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外文翻譯
專 業(yè) 機械設計制造及其自動化
學 生 姓 名 朱 丹 萍
班 級 BD機制042
學 號 0420110226
指 導 教 師 劉 道 標
外文資料名稱： An Intelligent Cavity Layout Design System for Injection Moulds
外文資料出處： International Journal of CAD/CAM Vol 2,No.1,pp
69～75(2002)
附 件： 1.外文資料翻譯譯文
2.外文原文
指導教師評語：
簽名：
年 月 日
智能腔布置設計系統(tǒng)的注塑模具
胡衛(wèi)剛，Syed Masood
朱丹萍 譯
摘要：本文介紹了多腔注塑模具。多腔注塑模具是一種智能腔布置設計系統(tǒng)。該系統(tǒng)的目的是協(xié)助模具設計人員在腔布局設計，在概念設計階段。該復雜性和原則腔布局設計以及各屬地的注塑模具設計介紹。對于腔布局設計，從功能，整體結構和總體過程中一一解釋。文中還討論了這些問題，作為知識表示和基于案例的推理在使用該系統(tǒng)的發(fā)展。系統(tǒng)的功能是用一實例說明了腔布局設計問題。
關鍵詞：智能設計，腔體布局設計，注塑模具設計，基于案例推理，系統(tǒng)設計。
1、導言
在制造，注塑成型，是一個最廣泛使用的生產工藝生產塑膠零件與高生產速度和很少或沒有整理需要對塑料制品等。過程包括注射液的塑料材料，從一個熱點成為一個封閉的模具，從模具中使塑料酷凝固和拔出成品。因為每一個新的塑料制品，注塑成型機，需要有新的注塑模具。設計和制造注塑模，是一個費時和昂貴的過程和傳統(tǒng)上需要高度熟練的工具和模具制造商。注塑模具包括幾個部分，其中包括結晶器基地，有溶洞，導向銷，澆道，蓋茨，冷卻水渠道，幻燈片和噴射器。模具設計也受其他幾個因素，如部分幾何，模具素材，每腔模具。
在計算機技術和人工智能智力中得到指示，以減少成本和時間，在設計和制造的一種注塑模具。注塑模具設計一直是主要的研究領域，因為它是一個復雜的過程涉及幾個子設計相關的各種組件該模具，每個需要專業(yè)的知識和經驗。模具設計，也影響到生產率， 模具維修成本，可制造模具， 和高質量的注塑部分。大部分的工作在模具設計工作已經向應用系統(tǒng)，知識為基礎的系統(tǒng)和人工智能情報，以補充大量專業(yè)知識，在傳統(tǒng)的設計過程。 kruth和willems研制出一種智能支持系統(tǒng)的設計注塑模具整合商用CAD / CAM系統(tǒng)，關系數(shù)據(jù)庫和一個專家系統(tǒng)。提出了一個系統(tǒng)化方法論和知識庫，為注塑模具設計在并行工程環(huán)境。 raviwongse 和allada制定了一個神經網(wǎng)絡化設計輔助工具，計算出模具復雜性指數(shù)，以幫助模具設計人員，以評估他們提出了模具設計對模具制造。制定了一個計算系統(tǒng)為工藝設計的注塑基于黑板為基礎的專家系統(tǒng)和基于案例推理方法，其中包括模具設計， 生產調度，成本估算和注塑
參數(shù)。討論了注塑模具設計，從功能性透視使用功能設計知識。發(fā)展一個互動的以知識為基礎的CAD系統(tǒng)注射模具設計知識和圖形模塊。
幾項研究也取得了改善設計中的具體組成部分的注塑模具。 王景榮等制定了一個以知識為基礎的和面向對象設計方法的飼料系統(tǒng)注塑模具，它可以有效地設計類型， 位置和大小相當于一澆注系統(tǒng)在模具。也開發(fā)了軟件系統(tǒng)，實現(xiàn)自動設計澆注并提供評價澆注設計基于特定的性能參數(shù)。提出了一套方法測定方向，在注塑模具設計的基礎上自動識別與提取削弱特點。在模具設計中通過計算削弱卷，最大限度地減少破壞了工作，在設計冷卻系統(tǒng)在注射模，并提出優(yōu)化設計根據(jù)熱分析和設計靈敏度分析該冷卻階段的注射成型工藝。
注塑模具設計中，其中有很少人注意設計的腔布局多腔注塑模具。腔布局設計影響到整個過程的注塑成型，直接， 由于這是其中一個最重要的階段，在模具設計過程。審議腔布局設計在注塑模具，在概念設計階段，將改善質量注射成型產品。
本文介紹了開發(fā)一個設計支持系統(tǒng)，所謂智能腔布局設計系統(tǒng)，為多腔注塑模具基于知識基礎和面向對象的方法。 它采用了基于案例，并裁定為基礎的推理到達布局解決方案。它是基于對商業(yè)軟件系統(tǒng)命名為綜合開發(fā)平臺，讓顧客發(fā)展自己的知識為基礎的系統(tǒng)。該目的是要充分利用現(xiàn)有的技術人工智能在協(xié)助模具設計師概念設計階段。
2 、型腔布置設計在注塑模具
目前的做法為注塑模具的設計，尤其是腔布局設計，在很大程度上取決于設計師的經驗和知識。因此，它將是不可取利用知識工程，人工智能和智能設計技術在創(chuàng)造一個可接受型腔布置設計在注塑模具準確，高效率。在模具設計中，大多數(shù)的格局腔布局和規(guī)則和原則腔布局設計也可以很容易的代表參與形式的知識， 它可以用來設計系統(tǒng)。
例如，以選擇合適的布局模式設計主要是依賴于工作環(huán)境， 條件和要求的客戶，主要基于設計師的技能和經驗。作選擇相互矛盾的因素，將依靠明顯設計師的知識和經驗。這是相當適合智能化設計技術，以用于系統(tǒng)設計這樣的情況，特別是創(chuàng)新設計。
注射模的設計，主要涉及考慮設計的下列要素：
（ 1 ）模具類型
（ 2 ）有多少腔腔布局
（ 4 ）流道系統(tǒng)
（ 5 ）噴射系統(tǒng)
（ 6 ）冷卻系統(tǒng)
（ 7 ）確定冷卻系統(tǒng)
（ 8 ）圖形結果顯示，輸出
3 、結構和設計過程
結構智能腔布局設計系統(tǒng)是基于案例推理和推理設計圍繞軟件系統(tǒng)。所示的總體結構可以看出，一般設計過程中開始與定義中的設計規(guī)格。該系統(tǒng)檢索出類似的案件，從案件基地通過計算之間的相似性案件和新的案例。如果解決不好，那么將利用以規(guī)則為基礎的推理雙方達成一項解決方案。如果解決的辦法是仍然不理想的話，那么用戶必須修改部分的初步設計規(guī)格。使用基于案例的技術，在設計過程中使用戶能夠獲得解決問題的設計問題更迅速和靈活的結構，知識基礎和數(shù)據(jù)庫的使用在發(fā)展是基于背后知識庫和數(shù)據(jù)庫結構，從軟件系統(tǒng)是上講，這是一個在商業(yè)上可用軟件開發(fā)平臺。
4 、發(fā)展
4.1 、分類知識
各種邏輯和步驟所涉及的版面設計， 有各種不同的知識，并需要以描述和代表在腔版面設計。該類型的知識，可分為五種基于面向對象（面向對象）的概念，分述如下：
（ 1 ）設計實例/案例：以前設計的情況下，結合目前的設計實例
（ 2 ）屬性：設計變量，特性設計問題
（ 3）規(guī)則：一般設計規(guī)則，設計經驗
（ 4 ）程序和/或模型：數(shù)值計算， 數(shù)學建模，分析，評價和程序。
4.2 、基于案例的推理
基于案例推理法是依賴于第一案例名詞?；诎咐龣z索的基礎是"相似公制" 。因此，如何計算相似度顯然很關鍵。 相似性度量讓每一個層面對應于一個領域，其價值是在查詢，它們之間的距離的情況和查詢（對應點在這多維空間）的計算方法是不同的，為序和名義領域的合作。
4.3 、距離為序領域的距離計算方法是：
（ 1 ）其中，自dij必須介于0和1 ，還必須介于0和1 。
（ 2 ）其中，因為該公司與dij必須介于0和1 ， 還必須介于0和1 。
4.4 、驗證案例
驗證的情況是，以檢查是否每條可接受的情況下，找出最合適的一個，所以每個案件應相關的測試方法和測試結果都不同。 不僅如此，根據(jù)一定的條件，它的測試結果，設計問題，可視為解決方案原型為進一步完善。
4.5 、準則的有效性降低成本
隨著應用空腔布局，兩種降低成本。一個是整體理論降低成本所取得的使用系統(tǒng)進行概念設計的注射液模具。另一種是實際成本降低的價值記錄在案例中，其中可能被用來做案例庫推理，如果案件以"降低成本"為理論之一，就沒有必要的任何標準的有效性，降低成本，因為節(jié)省成本將明顯地走出來，提高設計質量和回應給客戶。對于有效性的實際成本進一步降低。舉例來說， 我們可以比較一下，，并確定哪一個能更好地適合客戶的要求。你還可以使用比例降低成本的公式做比較。該百分比降低成本都可以計算出來。
5 、應用實例
一個應用實例， "測定腔布局模式" ， "概念設計腔布局" 所提供的智能腔布置設計系統(tǒng),提供以下資料： 如果最初的設計條件是：
（ 1 ）使用什么類型的模具？兩板塊
（ 2 ）使用什么類型的轉輪？
（ 3 ）什么形狀的產品能使其成型？ 矩形，其結果是由于：格局腔布局設計是： Y型矩形布置知識基礎，是開發(fā)利用的特點。
6 、結論
問題的設計型腔布置多重腔注射模具由電腦輔助設計支持系統(tǒng)。 注塑成型由計算機為基礎的設計系統(tǒng)提供,極大的節(jié)省了時間和成本，在達成最佳布局。 發(fā)展智能腔布局設計系統(tǒng)）相信是第一次嘗試在這個方向利用知識為基礎的方針。該發(fā)展注塑模具是基于在Windows環(huán)境下PC機。從實際的角度來看，可以用來作為一種工具來設計以落實腔布局設計的注射液模具在概念設計階段。它提供了一個積極一步的發(fā)展，完全自動化注塑模具設計過程中，從產品模型模具制造。
七、參考文獻
[1] Menges, G. et. al. (1986), “How to Make InjectionMolds”, Hanser Publisher, Munich.
[2] Kruth, J.P. and Willems, R. (1994), “Intelligent supportsystem for the design of injection moulds”, Journal of Engineering Design, 4(5), 339-351.
[3] Lee, R-S, Chen, Y-M, and Lee, C-Z (1997), “Developmentof a concurrent mould design system: a knowledge basedapproach”, Computer Integrated Manufacturing Systems,10(4), 287-307.
[4] Raviwongse, R. and Allada, V. (1997), “Artificial neuralnetwork based model for computation of injection mouldcomplexity”, International Journal of AdvancedManufacturing Technology, 13(8), 577-586.
[5] Kwong, C.K. and Smith, G.F. (1998), “A computationalsystem for process design of injection moulding: combining blackboard-based expert system and casebasedAdvanced Manufacturing Technology, 14(4), 239-246.
[6] Britton, G.A., Tor, S.B., et. al. (2001), “Modellingfunctional design information for injection mould design”,International Journal of Production Research, 39(12),2501-2515.
[7] Mok, C.K., Chin, K.S., and Ho, J.K.L. (2001), “Aninteractive knowledge-based CAD system for moulddesign in injection moulding processes”, InternationalJournal of Advanced Manufacturing Technology, 17(1),27-38.
[8] Ong, S.K. Prombanpong, S. and Lee, K.S. (1995), “Anobject-oriented approach to computer-aided design of aplastic injection mould”, Journal of IntelligentManufacturing, 6(1), 1-10.
[9] Irani, R.K. Kim, B.H. and Dixon, J.R. (1995), “Towardsautomated design of the feed system of injection mouldsby integrating CAE, iterative redesign and features”,Transactions ASME Journal Engineering for Industry,117(1), 72-77.
[10] Nee, A.Y.C., Fu, M.W. et. al., (1997), “Determination ofoptimal parting directions in plastic injection mould design”, Annals CIRP, 46(1), 429-432.
[11] Chen, L-L and Chou, S-Y (1995), “Partial visibility forselecting a parting direction in mould and die design”,Journal of Manufacturing Systems, 14(5), 319-330.
[12] Park, S.J. and Kwon, T.H. (1998), “Thermal and Designsensitivity analyses for cooling system of injection mould.Part 2:Design sensitivity analysis”, Transactions ASMEJournal Manufacturing Science & Engineering, 120(2),296-305.
[13] Lin, J.C. (2001), “Optimum gate design of freedominjection mould using the abductive network”, InternationalJournal of Advanced Manufacturing Technology, 17(4),297-304.
[14] Maher, M.L. et. al.. (1996), “Developing Case-BasedReasoning for Structural Design”, Intelligent System & Their Applications, IEEE Expert, USA, June.
[15] The Haley Enterprise, Inc. (1994), “Documentation ofRETE++”.
An Intelligent Cavity Layout Design System for Injection Moulds
Weigang Hu and Syed Masood*
Abstract - This paper presents the development of an Intelligent Cavity Layout Design System (ICLDS) for multiple cavityin jection moulds. The system is intended to assist mould designers in cavity layout design at concept design stage. Thecomp lexities and principles of cavity layout design as well as various dependencies in injection mould design are introduced. The knowledge in cavity layout design is summarized and classified. The functionality, the overall structure and general process of ICLDS are explained. The paper also discusses such issues as knowledge representation and case-based reasoning used in the development of the system. The functionality of the system is illustrated with an example of cavity layout design problem.
Keywords: Intelligent design, cavity layout design, injection mould design, case-based reasoning, design support system
1. Introduction
In manufacturing, the injection moul ding is one of he most widely used production processes for producing plastic parts with high production rate and little or no finishing required on plastic products. The process consists of injecting molten plastic material from a hot chamber into a closed mould, allowing the plastic to cool and solidify and ejecting the finished product from the mould. For each new plastic product, the injection moul ding machine requires a new injection mould. Design and manufacture of injection mould is a time consuming and expensive process and traditionally requires highly skilled tool and mould makers. An injection mould consists of several components, which include mould base, cavities, guide pins, a sprue, runners, gates, cooling water channels, support plates, slides and ejector mechanism [1]. Design of mould is also affected by several other factors such as part geometry, mould material, parting line and number of cavities per mould.
With the advances in computer technology and artificial intelligence, efforts have been directed to reduce the cost and lead time in the design and manufacture of an injection mould. Injection mould design has been the main area of research since it is a complex processinvolving several sub-designs related tovari ous components of the mould, each requiring expert knowledge and experience. Mould design also affects the productivity ,mould maintenance cost, manufacturability of mould ,and the quality of the mould ed part. Most of the workin mould design has been directed to the application of expert systems, knowledge based systems and artificial intelligence to eliminate or supplement the vast amount of human expertise required in traditional design process. Kruth and Willems [2] developed an intelligent support system for the design of injection moulds integrating commercial CAD/CAM, a relational database and an expert system. Lee et. al. [3] proposed a systematic methodology and knowledge base for injection mould design in a concurrent engineering environment. Raviwongse and Allada [4] developed a neural networkbased design support tool to compute the mould complexity index to help mould designers to assess their proposed mould design on mould manufacturability. Kwong and Smith [5] developed a computational system for the process design of injection moulding based on the blackboard-based expert system and the case-based reasoning approach, which includes mould design, production scheduling, cost estimation and determination of injection moulding parameters. Britton et. al. [6]discussed the injection mould design from a functional perspective using functional design knowledge and a number of knowledge libraries. Mok et. al. [7] developed an interactive knowledge-based CAD system for injection mould design incorporating computational, knowledge
and graphic modules.
Several studies have also been made on improving the design of specific components of an injection mould. On get. al. [8] developed a knowledge-based and objectoriented
approach for the design of the feed system for injection moulds, which can efficiently design the type, location and size of a gating system in the mould. Iraniet. al. [9] also developed a software system for automatic design of gating and runner systems for injection moulds and provide evaluation of gating design based on specified performance parameters. Nee et. al. [10] proposed a methodology for determination of optimal parting directions in injection mould design based on automatic recognition and extraction of undercut features. Chen and Chou [11] developed algorithms for selecting a parting line in mould design by computing the undercut volumes and minimising the number of undercuts. Park and Kwon [12] worked on the design of cooling systems in injection moulds and proposed an optimal design based on thermal analysis and design sensitivity analysis of the cooling stage of the injection moulding process. Lin [13] worked on the use of gate size and gate position as the major parameters for simulated injection mould performance prediction.
One area in injection mould design, which hasreceived little attention, is the design of cavity layout in a multiple cavity injection mould. Cavity layout design affects the whole process of injection moulding directly, since it is one of the most important phases in mould design process. Consideration of cavity layout design in injection mould at concept design stage will improve the quality of injection moulded products because it is associated with the determination of many key factors affecting the design and quality of mould. Such factors include number of cavities; parting line; type of mould; type and position of gate; runner system; cooling system and ejection system. Some of these factors are difficult to build as true mathematical models for analysis and design.
This paper presents the development of a design support system, called Intelligent Cavity Layout Design System (ICLDS), for multiple-cavity injection moulds based on knowledge based and object oriented approaches. It uses the case-based and ruled-based reasoning in arriving at the layout solution [14]. It is based on the commercial software system named “RETE++”, which is an integrated development platform for customers to develop their own knowledge-based systems [15]. The objective is to make full use of available techniques in artificial intelligence in assisting mould designers at concept design stage.
2. Cavity Layout Design in Injection Moulds
Current practice for injection mould design, especially cavity layout design, depends largely on designers’ experiences and knowledge. It would therefore be desirable to use knowledge engineering, artificial intelligence and intelligent design techniques in generating an acceptable cavity layout design in injection mould accurately and efficiently. In mould design, most of patterns of cavity layout and rules and principles of cavity layout design can also be easily represented in the form of knowledge, which can be used in most of knowledge-based design systems.
For example, for the layout patterns shown in Fig. 1, the criteria to select the suitable layout pattern for design are mainly dependent on working environments, conditions and requirements of customer and are mainly based on designer’s skill and experience. To make a choice of contradictory factors will rely obviously on designer’s knowledge and experiences. It is rather suitable for intelligent design techniques to be used in systems designed for such situations, especially for routine or innovation design.
Design of injection mould mainly involves consideration of design of the following elements or sub-systems:
(1) mould type
(2) number of cavities
(3) cavity layout
(4) runner system
(5) ejector system
(6) cooling system
(7) venting
(8) mounting mechanism
Most of the elements are inter-dependent such that itis virtually impossible to produce a meaningful flowchart covering the whole mould design process. Someof the design activities form a complicated design network as shown in Fig. 2.Obviously, in injection mould design, it is difficult for designer to monitor all design parameters. Cavity design and layout directly affects most of other activities.
The application of advanced knowledge based techniques to assist designer in cavity layout design at concept design stage will greatly assist in the development of a comprehensive computer-aided injection mould design and manufacturing system. It is noted from Fig. 1 that a number of different layout patterns are possible with multiple cavities inside a mould. Higher the number of cavities of mould, higher the productivity of the injection mould. But this may lead to difficulties with issues such as balancing the runners or products with the complicated cavity shapes, which in turn may lead to problems of mould manufacturability. It is also possible that the number of cavities and the pattern of cavity layout will influence the determination of parting line, type of gate, position of gate, runner system and cooling system. Most of the main activities of mould design are therefore linked to cavity layout design. Fig. 3 shows the relations between cavity layout design and other design activities. The cavity layout design problem therefore depends upon a number of functionalities of the overall mould design system, which includes:
(1) definition of design specifications including analysis and description of characteristics of design problem
(2) determination of mould type
(3) determination of number of cavities
(4) determination of orientation of product
(5) determination of runner type and runner configuration
(6) determination of type and position of gate
(7) cavity layout conceptual design
(8) evaluation of ejection ability, manufacturingability and economic performances
(9) determination of cooling system
(10) graphic results display and output
3. Structure of ICLDS and the Design Process
The structure of the Intelligent Cavity Layout Design System (ICLDS) is based on case-based reasoning and ruled-based reasoning designed around the RETE++software system. Fig. 4 shows the overall structure of ICLDS schematically. Fig. 5 shows the general design process of ICLDS. The design process starts with the definition of design specifications. The ICLDS system retrieves similar cases from case base by computing the similarity between the cases and the new case. If the solution is satisfactory, then results are displayed graphically. If the solution is not satisfactory, then ICLDS will use rule-based reasoning with forward or backward chaining or a mixture of both to arrive at a solution. If the solution is still unsatisfactory, then the user has to modify some of the initial design specifications. The use of case-based technology in the design process in ICLDS allows the user to obtain the solution(s) of design problem more quickly and flexibly.
The structure of knowledge base and database used in the development of ICLDS is based on the underlying knowledge base and database structure from the RETE++ software system, which is a commercially available software development platform.
4. Development of ICLDS
4.1. Classifications of Knowledge
For various logic and steps involved in layout design, there are different kinds of knowledge that needs to be described and represented in cavity layout design. The types of knowledge can be classified into five kinds based on object oriented (OO) concept as described below:
(1) Design instance/case: previous design cases and current design instances
(2) Relation: superclass-class-subclass relation, classin stance relation
(3) Attribute: design variables, features, attributes of design problem
(4) Rule: general design rules, design experiences
(5) Procedure and/or model: numeric calculation, mathematical modeling, analysis, evaluation and procedures.
4.2. Knowledge Representations
To describe each of these types of knowledge, the internal data structures of the ECLIPSE language, included in RETE++ inherently, can be used to make the object orientated representation of the design process as explained earlier.
4.3. Case-based Reasoning
Case-Based Reasoning (CBR) is dependent firstly on case retrieved. Case-based retrieval is based on “Similarity Metric”. Therefore, how to calculate the similarity is obviously the key technique in CBR, and it is described in detail as below. which, since dij must range between 0 and 1, must also range between 0 and 1. which, since Wj and dij must range between 0 and 1, must also range between 0 and 1.
4.4. Validation of Case
Validation of case is to check up whether each acceptable case is suitable for current problem and to find out the most suitable one, so each case should be associated with testing methods and tested results on it. Only the case, under the given conditions, for which all tested results on it match those of the current design problem, can be considered as the solution prototype for further refining.
4.5. Criteria for Validity of Cost Reduction
With the application of ICLDS for cavity layout, two kinds of cost reduction can be expected. One is the overall theoretical cost reduction achieved in using the system to carry out the concept