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畢業(yè)設(shè)計(jì) 論文 中期報告 題目 玩具照相機(jī)零件模具設(shè)計(jì) 系 部 機(jī)電信息系 專 業(yè) 機(jī)械設(shè)計(jì)制造及其自動化 班 級 學(xué) 生 學(xué) 號 指導(dǎo)教師 2013 年 3 月 18 日 1 設(shè)計(jì) 論文 進(jìn)展?fàn)顩r 1 1 完成與課題相關(guān)的英文資料的翻譯 1 2 分析了玩具照相機(jī)零件的結(jié)構(gòu) 測繪了零件的 CAD 和 Pro E 圖 1 3 確定零件的分形面和模具的側(cè)抽芯機(jī)構(gòu) 脫模機(jī)構(gòu) 澆注系統(tǒng) 繪制了 玩具照相機(jī)零件支架的模具裝配示意圖 1 4 進(jìn)行了側(cè)抽芯機(jī)構(gòu) 導(dǎo)向機(jī)構(gòu) 頂出機(jī)構(gòu) 定距分型機(jī)構(gòu) 澆注系統(tǒng) 模板厚度 型腔尺寸相關(guān)計(jì)算 2 存在問題及解決措施 問題 1 進(jìn)料口形式的設(shè)計(jì) 解決措施 自己查閱相關(guān)的設(shè)計(jì)手冊 書籍 電子資料 詢問老師澆 口采用個點(diǎn)澆口 2 導(dǎo)軌結(jié)構(gòu)設(shè)計(jì)和它的調(diào)隙方式 解決措施 自己查閱相關(guān)的設(shè)計(jì)手冊 書籍 電子資料 詢問老師采 用活動滑塊用螺釘和銷釘連接和定位方便調(diào)節(jié)滑動間隙 3 頂出距離的確定 解決措施 自己查閱相關(guān)的設(shè)計(jì)手冊 書籍 電子資料 詢問老師頂 出距離的概念 是指從分型面到塑件最底端的距離而并非塑件的整個高度 4 冷卻水道的設(shè)計(jì) 解決措施 自己查閱相關(guān)的設(shè)計(jì)手冊 書籍 電子資料 詢問老師和 同學(xué)在模板上打冷卻液孔道然后根據(jù)需要在孔道上裝密封螺釘改變冷卻液的流向 5 三視圖的設(shè)計(jì) 解決措施 自己查閱相關(guān)的設(shè)計(jì)手冊 書籍 電子資料 詢問老師主 視圖要盡可能剖出塑件的最大面并剖出澆注系統(tǒng)的位置及側(cè)抽芯機(jī)構(gòu) 左視圖要 表達(dá)出定距分型機(jī)構(gòu) 導(dǎo)向機(jī)構(gòu)及頂出機(jī)構(gòu) 俯視圖主要表達(dá)出型腔位置 壁厚 及塑件在型腔中的位置 3 后期工作安排 3 1 繼續(xù)進(jìn)行相關(guān)計(jì)算 根據(jù)計(jì)算結(jié)果校核注塑機(jī)和模具的結(jié)構(gòu)尺寸 安裝尺 寸 調(diào)整相關(guān)結(jié)構(gòu)和尺寸確定最終裝配圖和模具零件結(jié)構(gòu)和尺寸 第十一 周 3 2 繪制模具裝配圖 零件圖 第十二 周 3 3 對模具典型零件的選材及熱處理工藝路線分析 對于設(shè)計(jì)中典型模具零件 編制零件制造工藝規(guī)程卡片 第十三周 3 4 對設(shè)計(jì)方案和設(shè)計(jì)結(jié)果要進(jìn)行經(jīng)濟(jì)分析和環(huán)保分析 編寫設(shè)計(jì)論文 15000 字以上 第十四到十五周 3 5 將論文 圖紙交老師查閱 第十六周 3 6 準(zhǔn)備終期答辯 第十八周 指導(dǎo)教師簽字 年 月 日 五 所在系審查意見 系主管領(lǐng)導(dǎo) 年 月 日 International Journal of Automotive Technology Vol 13 No 2 pp 273 277 2012 DOI 10 1007 s12239 012 0024 5 Copyright 2012 KSAE 063 11 pISSN 1229 9138 eISSN 1976 3832 273 DESIGN OPTIMIZATION OF AN INJECTION MOLD FOR MINIMIZING TEMPERATURE DEVIATION J H CHOI 1 S H CHOI 1 D PARK 2 C H PARK 2 B O RHEE 1 and D H CHOI 2 1 Graduate School of Mechanical Engineering Ajou University Gyeonggi 443 740 Korea 2 Graduate School of Mechanical Engineering Hanynag University Seoul 133 791 Korea Received 24 January 2011 Revised 15 June 2011 Accepted 17 June 2011 ABSTRACT The quality of an injection molded part is largely affected by the mold cooling Consequently this makes it necessary to optimize the mold cooling circuit when designing the part but prior to designing the mold Various approaches of optimizing the mold cooling circuit have been proposed previously In this work optimization of the mold cooling circuit was automated by a commercial process integration and design optimization tool called Process Integration Automation and Optimization PIAnO which is often used for large automotive parts such as bumpers and instrument panels The cooling channels and baffle tubes were located on the offset profile equidistant from the part surface The locations of the cooling channels and the baffle tubes were automatically generated and input into the mold cooling computer aided engineering program Autodesk Moldflow Insight 2010 The objective function was the deviation of the mold surface temperature from a given design temperature Design variables in the optimization were the depths distances and diameters of the cooling channels and the baffle tubes For a more practical analysis the pressure drop and temperature drop were considered the limited values Optimization was performed using the progressive quadratic response surface method The optimization resulted in a more uniform temperature distribution when compared to the initial design and utilizing the proposed optimization method a satisfactory solution could be made at a lower cost KEY WORDS Injection molding Cooling channel Cooling analysis PQRSM Design optimization 1 INTRODUCTION The cooling stage is the longest stage during the cycle time of the injection molding process Therefore the most effective method to reduce the cycle time is to reduce the cooling time The cooling time is fundamentally determined by the part thickness and mold temperature which creates a cooling time limitation If the mold temperature and part thickness are uniform over a whole part the cooling time is not a concern however non uniform part thickness and mold temperature distribution lengthen the overall cooling time A longer cooling time means poor temperature uniformity which can cause the part to warp This is especially true for large products such as automotive bumpers and instrument panels It is for these types of parts that temperature uniformity becomes the most important factor in mold design We developed an automated optimization of the cooling circuit for an early part design in order to check the design validity Usually the early part design is checked by the filing packing and warpage analyses without a cooling analysis This is because the assumption is that the mold temperature is uniform which is not actually true Providing a rapidly optimized cooling circuit for the designed part would help part designers correct their design Koresawa and Suzuki 1999 The optimization was designed to minimize the part temperature deviation using design variables such as the diameters and distances of the cooling channels and baffle tubes and the depths of the part from the mold surface of the cooling channels and baffle tubes A commercial computer aided engineering CAE tool Autodesk Moldflow Insight was used for the cooling analysis We successfully obtained an optimized cooling circuit in a time much shorter than can be achieved in a manual design In order to develop the automated optimization of the cooling circuit for the practical mold design practical design parameters such as the pressure drop limit and the coolant temperature rise were considered in the optimization The performance of the optimization technique can be affected by numerical noise in the responses To find an optimum solution effectively when numerical noise exists we performed an optimization by applying a regression based sequential approximate optimizer known as the Progressive Quadratic Response Surface Method PQRSM Hong et al 2000 which was part of a commercial process integration and design optimization PIDO tool known as the Process Integration Automation and Optimization PIAnO FRAMAX 2009 Corresponding author e mail rhex ajou ac kr 274 J H CHOI et al 2 MODEL AND CHANNEL CONFIGURATION 2 1 Model Configuration The model used for the optimization and CAE analysis was an automotive front bumper FB The size of the part was 1 800 600 mm the element type was triangular and the number of elements in the model was approximately 26 000 with an average aspect ratio of 1 5 The model is shown in Figure 1 2 2 Cooling Channel Configuration The cooling circuit for the automotive bumper mold is typically designed to have a horizontal plane of line cooling channels and to install baffle tubes from the line cooling channels However in this design unnecessarily long baffle tubes attached at a line cooling channel may cause a high pressure drop in the cooling channel The line cooling channels may not contribute to mold cooling due to their large distance from the part surface In order to improve the design the line cooling channels were located along the offset profile of the part surface as shown in Figure 2 The end points of the baffle tubes were also located on the offset profile along a line cooling channel Either the line cooling channels or baffle tubes were located on the offset profiles with equal arc distances between them 3 FORMULATION 3 1 Design Constraints The limitation of the pressure drop and the temperature rise between the inlet and outlet of cooling channel should also be considered in the design of the mold cooling circuit A high pressure drop usually occurs in a needlessly long cooling circuit In a long cooling circuit the flow rate of coolant is low which results in a high mold temperature and a high temperature rise at the outlet The design defect could eventually be found in the cooling analysis however the optimization is already time consuming so it is better to instead apply the limits as constraints in the optimization In this work we assumed that 4 line cooling channels were connected in series as a cluster as shown in Figure 3 Clusters are connected in parallel by a manifold Usually the maximum pressure drop in a cluster is limited to 200 kPa and the maximum temperature rise at the outlet is 5 o C Menges et al 2001 In the cooling analysis each line cooling channel is regarded as a separate independent circuit for convenience Because there were 4 line cooling channels in a circuit the limits on the pressure drop and the temperature rise in each line cooling channel were 50 kPa and 1 25 o C respectively We also have an additional constraint due to the fact that the diameter of the baffle tube must be greater than or equal to the diameter of the cooling channel because the baffle tube has lower heat removal efficiency than the cooling channel These three design constraints can be expressed as Equations 1 2 and 3 1 3 where G 1 is the constraint on pressure drop G 2 is the constraint on temperature rise and G 3 represents the subtraction of the diameter of the baffle tube from the diameter of the cooling channel 3 2 Design Variables In this work the diameters distances and depths of the line cooling channels and baffle tubes were chosen as design variables for optimization The total number of design variables was 6 as shown in Table 1 Typically the diameters of the cooling channels and baffle tubes are determined by the mold designer according to their rule of 0 Pa G 1 50000 pa 0 C o G 2 1 2 C o G 3 0 mm Figure 1 Finite element model of the product used for the optimization Figure 2 Configuration of cooling channels located along the offset profiles Figure 3 Clusters consisting of 4 cooling channels with baffle tubes DESIGN OPTIMIZATION OF AN INJECTION MOLD FOR MINIMIZING TEMPERATURE DEVIATION 275 thumb Rhee et al 2010 However it has been examined in great detail among the mold designers Table 1 shows the design variables with their ranges and initial values The minimum values for the cooling channel distance baffle distance and baffle depth were determined by the constraints of the machining requirement The maximum values of cooling channel distance and baffle distance were determined by the empirical maximum obtained from the mold designers The baffle distance was a discrete variable due to a restriction in the automated use of the CAE software In this work the baffle distances for optimization were 60 90 and 120 mm 3 3 Objective Function A principal purpose of the mold cooling circuit optimization is to achieve uniform temperature distribution over the part The uniform temperature distribution means that the temperature deviation caused by the cooling channels is minimized as shown in Figure 4 The objective function in the optimization was the standard deviation of part temperature as shown in Equation 4 The part temperature was an arithmetic average of the upper and the lower surfaces of the mold halves The mold surface temperature was calculated from the finite element of the part min 4 where is the standard deviation of the part temperature E i is the temperature of i th element E w is the average temperature of the entire triangular elements and N is the number of elements 4 OPTIMIZATION 4 1 Parametric Study In order to examine the effects of the design variables on the objective function pressure drop and temperature rise parametric studies were carried out A parametric study was performed by changing a variable in a certain range while keeping all other variables fixed Figures 5 7 show the results of parametric studies for the objective function pressure drop temperature rise respectively In each figure the x axis indicates the levels of design variables Every design variable was divided into 11 levels from its lower bound to its upper bound 5 and 5 mean the lower and upper bounds respectively When examining the temperature deviation the diameter of the cooling channels shows little influence to the objective function see Figure 5 This result was predictable because the cooling channel affects the part temperature to a lesser degree than the baffle tubes in the automotive bumper mold The automotive bumper mold has a deep core so that the mold cooling depends upon the baffle tubes rather than the cooling channels Another reason of the lack of influence can be that the flow state in the cooling channel remains turbulent in the range of the parametric study The cooling channel usually has a smaller diameter than the baffle tube When the flow in the baffle tube is kept in the turbulent state the flow in the cooling channel will be in the turbulent state The diameters of the baffle tubes show a tangible influence when it increases above a certain value Increasing of the diameter changes the flow in the tube to a laminar flow state This is the cause for the lower heat transfer coefficient when compared to the turbulent flow state This is why the temperature deviation becomes larger when the baffle tube diameter increases E i E w 2 N i 1 N Figure 4 Scheme of the temperature field by the cooling channels Table 1 Lower and the upper bounds for design variables and the initial values for the optimization unit mm Description Lower Initial Upper X 1 Channel diameter 10 30 40 X 2 Baffle diameter 10 30 40 X 3 Channel distance 60 90 120 X 4 Baffle distance 60 60 120 X 5 Channel depth 30 60 90 X 6 Bafle depth 306090 Figure 5 Parametric study result of temperature deviation objective function 276 J H CHOI et al Among all parameters the baffle depth shows the largest influence on the objective function as shown in Figure 5 As the baffle depth increases the objective function increases This means that the deeper location of the baffle tubes causes the temperature deviation to increase Also it confirms that the cooling of the automotive bumper mold depends upon the baffle tubes The diameters of the cooling channels and the baffle tubes have the highest influence on the pressure drop in the cooling circuit while the other variables show little influence see Figure 6 As the diameters increase the pressure drop decreases after a certain value This is also a predictable result as a larger diameter decreases the pressure drop The influences of the temperature rise at the outlet are shown in Figure 7 The most influential parameters are the baffle diameter and the channel distance The influence of the baffle diameter shows the highest values in the range from 1 to 3 In the case of the smaller baffle diameter the reduced surface area for the heat transfer may cause a smaller temperature rise while the larger baffle diameter may cause the lower heat transfer coefficient due to the lower flow rate The increased channel distance means that each cooling channel takes up a larger area of the part surface with a larger amount of heat removal This may give a physical explanation to why the increase of the temperature rise increases with channel distance The fluctuations shown in Figure 7 are supposed to be numerical noise 4 2 Optimization Results The largest increase in the temperature rise Figure 7 is approximately 0 15 o C This value is much less than the constraint The influence of the variables on the temperature rise is not tangible The baffle distance was considered the discrete variable in this work hence it was difficult to apply a general optimization method Because there were three values optimizations were carried out 3 times with the 5 design parameters The baffle distance was fixed in each optimization Figures 8 and 9 show the temperature deviations as the channel diameter x 1 and the channel distance x 3 change by 0 1 using the perturbation method around their initial design values From these results we recognized that the variations in the temperature deviations as x 1 and x 3 varied included numerical noise Therefore we chose PQRSM as the optimization method that could effectively optimize the response with numerical noise The PQRSM equipped in a commercial Figure 6 Parametric study result of the pressure drop Figure 7 Parametric study result of the temperature rise Figure 8 Variation of the temperature deviation w r t x 1 observed by using 0 1 perturbation method Figure 9 Variation of the temperature deviation w r t x 3 observed by using 0 1 perturbation method DESIGN OPTIMIZATION OF AN INJECTION MOLD FOR MINIMIZING TEMPERATURE DEVIATION 277 PIDO tool PIAnO approximates the objective function and constraints with quadratic functions in the trust region and it sequentially moves and reduces the trust region until it finds the optimum solution The results of the optimization using the PQRSM are shown in Table 2 Baseline represents the standard condition before applying the optimization After the optimizations were carried out for the 3 cases of the baffle distance x 4 the lowest temperature deviation was obtained in the case of a baffle distance of 60 mm Therefore we conclude that a baffle distance of 60 mm is our optimized result At this optimized result the temperature deviation was reduced by 19 2 compared to that of the baseline design while satisfying all other design requirements Among the design variables the channel diameter x 1 the baffle diameter x 2 and the channel distance x 3 remained close to their initial values while the channel depth x 5 moved toward the upper bound and the baffle depth x 6 toward the lower bound Thus we expect a better result if the bounds of the baffle distance x 4 channel depth x 5 and baffle depth x 6 can be relaxed 5 CONCLUSION In this study we carried out the optimization of the cooling circuit for an automotive front bumper The design objective was to minimize the temperature deviation while satisfying all constraints There were three design constraints that included the pressure drop temperature rise and aspect ratio in addition to side constraints on six design variables Among the six design variables the baffle distance was the discrete design variable Thus we carried out optimizations for the three cases of baffle distances being 60 90 and 120 mm The lowest temperature deviation was obtained in the case of a baffle distance of 60 mm In this case the temperature deviation was reduced by 19 2 compared to the baseline design while satisfying all design requirements It is believed that the design optimization approach of employing CAE and PIDO tools adopted in this study can be applied for the design of many industrial manufacturing processes REFERENCES FRAMAX Inc 2009 PIAnO Tutorial FRAMAX Inc 2009 PIAnO User s Manual Hong K J Choi D H and Kim M S 2000 Progressive quadratic approximation method for effective constructing the second order response surface models in the large scaled system design The Korean Society of Mechanical Engineers A 24 12 12 3040 3052 Koresawa H and Suzuki H 1999 Autonomous arrangement of cooling channels layout in injection molding Proc 1999 Annual Technological Conf Society of Plastics Engineers 1073 1077 Menges G Michaeli W and Mohren P 2001 How to Make Injection Molds 3rd Edn Hanser Gardner Publications Inc Ohio 298 302 Rhee B O Park C S Chang H K Jung H W and Lee Y J 2010 Automatic generation of optimum cooling circuit for large injection molded parts Int J Precision Eng and Manufacturing 11 439 444 Table 2 Optimization results summary Lower Baseline X 4 60 X 4 90 X 4 120 Upper x 1 10 00 30 00 29 67 28 39 30 00 40 00 x 2 10 00 30 00 30 36 28 39 30 00 40 00 x 3 60 00 90 00 89 37 90 29 88 13 120 00 x 4 60 00 60 00 60 00 90 00 120 00 120 00 x 5 30 00 60 00 87 63 88 81 90 00 90 00 x 6 30 00 60 00 30 00 30 00 30 00 90 00 OBJ 6 62 5 35 5 60 5 46 G 1 0 16790 16904 16610 8758 50000 G 2 0 0 36 0 43 0 33 0 38 1 20 G 3 0 00 0 69 0 00 0 00 0 00