型工艺过程分析 83.2浇口种类的确定 83.3型腔数目的确定 93.4注射机的选择和校核 93.3.1注射量的校核 93.3.2工艺参数校核 103.3.3安装参数校核 104 浇注系统的设计 124.1浇注系统的组成 124.2浇注系统设计原则 124.3流道系统的设计 124.3.1主流道的设计 124.3.2分流道的设计 134.3.3浇口的设计 144.3.4冷料穴的设计 155 成型零部件工作尺寸的计算 165.1成型零部件结构设计 165.2成型零部件的工作尺寸计算 175.2.1型腔径向尺寸计算 175.2.2型腔的深度尺寸 185.2.3型芯的径向尺寸 185.2.4型芯的高度尺寸 185.2.5中心距尺寸计算 185.4模架的选用 195.4.1模具基本类型的确定 195.4.2模架的选择 196 脱模机构的设计 216.1脱模机构的选择 216.2顶杆机构设计 216.3斜滑杆侧抽芯机构设计 227 注射模温度调节系统 237.1温度调节对塑件质量的影响 237.2冷却系统的设计规则 237.3排气结构设计 248 模具的材料 258.1成型零件材料的选用 258.2注射模用钢种 259 模具装配图 2610 模具的可行性分析 2810.1本模具的特点 2810.2市场前景与经济效益分析 2811 结论 29致谢 30参考文献 31毕业设计（论文）知识产权声明 32毕业设计（论文）独创性声明 331 绪论1.1前言随着中国当前的经济形势的日趋好转，在“实现中华民族的伟大复兴”口号的倡引下，中国的制造业也日趋蓬勃发展，而模具技术已成为衡量一个国家制造业水平的重要标志之一，模具工业能促进工业产品生产的发展和质量提高，并能获得极大的经济效益，因而引起了各国的高度重视和赞赏1。在日本，模具被誉为“进入富裕的原动力”，德国则冠之为“金属加工业的帝王”，在罗马尼亚则更为直接“模具就是黄金”，可见模具工业在国民经济中重要地位。我国对模具工业的发展也十分重视，早在1989年3月颁布的关于当前国家产业政策要点的决定中，就把模具技术的发展作为机械行业的首要任务。近年来，塑料模具的产量和水平发展十分迅速，高效率、自动化、大型、长寿命、精密模具在模具产量中所占比例越来越大。注塑成型模具就是将塑料先加在注塑机的加热料筒内，塑料受热熔化后，在注塑机的螺杆或活塞的推动下，经过喷嘴和模具的浇注系统进入模具型腔内，塑料在其中固化成型。本次毕业设计的主要任务是电话听筒盖的注塑模具设计，也就是设计一副注塑模具来生产电话听筒盖的塑件产品，以实现自动化提高产量。针对电话听筒盖的具体结构，通过此次设计，使我对模具设计有了较深的认识。同时，在设计过程中，通过查阅大量资料、手册、标准、期刊等，结合教材上的知识也对注塑模具的组成结构（浇注系统、成

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关键词：: 电话听筒塑料模具设计 cad 图纸 word 毕业论文注塑模具

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电话听筒盖的塑料模具设计
摘要
模具是工业生产中使用极为广泛的主要工艺装备，它是当代工业生产的重要手段和工艺发展方向，许多现代工业的发展和技术水平的提高，在很大程度上取决于模具工业的发展水平。
本文介绍了注射模具的特点及发展趋势，叙述了电话机听筒注射模具设计与计算的详细过程，介绍了该塑件成型工艺、注射模具的结构特点与工作过程，阐述了在有斜滑杆抽芯的注射模设计中应注意的事项。
采用此模具能够保证塑件尺寸外形以及表面要求，而且成本低、结构简单、开模容易、效率高，具有较强的实用性。

关键词：电话听筒；注射模具；抽芯

The Plastic Mold Design of Telephone HandsetCover
Abstract
Mold is widely used in industrial production the main technological equipment, It is an important means of modern industrial production and process development direction ,Many modern industrial development and the improvement of the technical levels ,Largely depends on the development of die and mold industry level.
The characteristics and developments of injection mold will be introduced in this paper. The designing and calculating of injection mold of microphone will be stated in detail. The forming process of the product and the structure characteristics as well as working process of the injection mold will be introduced .The attention should be paid in the design of the injection mold for the part with lifters also will be stated.
Using this mold can guarantee plastics dimension appearance and surface requirements,And low cost, simple structure and easy to open mold, high efficiency, with strong practicability.

Key Words: microphone；injection mold；core pulling

目录
1 绪论 1
1.1前言 1
1.2塑料模具的特点 1
1.3塑料模具设计的发展概况 2
1.4课题的研究方向 3
2 塑件的材料选择及工艺分析 4
2.1塑件材料选择 4
2.2塑件工艺性 4
2.3塑件的结构设计 5
2.4塑件尺寸及精度 6
2.5塑件表面粗糙度 7
2.7塑件的体积和质量 7
2.8塑件分型面的确定 7
3 注射成型工艺方案及模具结构的分析和确定 8
3.1注射成型工艺过程分析 8
3.2浇口种类的确定 8
3.3型腔数目的确定 9
3.4注射机的选择和校核 9
3.3.1注射量的校核 9
3.3.2工艺参数校核 10
3.3.3安装参数校核 10
4 浇注系统的设计 12
4.1浇注系统的组成 12
4.2浇注系统设计原则 12
4.3流道系统的设计 12
4.3.1主流道的设计 12
4.3.2分流道的设计 13
4.3.3浇口的设计 14
4.3.4冷料穴的设计 15
5 成型零部件工作尺寸的计算 16
5.1成型零部件结构设计 16
5.2成型零部件的工作尺寸计算 17
5.2.1型腔径向尺寸计算 17
5.2.2型腔的深度尺寸 18
5.2.3型芯的径向尺寸 18
5.2.4型芯的高度尺寸 18
5.2.5中心距尺寸计算 18
5.4模架的选用 19
5.4.1模具基本类型的确定 19
5.4.2模架的选择 19
6 脱模机构的设计 21
6.1脱模机构的选择 21
6.2顶杆机构设计 21
6.3斜滑杆侧抽芯机构设计 22
7 注射模温度调节系统 23
7.1温度调节对塑件质量的影响 23
7.2冷却系统的设计规则 23
7.3排气结构设计 24
8 模具的材料 25
8.1成型零件材料的选用 25
8.2注射模用钢种 25
9 模具装配图 26
10 模具的可行性分析 28
10.1本模具的特点 28
10.2市场前景与经济效益分析 28
11 结论 29
致谢 30
参考文献 31
毕业设计（论文）知识产权声明 32
毕业设计（论文）独创性声明 33

1 绪论
1.1前言
随着中国当前的经济形势的日趋好转，在“实现中华民族的伟大复兴”口号的倡引下，中国的制造业也日趋蓬勃发展，而模具技术已成为衡量一个国家制造业水平的重要标志之一，模具工业能促进工业产品生产的发展和质量提高，并能获得极大的经济效益，因而引起了各国的高度重视和赞赏[1]。在日本，模具被誉为“进入富裕的原动力”，德国则冠之为“金属加工业的帝王”，在罗马尼亚则更为直接“模具就是黄金”，可见模具工业在国民经济中重要地位。我国对模具工业的发展也十分重视，早在1989年3月颁布的《关于当前国家产业政策要点的决定》中，就把模具技术的发展作为机械行业的首要任务。
近年来，塑料模具的产量和水平发展十分迅速，高效率、自动化、大型、长寿命、精密模具在模具产量中所占比例越来越大。注塑成型模具就是将塑料先加在注塑机的加热料筒内，塑料受热熔化后，在注塑机的螺杆或活塞的推动下，经过喷嘴和模具的浇注系统进入模具型腔内，塑料在其中固化成型。
本次毕业设计的主要任务是电话听筒盖的注塑模具设计，也就是设计一副注塑模具来生产电话听筒盖的塑件产品，以实现自动化提高产量。针对电话听筒盖的具体结构，通过此次设计，使我对模具设计有了较深的认识。同时，在设计过程中，通过查阅大量资料、手册、标准、期刊等，结合教材上的知识也对注塑模具的组成结构（浇注系统、成

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内容简介：: ?Journal of Mechanical Science and Technology 21 (2007) 789798 Journal of MechanicalScience andTechnologyMicro Genetic Algorithm Based Optimal Gate Positioning in Injection Molding Design Jongsoo Lee*, Jonghun Kim School of Mechanical EngineeringYonsei University, Seoul 120-749 Korea (Manuscript Received December 12, 2006; Revised March 26, 2007; Accepted March 26, 2007) - Abstract 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.?Keywords: Micro genetic algorithm; Design optimization; Filling injection mold- 1. Introduction Injection molding process has been recognized as one of the most efficient manufacturing technologies since high performance polymer materials can be utilized to accurately manufacture a product with complicated shape (Chiang, et al., 1991; Chang and Yang, 2001; Himasekhar, et al., 1992; Kwon and Park, 2004). Also, the demand on injection molded products such as from conventional plastic goods to micro optical devices is being dramatically increased over the recent years (Piotter, et al., 2001; Kang, et al., 2000). In general, the injection mold process is initiated by the filling stage where the polymer materials fill into a cavity under the injection temperature. After the cavity is completely filled, the post-filling stage, that is, the packing stage is conducted to be additionally filled with the high pressure polymer, thereby resulting in the avoidance of material shrinkage. Subsequently, the cooling stage is required for a molded product to be ejected without any deformation. It is important to accommodate the molding conditions in the filling stage since it is the first stage in the overall injection molding design (Zhou and D. Li, 2001). After that, one can success-fully expect more improved molding conditions during post-filling stages such as packing, cooling stages. The paper deals with optimal conditions of the filling injection molding design in which the flow pattern and pressure for the polymer materials to be filled through gates of a runner are of significant. That is, one of design requirements are such that when the polymer comes into a cavity through a number of gates located at different positions, pressure levels on the surface of a product should be as uniform as possible. Such design can be performed through the intelligent gate positioning to generate the more *Corresponding author. Tel.: +82 2 2123 4474; Fax.: +82 2 362 2736 E-mail address: jleejyonsei.ac.kr 790 Jongsoo Lee and Jonghun Kim / Journal of Mechanical Science and Technology 21(2007) 740749 uniform distribution of injection pressure over the product surface. There have been a number of studies of optimal gate location in the context of CAE filling injection molding design problems where various kinds of optimizer have been employed to conduct design optimization (Kim et al., 1996; Young, 1994; Pan-delidis and Zou, 2004; Lin, 2001; Li and Shen, 1995). The paper explores the design of injection mold system using micro genetic algorithm (mGA). Ge-netic algorithm (conventional GA) is based on the Darwins theory of the survival of the fittest, and adopts the concept of natural evolution; the competitive designs with more fit are survived by selection, and the new designs are created by crossover and mutation (Lee, 1996; Lee and Hajela, 1996). A conventional GA works with a multiple number of designs in a population. Handling with such designs results in increasing a higher probability of locating a global optimum as well as multiple local optima. GA is also advantageous when the design problem is represented by a mixture of integer/dis-crete and continuous design variables. Nevertheless, it requires expensive computational costs especially when combining with finite element based CAE analysis tools. A conventional GA determines the population size depending upon the stringlength of a chromosome that is a coded value of a set of design variables. The main difference between a conven-tional GA and mGA resides on the population size. The population size in mGA is based on Goldbergs concept such that Evolution process is possible with small populations to reduce the cost of fitness function evaluation (Goldberg, 1988). This implies that mGA employs a few number of populations for GA evolution regardless of the number of design variables and the complexity of design parameters (Krishnakumar, 1989; Dennis and Dulikravich, 2001). The paper discusses the design requirements of filling injection mold optimization to construct the proper objective functions and design constraints. 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. 2. Mold flow analysis The flow of a polymer in injection molding process obeys the following governing equations: 22()()0ppSSxxyy? (1) 222()pxyTTTTCktxyz? (2) where, 220hzSdz?.In the above equations, p is a flow pressure, T is a temperature of polymer, and t is denoted as time. Parameters ?, ?, and k are viscosity, shear rate and thermal conductivity, respectively (Lee, 2003). It is assumed that polymer is a non-compaction substance in the filling analysis. The flow analysis in the present study is conducted by Computer Aided Plastics Application (CAPA) (Koo, 2003), a finite element based commercial code for polymer flow of injection molding. The runner system in injection mold covers the passage of molten polymer from injection port to gates. The present study develops two different runner systems where a cold system requires the change in polymer temperature, and a hot system keep it unchanged while the flow passes through the runner. For the hot runner system has a geometrically consistent thickness due to the constant temperature as shown in Fig. 1a. However, the CAE result of a cold runner system depends on the thickness and shape Table 1. Ten-bar truss design results. micro GA conventional GA Case 1 Case 2 Case 3 Case 1 Case 2 Case 3 Reference20 X17.868.157.858.15 7.30 7.81 7.90 X20.40 0.83 0.45 0.10 X38.387.998.158.20 8.77 8.37 8.10 X45.053.833.893.97 3.27 4.16 3.90 X50.120.960.151.10 0.75 0.55 0.10 X60.40 0.82 0.30 0.10 X76.415.675.875.84 6.74 6.30 5.80 X85.236.295.525.68 5.06 5.26 5.51 X93.833.855.055.07 2.89 3.86 3.68 Optimal areaX100.500 1.16 0.42 0.14 Optimal weight 1599158715881593 1590 1585 1499 # of function evaluations57540 54230 25335 78894 69497 73533 Jongsoo Lee and Jonghun Kim / Journal of Mechanical Science and Technology 21(2007) 789798 791 (a) Hot runner system (b) Cold runner system Fig. 1. Modeling of runner system. shape of a runner. The typical illustration of the geometric model in a cold runner system is shown in Fig.1b where the runner thickness is changed according to the temperature gradient. 3. Molding design requirements 3.1 Objective functions One of the most significant factors considered in the injection molding design is a flow pattern, which implies that a balanced flow should be maintained while a polymer arrives at each part of a design product. Once the improvement on flow balance is obtained, the flow of molten polymer smoothes and the maximum injection pressure is decreased with the same or at least evenly distributed injection pressure level at each gate. In a case where the certain part of a product within the mold is filled up earlier than other parts, each part would fall into over-packing and under-packing situations during the filling process of a polymer into mold. Such problem further evokes a malformation like twisting and bending, resulting from the difference in contraction rate during the course of cooling-off. The difference in pressure triggers the flow of polymer during the filling process, in which the maximum injection pressure is detected at the injection port of polymer. The polymer always flows from high-pressure region to low-pressure one. When a flow pattern improves, the flow of polymer gets smoother with the maximum injection pressure decreased. However, the flow instability sometimes happens, thereby requiring a higher pressure to fill up. That is, the maximum injection pressure needs to be reduced in order to improve the flow instability. The pressure gap (i.e., the highest and lowest pressure values) among all of gates is also taken as another objective function to determine whether the whole mold is being filled at once. Most commonly accepted design strategy to improve the flow pattern is the adjustment of gate location. The present study controls the flow pattern by developing the optimal gate positioning problems with proper objective function(s) and design cons-traints. Objective functions for injection molding design are considered as both maximum injection pressure (MIP) and maximum pressure difference (MPD). It should be noted that the maximum injection pressure is calculated at the injection port and the maximum pressure difference is a numerical difference between the highest and lowest values of pressure among all of gates. The aforementioned statements could be interpreted as a multiobjective design problem, hence the present study simply employs a weighting method as follows: *( )( )( )MIP xMPD xF xMIPMPD? (3) where, ? and ? are weighting factors as ?+?=1, and x is a set of design variables which are Cartesian coordinates of gates on a product. Each component in the above equation is normalized by optimal single-objective function value, (i.e., MIP*, MPD*). It is mentioned that the number of gates is considered as a problem parameter in the study. 3.1 Constraints Weld-lines are easily detected when more than two flow fronts having different temperature values meet during the filling process. The weld-line is one of the weakest points in molded product; it is very 792 Jongsoo Lee and Jonghun Kim / Journal of Mechanical Science and Technology 21(2007) 740749 vulnerable to a shock and subsequently causes external defects of a very glossy polymer. The weld-line should be moved into a less weak region by adjusting the width of a product, the size and/or shape of gates and runners, and the position of gates, etc. The present study considers the position of a weld-line as a constraint in optimal gate positioning of mold design. Once a designer specifies areas where weld-lines should not be generated, all of the finite element nodes in such areas are constrained not to form the weld-lines. Shear stress is defined as a shear force imposed on the wall of a mold by the shear flow of a polymer. The magnitude of shear stress is proportional to the pressure gradient of each position. In general, the shear stress is zero at the center of a molded product, and reaches a maximum value on the wall. High shear stress triggers the molecule cultivation on the surface of a molded product. Flow instability such as melt fracture has a close relationship with the shear stress. The clear surface of a molded product can be obtained by reducing the magnitude of shear stress. That is, shear stress should be minimized during the mold filling process in order to improve the quality of a molded product, particularly on its surface. Maximum allowable shear stress depends on the kinds of polymer, and is generally taken as 1% of tensile strength of a polymer. Shear stress affecting the quality of end product is considered as another constraint. 3.3 Formulation of optimization problem The statement of a mold design optimization problem can be written as follows: Find 12( , , ) ( , , ),( , , ),.,( , , )Nx i j kx i j kxi j kxi j k? (4) to minimize *( )( )( )MIP xMPD xF xMIPMPD? (5) subject to shear stress(i, j, k) shear stress allowable (6) weld-line(i, j, k) = designated area(s) only (7) where, lowerupperxxx? A set of design variables, x are Cartesian coordi-nates (i, j, k) of gates on the surface of a molded product, where N is the number of gates. A traditional weighted-sum method in the context of multiob-jective optimization is employed by using two wei- Fig. 2. Micro GA process. ghting factors of ? and ?, where ?+?=1. Multi-objective functions considered in the present study are maximum injection pressure (MIP) measured at the injection port and maximum pressure difference (PD) among all of gates. The constants, MIP* and MPD* are optimal objective function values obtained via single-objective optimization. The permission of weld-lines to designated areas only and the upper limits on shear stress are imposed as design cons-traints. The flow pattern analysis is performed by CAPA as mentioned in the earlier section, and the optimization is conducted through mGA. It should be noted that Cartesian coordinates (i, j, k) is recognized as nodal points when a molded product is discretized by finite elements in CAPA. 4. Micro GA The overall process of mGA in the present study is depicted in Fig. 2, and a stepwise procedure can be explained as follows: Step-1) Generate an initial population at random. The recommended population size is 3, 5, or 7. Step-2) Perform a conventional GA evolution until the nominal convergence is satisfied. In the present study, the population size is selected as 5, and a tournament selection operator is used. The crossover probability in mGA is 1.0 due to the small size in population, while a conventional GA is preferred to use it less than 1.0. The nominal convergence means that the difference of 1s and/or 0s among string positions is within 5% out of the stringlength, thereby resulting in the convergence to a local solution. Step-3) During the user-specified number of ge-nerations, a new population is updated; one individual is selected by elitism; the remaining individuals in a Jongsoo Lee and Jonghun Kim / Journal of Mechanical Science and Technology 21(2007) 789798 793 new population are generated at random. It should be noted that the selection operation adopts tournament for activating the diversity and elitism for keeping the best solution. Since the updated populations except for the elitism are generated at random, mGA seldom considers the mutation. ?N_functionObjective (a) A conventional GA ?N_functionObjective (b) Micro GA Fig. 3. Convergence histories of ten-bar truss problem. ?Fig. 4. Seven discrete design spaces for vehicle dashboard problem. Fig. 5. Initial gate location of vehicle dashboard. In summary, mGA enables to locate an optimal solution thanks to the small size of populations, tournament and elitism operations in selection, and the full participation in crossover. However, mGA has a drawback upon finding one of multiple local optima only due to the small size of populations and the nominal convergence strategy. A conventional GA is superior to maintaining the diversity while mGA is advantageous of savings in computational resource requirements. 4.1 Truss design The proposed mGA is verified by a typical ten-bar planar truss optimization problem. The objective is to find optimal cross-sectional areas by minimizing the structural weight subjected to stress constraints (Haftka and Gurdal, 1993). Optimal solutions are obtained via mGA and a conventional GA to compare with each other. The population size in mGA is 5, while a conventional GA requires 250 individuals in a population since the stringlength in this problem is 100. Crossover and mutation probabilities in a con-ventional GA used are 0.8 and 0.01, respectively. After two genetic search methods are conducted ten times by changing randomly generated initial popul-ations, the most fit design results are demonstrated in Table 1. The convergence history for each optimizer demonstrates that mGA produces the better design and locates the near-optimal solution at the earlier stage of evolution in Fig. 3. 5. Results of design applications 5.1 Vehicle dashboard A passenger car in-panel has been first examined. This model is supposed to have 7 gates, and design spaces for use in genetic evolution are shown in Fig. 4. Objective functions of MIP and MPD are taken into account, but no constraints are imposed in this model. The initial design is shown in Fig. 5; this design has been obtained through experience and trial-and-errors in an automotive part molding company. Optimized results by mGA are shown in Figs. 6 to 8, whose objective functions were considered as MIP only, MPD only and both MIP and MPD, respectively. Design results for each case are summarized in Table 2 as well. It is noted that both MIP and MPD is calculated with ? changing from 0.0 to 1.0 with an increment of 0.1 while keeping ?.0. 794 Jongsoo Lee and Jonghun Kim / Journal of Mechanical Science and Technology 21(2007) 740749 Fig. 6. Optimized design of vehicle dashboard (MIP only). Fig. 7. Optimized design of vehicle dashboard (MPD only). Fig. 8. Optimized design of vehicle dashboard (both MIP and MPD). In case of MIP only in Fig. 6, the maximum injection pressure value has an improvement of 23.9% compared with an initial model, but the pressure distribution on the product becomes worse, resulting in over-packing on the left region. When a case of MPD only is considered, the design performance in Fig. 7 is achieved in terms of not only maximum pressure difference but also maximum injection pressure as shown. It is expected that the flow gets smoother during the improvement of pressure distribution, and the maximum injection pressure is decreased as well. In case of both MIP and MPD in Fig. 8, its result is quite similar to a case Table 2. Optimization results of vehicle dashboard. maximum pressure MPa maximum difference MPaInitial design 242.69 20.26 MIP only184.73 35.08 MPD only231.22 12.44 objectiveboth MIP and MPD229.92 12.58 Table 3. Optimization results of TV monitor. maximum pressure MPa maximum difference MPa shear stress 0.5 MPaInitial design 80.55 13.71 0.45 MIP only68.46 4.06 0.43 MPD only72.27 3.04 0.45 objectiveboth MIP and MPD68.46 4.06 0.43 of MPD only in terms of gate locations from Figs. 7 and 8 and the percentile improvement in Table 2. A weighted-sum method is used to obtain the mul-tiobjective optimal solutions by changing ? and ?simultaneously, but yields the same results out of a total of 11 weighting factor based trials. The reason why a few number of Pareto solutions are detected is such that the maximum pressure is not counter to pressure distribution in the filling injection molding. In other words, when the overall pressure distribution is improved thanks to the enhancement of flow balance and the smoothness of polymer flow, the maximum pressure is consequently decreased. As far as the pressure distribution of a modeled product is concerned, the change in gate position is noticeable; Gate_5 of optimized models moves from right to left region compared with an initial model. 5.2 TV monitor The model of a TV monitor equipped with 4 gates is now optimized using objective functions and the upper limit on shear stress constraint, where the shear stress allowable is 0.5MPa. The initial design with 4 discrete design spaces is displayed in Fig. 9, and optimized pressure distributions are shown in Figs. 10 and 11. Design results for single-objective and mul-tiobjective optimization are tabulated in Table 3. In case of MIP only generates the same result as weighting method based multiobjective solutions of both MIP and MPD. In case of MPD only, the maxi- Jongsoo Lee and Jonghun Kim / Journal of Mechanical Science and Technology 21(2007) 789798 795 Fig. 9. Initial gate location of TV monitor. Fig. 10. Optimized design of TV monitor (MPD only). Fig. 11. Optimized design of TV monitor (MIP only & both MIP and MPD). mum injection pressure and maximum pressure di-fference have been improved by 10.3% and 77.8%, respectively. It is expected that the enhancement on flow balance and smoothness may be made possible by optimizing the gate positions. 5.3 CD tray The CD tray use in a laptop computer has 4 gates for injection molding. The optimization on this model Fig. 12. CD tray (left) and its initial gate location (right). Fig. 13. Optimized design of CD tray (MIP only). Fig. 14. Optimized design of CD tray (MPD only). is conducted with a shear stress constraint, where the upper limit on shear stress allowable is 1.5MPa. Initial and optimized results for pressure distribution are shown in Figs. 12 to 15. From the summary of Table 4, the design solutions of optimal objective fun- 796 Jongsoo Lee and Jonghun Kim / Journal of Mechanical Science and Technology 21(2007) 740749 Fig. 15. Optimized design of CD tray (both MIP and MPD). Table 4. Optimization results of CD tray. maximum pressure MPa maximum difference MPa shear stress 1.5 MPaInitial design 82.66 1.192 1.22 MIP only 73.91 7.085 1.26 MPD only 80.44 0.332 1.12 objective both MIP and MPD 78.79 0.376 1.14 ction values in this problem are quite similar to that in the vehicle dashboard. In case of MIP only, the maximum pressure difference value gets worse than the initial design, even though the maximum injection pressure value has been improved. The cases of MPD only and both MIP and MPD have turned out that both objective function values are improved. Also, the duplicated multiobjective design solutions are much close to the result obtained by MPD only, as in the vehicle dashboard design. 5. 4 Plug receptacle This problem employs the weld-line condition as a constraint instead of shear stress. In Fig. 16, the design space for optimally locating 2 gates is re-presented by a dotted region, and the restricted areas against weld-lines are designated by 5 solid regions. Actual mold designers do not locate the weld-line restriction just like this problem. Side or rear parts of a product might be preferred. However, this problem places the disjointed 5 weld-line restriction areas in the front to see how much the proposed design strategy of mGA works in the present study. Opti-mized results of weld-line distribution are shown in Figs. 17 and 18. It is clear to see that all the results are ? Fig. 16. Design space (dotted area) and weld-line restriction region (solid areas) of plug receptacle. Fig. 17. Weld-line in optimized design of plug receptacle (MPD only). Fig. 18. Weld-line in optimized design of plug receptacle (MIP only & both MIP and MPD). Jongsoo Lee and Jonghun Kim / Journal of Mechanical Science and Technology 21(2007) 789798 797Table 5. Optimization results of plug receptacle. max pressure MPa max difference MPa MIP only 160.30 0.51 MPD only 166.47 0.05 objectiveboth MIP and MPD 160.30 0.51 satisfied with weld-line constraint. The design solu-tions for optimal objective function values are also similar to those of TV monitor. The solutions of MIP only and weighting method based both MIP and MPD are the same (see Table 5). 6. Concluding remarks The paper examines micro genetic algorithm in the context of engineering design optimization. Micro genetic algorithm is efficient in handling with small populations over a conventional genetic algorithm. The proposed optimization algorithm is applied to filling injection mold design problem. The central of the paper 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. Multiobjective design solutions show that the enhancement on flow balance and smoothness may be made possible by optimizing the gate positions. The use of optimized runner systems would subsequently expect to reduce defects such as deformation and twisting that are to be generated during the cooling process. Acknowledgments Authors greatly appreciate for the partial support from iDOT, Center of Innovative Design Optimi-zation Technology. References Chang R. Y. and Yang, W. H., 2001, “Numerical Simulation of Mold Filling in Injection Molding Using a Three-Dimensional Finite Volume Approach,” Inter-national Journal for Numerical Methods in Fluids, Vol. 37, Issue 2, pp. 125148. Chiang, H. H., Hieber C. A., and Wang, K. K., 1991, “A Unified Simulation of the Filling and Postfilling Stages in Injection Molding, I: Formulation,” Polymer Engi-neering and Science, Vol. 31, No. 2, pp. 116124. Dennis, B. H. and Dulikravich, G. S., 2001, “Optimization of Magneto-Hydrodynamic Control of Diffuser Flows Using Micro-Genetic Algorithms and Least-Squares Finite Elements,” Finite Elements in Analysis and Design, Vol. 37, No. 5, pp. 349363. Goldberg, D. E., 1988, “Sizing Populations for Serial and Parallel Genetic Algorithms,” TCGA Report No. 88004, The Clearinghouse for Genetic Algorithms, University of Alabama. Haftka R. T. and Gurdal, Z., 1993, “El

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