【正文】 其余各件的操作方法與繪制定模固定板基本相同，這塊就不做一一介紹了。子組件1：上模板部分步驟1：?jiǎn)螕舨藛挝募陆?，打開(kāi)新建對(duì)話框，選擇組件類型，輸入新建文件名稱“shangmo”,然后確定，進(jìn)入組件工作環(huán)境，選擇子組件，進(jìn)行裝配。接著重復(fù)步驟2操作插入第二個(gè)元件導(dǎo)柱，在元件放置對(duì)話框中單擊添加約束，相應(yīng)選擇組件和元件的裝配參考，使其符合條件。重復(fù)以上步驟，完成整個(gè)上模板子組件的裝配如圖68所示。 圖69 下模子組件模具的整體裝配：步驟1：?jiǎn)螕舨藛挝募陆?，打開(kāi)新建對(duì)話框，選擇組件類型，輸入新建文件名稱總裝，然后確定，進(jìn)入組件工作環(huán)境。步驟3：重復(fù)步驟2導(dǎo)入組件“”, 確定后系統(tǒng)彈出元件放置對(duì)話框，選擇連接類型為滑動(dòng)桿，連接的名字命名為“xiamo1”，約束欄中“軸對(duì)齊”選擇元件和組件在導(dǎo)套和導(dǎo)柱各自的軸線，約束欄中“旋轉(zhuǎn)”選擇元件和組件中兩個(gè)平行的面作為它的旋轉(zhuǎn)約束，選項(xiàng)卡中顯示完成連接定義，單擊確定。完成的裝配圖如圖611所示。單擊新建出現(xiàn)彈簧定義對(duì)話框，名稱為系統(tǒng)的缺省設(shè)置，參照類型選擇點(diǎn)至點(diǎn)，選擇墊板和推板中相關(guān)的基準(zhǔn)點(diǎn)，屬性設(shè)置中k=10,u=100圖標(biāo)直徑設(shè)置為25mm，單擊確定顯示的畫(huà)面如圖612所示。重復(fù)步驟2添加其他的電動(dòng)機(jī)，驅(qū)動(dòng)器2“feiliao1”方向向上；驅(qū)動(dòng)器3“tuiban1”方向向上；驅(qū)動(dòng)器4“tuiban1”方向向下；驅(qū)動(dòng)器5“sujian1平移軸”方向向上；驅(qū)動(dòng)器6“sujian1旋轉(zhuǎn)軸”方向向上“sujian2” “sujian3” “sujian4”同“sujian1”一樣設(shè)置。圖615 分析對(duì)話框　圖616 分析定義對(duì)話框　圖617 電動(dòng)機(jī)設(shè)置對(duì)話框步驟4：結(jié)果回放，話框單擊主工具欄中“回放”按鈕，系統(tǒng)彈出回放對(duì)話框（如圖618所示），單擊“演示”，此時(shí)彈出動(dòng)畫(huà)對(duì)話框（如圖619所示），單擊“播放”按鈕即可播放動(dòng)畫(huà)，單擊“捕獲”按鈕，彈出“捕獲”對(duì)話框，保存畫(huà)片。通過(guò)在這次畢業(yè)設(shè)計(jì)中參考、查閱各種有關(guān)模具方面的資料對(duì)速度表主動(dòng)齒輪模具及工作過(guò)程的動(dòng)畫(huà)設(shè)計(jì)，掌握了Pro/ENGINEER的一些基本使用方法；使用Mechanism模塊,實(shí)現(xiàn)機(jī)構(gòu)定義,用來(lái)處理裝配件的運(yùn)動(dòng)仿真；體會(huì)到利用Pro/ENGINEER繪圖的優(yōu)越性和他的強(qiáng)大功能；學(xué)會(huì)了基本塑料注射模具的設(shè)計(jì)步驟；使我在這短暫的時(shí)間里，對(duì)模具的認(rèn)識(shí)有了一個(gè)質(zhì)的飛躍,并對(duì)塑料模具設(shè)計(jì)的各種成型方法，成型零件的設(shè)計(jì)，主要工藝參數(shù)的計(jì)算，產(chǎn)品缺陷及其解決辦法，模具的總體結(jié)構(gòu)設(shè)計(jì)及零部件的設(shè)計(jì)等都有了進(jìn)一步的理解和掌握。致謝本次設(shè)計(jì)從選題、設(shè)計(jì)、到指導(dǎo)工作均是在陶褔春老師的直接關(guān)懷和悉心指導(dǎo)下完成的，導(dǎo)師嚴(yán)謹(jǐn)?shù)闹螌W(xué)態(tài)度，敏捷的思維，淵博的知識(shí)均使我受益非淺。這次設(shè)計(jì)是大學(xué)期間所學(xué)知識(shí)的一次綜合運(yùn)用，它涉及到軟件應(yīng)用，機(jī)械設(shè)計(jì)，模具設(shè)計(jì)等多方面的知識(shí)。在此期間，我遇到了許多困難和疑惑，但在老師和同學(xué)的幫助下，得以在設(shè)計(jì)的期限內(nèi)按要求順利完成設(shè)計(jì)。大學(xué)畢業(yè)后，我將走向工作崗位。母校給我的一切我都將銘記于心。 Feature。1. IntroductionThe research of bining neural network with expert system has been paid much attention all over the word. At present, artificial intelligence expert system is widely used in CAPP, which is the main part of the integrated CAD/CAM. But there are serious problems in building CAPP system, which are the representation and inference of process knowledge. This paper uses feature technique, and bines artificial intelligence expert system with neural network to solve the above problems. Description of Workpiece Information The description and the input of workpiece information are a prerequisite torealize present, the geometry model, technique widely employed in CAD can’t be used in CAPP directly, for it provides only the geometry information of workpiece, and lacks the manufacturing information. Now the method based on feature technique is used to describe workpiece. The diagram workpieceMain direction levelPosition surface levelManufacturing feature levelGeometry informationManufacturing informationof workpieces based on feature hierarchy is shown in . The diagram of workpiece based on feature hierarchyThe main direction is defined by the normal line of workpiece surface. The position surfaces are surfaces which are at the different position, but have the same normal line direction. The manufacturing feature includes various surface, holes and grooves etc. Every feature is given by the corresponding parameters.3. The Representation of Process Knowledge Generally, the task of engineering design may be divided into a series of subtasks. The different subtask needs different knowledge. According to the characteristic of process design, the new representation method of multiknowledge is put forward:The representation of knowledge based on frame representation of knowledge based on frame structure can show not only the attribute of the thing, but only the subordination of different things. The frame structure expresses factual knowledge including blank fact, tool fact and machine tool fact etc. in our CAPP representation of knowledge based on production rule The characteristic of production rule is to use inplete elicitation knowledge to solve question. Its expression way is:IF W1(x) THEN W2(x)In this system, the production rule is used to represent the control knowledge whose content of knowledge is little and whose rule is representation of knowledge based on neural network Generally, selection knowledge has a large proportion in knowledge base of process design system, including the selection of blank, the machining method of feature unit, the machining tool and clamping apparatus etc. In existent CAPP system, the representation of knowledge usually es form the production rule of AI expert system. Although the great progress has been made in the research and application of expert system, there are also some problems:(1)“battle neck” and“narrow flight of steps”in acquiring knowledge.(2) The inference ability is weak. The problems of mach conflict and bination explosion usually arise.(3)The intelligence level is low. On the contrary, neural network technique is more effective in knowledge acquirement and inference. In this paper, BP network is regarded as rule memory. Knowledge acquirement is realized by the training of learning sample, and the inference is acplish by multiorder forward spread of network. In every neural network, knowledge is shown by network structure and value of joint weight [(,)] and threshold ((θ)). They are distributed in the whole neural network. The process of acquiring knowledge is shown in . In this system, every neural network is regarded as a module to represent and infer the partial knowledge. For example, system uses production rules to represent manufacturing knowledge of hole.xmynW(z1,y1)Y2Y1zkW(z1,y1)Z1W(zk,yn)xMzkX1W(xk,zk)( x1 ,…,xM,y1t…ytN){W()}qq—training order, ynt—the aim of yn==D or {W(,)}q={W}D—given precision {W}—constant weight array the process of acquiring knowledge It needs ten rules, and condition items are many, structure is plicated. If ten rule condition items as training sample. The rule condition items as input of network and condition items as output of network, a 10╳9╳4 BP neural network can be built. The knowledge of selecting hole process plan may be stored conveniently by network structure and value of joint weight and threshold. Where diameter is divided into four conditions。 tolerance grade is divided into there conditi