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畢業(yè)設計（論文）
題目： 水泵體零件工藝及工裝設計
信機 系 機械工程及自動化 專業(yè)
學 號：
學生姓名：
指導教師：
無錫太湖學院本科畢業(yè)設計（論文）
誠 信 承 諾 書
本人鄭重聲明：所呈交的畢業(yè)設計（論文）水泵體零件工藝及工裝設計是本人在導師的指導下獨立進行研究所取得的成果，其內容除了在畢業(yè)設計（論文）中特別加以標注引用，表示致謝的內容外，本畢業(yè)設計（論文）不包含任何其他個人、集體已發(fā)表或撰寫的成果作品。
班 級：
學 號：
作者姓名：
年 11月 23日
信 機 系 機械工程及自動化 專業(yè)
畢 業(yè) 設 計論 文 任 務 書
一、題目及專題：
1、題目 水泵體零件工藝及工裝設計
2、專題
二、課題來源及選題依據(jù)水泵體零件屬于結構較復雜的箱體類零件，設計內容包含工藝文件制訂、刀具選擇、切削用量計算等，以及夾具設計。是對機械專業(yè)學生所學專業(yè)知識的綜合應用。該課題難度適中，設計量合適，能提升學生對知識的應用能力。
三、本設計（論文或其他）應達到的要求： 1.根據(jù)零件使用要求，分析零件的機械加工的工藝性，編制零件機械加工工藝規(guī)程，并形成全套工藝文件，即工藝過程卡、機加工工藝卡、工序卡；
2.分析計算該零件機械加工工藝參數(shù)，選用加工設備和工藝裝備；
3.設計該零件加工的專用夾具，繪制夾具裝配圖及全套非標零件圖，分析夾具的定位精度等。
4.編寫設計說明書（大于30頁）；
5.專業(yè)外語翻譯（大于8000~10000字符，約合漢字5000字符）；
四、接受任務學生：
五、開始及完成日期：
自2012年12月1日 至2013年5月20日
六、設計（論文）指導（或顧問）：
指導教師 簽名
簽名
簽名
教研室主任
〔 學科組組長 研究所所長 〕 簽名
系主任 簽名
2012年 12月 1日
II
3
英文原文
Application and development
Of case based reasoning in fixture design
Abstract: Based on the case based designing (CBD) methodology, the fixture similarity is in two respects: the function and the structure information. Then, the computer aided fixture design system is created on case based reasoning (CBR),in which the attributes of the main features of workpiece and structure of fixture as case index code are designed for the retrieve of the similar cases, and the structure and hierarchical relation of case library are set up for store. Meanwhile, the algorithm based on the knowledge guided in the retrieve of the similar cases, the strategy of case adapt at ion and case storage in which the case ident if cat ion number is used to distinguish from similar cases are presented. The application of the system in some projects improves the design efficiency and gets a good result .
Keywords: case based reasoning ;fixture design; computer aided design(CAD)
Fixtures are devices that serve as the purpose of holding the workpiece securely and accurately, and maintaining a consistent relationship with respect to the tools while machining. Because the fixture structure depends on the feature of the product and the status of the process planning in the enterprise, its design is the bottleneck during manufacturing, which restrains to improve the efficiency and leadtime. And fixture design is a complicated process, based on experience that needs comprehensive qualitative knowledge about a number of design issues including workpiece configuration, manufacturing processes involved, and machining environment. This is also a very time consuming work when using traditional CAD tools (such as Unigraphics, CATIA or Pro/E), which are good at performing detailed design tasks, but provide few benefits for taking advantage of the previous design experience and resources, which are precisely the key factors in improving the efficiency. The methodology of case based reasoning (CBR) adapts the solution of a previously solved case to build a solution for a new problem with the following four steps: retrieve, reuse, revise, and retain [1]. This is a more useful method than the use of an expert system to simulate human thought because proposing a similar case and applying a few modifications seems to be self explanatory and more intuitive to humans .So various case based design support tools have been developed for numerous areas[2-4], such as in injection molding and design, architectural design, die casting die design, process planning, and also in fixture design. Sun used six digitals to compose the index code that included workpiece shape, machine portion, bushing, the 1st locating device, the 2nd locating device and clamping device[5]. But the system cannot be used for other fixture types except for drill fixtures, and cannot solve the problem of storage of the same index code that needs to be retained, which is very important in CBR[6].
1 Construction of a Case Index and Case Library
1.1 Case index
The case index should be composed of all features of the workpiece, which are distinguished from different fixtures. Using all of them would make the operation in convenient. Because the forms of the parts are diverse, and the technology requirements of manufacture in the enterprise also develop continuously, lots of features used as the case index will make the search rate slow, and the main feature unimportant, for the reason that the relative weight which is allotted to every feature must diminish. And on the other hand, it is hard to include all the features in the case index.
Therefore, considering the practicality and the demand of rapid design, the case index includes both the major feature of the workpiece and the structure of fixture. The case index code is made up of 16 digits: 13 digits for case features and 3 digits for case identification number.
The first 13 digits represent 13 features. Each digit is corresponding to an attribute of the feature, which may be one of“*”, “？”, “1”, “2”,…,“A”,“B”,…, “Z”,…, etc. In which, “*” means anyone, “?” uncertain, “0” nothing.
The system rules: fixture type, workpiece shape, locating model cannot be “*”or“?”. When the system is designed, the attribute information of the three items does not have these options, which means the certain attribute must be selected.
The last three digits are the case identification number, which means the 13 digits of the case feature are the same, and the number of these three digits is used for distinguishing them.
The system also rules: “000” is a prototype case, which is used for retrieval, and other cases are “001”,“002”,…, which are used for reference cases to be searched by designers. If occasionally one of them needs to be changed as the prototype case, first it must be required to apply to change the one to “000”, and the former is changed to referential case automatically.
The construction of the case index code is shown in Fig.1.
1.2 Case library
The case library consists of lots of predefined cases. Case representation is one of the most important issues in case based reasoning. So compounding with the index code,.
1.3 Hierarchical form of Case
The structure similarity of the fixture is represented as the whole fixture similarity, components similarity and component similarity. So the whole fixture case library, components case library, component case library of fixture are formed correspondingly. Usually design information of the whole fixture is composed of workpiece information and workpiece procedure information, which represent the fixture satisfying the specifically designing function demand. The whole fixture case is made up of function components, which are described by the function components’ names and numbers. The components case represents the members. (function component and other structure components， main driven parameter, the number, and their constrain relations.) The component case (the lowest layer of the fixture) is the structure of function component and other components. In the modern fixture design there are lots of parametric standard parts and common non standard parts. So the component case library should record the specification parameter and the way in which it keeps them.
2 Strategy of Case Retrieval
In the case based design of fixtures ,the most important thing is the retrieval of the similarity, which can help to obtain the most similar case, and to cut down the time of adaptation. According to the requirement of fixture design, the strategy of case retrieval combines the way of the nearest neighbor and knowledge guided. That is, first search on depth, then on breadth; the knowledge guided strategy means to search on the knowledge rule from root to the object, which is firstly searched by the fixture type, then by the shape of the workpiece, thirdly by the locating method. For example, if the case index code includes the milling fixture of fixture type, the search is just for all milling fixtures, then for box of workpiece shape, the third for 1plane+ 2pine of locating method. If there is no match of it, then the search stops on depth, and returns to the upper layer, and retrieves all the relative cases on breadth.
Retrieval algorithms:
1)According to the case index information of fixture case library, search the relevant case library;
2)Match the case index code with the code of each case of the case library, and calculate the value of the similarity measure;
3)Sort the order of similarity measure, the biggest value, which is the most analogical case.
Similarity between two cases is based on the similarity between the two cases. features. The calculation of similarity measure depends on the type of the feature. The value of similarity can be calculated for numerical values, for example, compareWorkpiece with the weight of 50kg and 20kg. The value can also be calculated between non numerical values, for example, now the first 13 digits index code is all non numerical values. The similarity measure of a fixture is calculated as follows:
where S is the similarity measure of current fixture, n is the number of the index feature, is the weight of each feature, is the similarity measure of the attribute of the i2th feature with the attributeof relative feature of the j-th case in the case library. At the same time, , the value counts as follows:
.
Where is the value of the index attribute of the i-th feature, and is the value of attribute of the relative i-th feature of the j-th case in case library.
So there are two methods to select the analogical fixture. One is to set the value. If the values of similarity measure of current cases were less than a given value, those cases would not be selected as analogical cases. When the case library is initially set up, and there are only a few cases, the value can be set smaller. If there are lots of analogical cases, the value should get larger. The other is just to set the number of the analogical cases (such as10), which is the largest value of similarity measure from the sorted order.
3 Case adaptation and Case Storage
3.1 Case adaptation
The modification of the analogical case in the fixture design includes the following three cases:
1) The substitution of components and the component;
2) Adjusting the dimension of components and the component while the form remains;
3) The redesign of the model.
If the components and component of the fixture are common objects, they can be edited, substituted and deleted with tools, which have been designed.
3.2 Case storage
Before saving a new fixture case in the case library, the designer must consider whether the saving is valuable. If the case does not increase the knowledge of the system, it is not necessary to store it in the case library. If it is valuable, then the designer must analyze it before saving it to see whether the case is stored as a prototype case or as reference case. A prototype case is a representation that can describe the main features of a case family. A case family consists of those cases whose index codes have the same first 13 digits and different last three digits in the case library. The last three digits of a prototype case are always “000”. A reference case belongs to the same family as the prototype case and is distinguished by the different last three digits.
From the concept that has been explained, the following strategies are adopted:
1) If a new case matches any existing case family, it has the same first 13 digits as an existing prototype case, so the case is not saved because it is represented well by the prototype case. Or is just saved as a reference case (the last 3 digits are not “000”, and not the same with others) in the case library.
2) If a new case matches any existing case family and is thought to be better at representing this case family than the previous prototype case, then the prototype case is substituted by this new case, and the previous prototype case is saved as a reference case.
3) If a new case does not match any existing case family, a new case family will be generated automatically and the case is stored as the prototype case in the case library.
4 Process of CBR in Fixture Design
According to the characteristics of fixture design, the basic information of the fixture design such as the name of fixture, part, product and the designer, etc. must be input first. Then the fixture file is set up automatically, in which all components of the fixture are put together. Then the model of the workpiece is input or designed. The detailed information about the workpiece is input, the case index code is set up, and then the CBR begins to search the analogical cases, relying on the similarity measure, and the most analogical case is selected out. If needed, the case is adapted to satisfy the current design, and restored into the case library. The flowchart of the process is shown in Fig.3.
5 Illustrating for Fixture Design by CBR
This is a workpiece (seeFig.4). Its material is 45# steel. Its name is seat. Its shape is block, and the product batch size is middle, etc. A fixture is turning fixture that serves to turn the hole, which needs to be designed.
The value of feature, attribute, case index code and weight of the workpiece is show n in Tab.2.
Through searching, and calculating the similarity, the case index code of the most similar case is 19325513321402000, and the detailed information is show n in Tab. 3.
The similarity is calculated as follows:
So the value of similarity measure of the fixture which needs to be designed with the most analogical case in case library is 0.806, and the structure of the most analogical case is shown in Fig.5.
After having been substituted the component, modified the locating model and clamp model, and adjusted the relative dimension, the new fixture is designed, and the figure is show n in Fig.6.
As there is not the analogical fixture in the case library, the new fixture is restored in to the case library. The case index code is 19325513311402000.
6 Conclusion
CBR, as a problem solving methodology, is a more efficient method than an expert system to simulate human thought, and has been developed in many domains where knowledge is difficult to acquire. The advantages of the CBR are as follows: it resembles human thought more closely; the building of a case library which has self learning ability by saving new cases is easier and faster than the building of a rule library; and it supports a better transfer and explanation of new knowledge that is more different than the rule library. A proposed fixture design framework on the CBR has been implemented by using Visual C ++, UG/Open API in U n graphics with Oracle as database support, which also has been integrated with the 32D parametric common component library, common components library and typical fixture library. The prototype system, developed here, is used for the aviation project, and aids the fixture designers to improve the design efficiency and reuse previous design resources.
中文
應用和發(fā)展
基于實例推理的夾具設計
摘要：基于案例的設計（CBD）方法，夾具相似性體現(xiàn)在兩個方面:功能和結構信息。然后，計算機輔助夾具設計系統(tǒng)是建立在基于案例的推理（CBR），并對工件和夾具結構的主要特征屬性作為案例索引代碼用于檢索類似的情況，并且結構和案例庫的層次關系建立商店，同時，算法在檢索相似案例知識的引導，案例策略適應在離子和案例庫中案例驗證如果貓離子數(shù)是用來區(qū)分相似的案例，該系統(tǒng)在某工程中的應用提高了設計效率，取得了良好的效果。
關鍵詞：基于案例的推理；夾具設計；計算機輔助設計（CAD）
夾具裝置，作為夾持工件的安全的目的地，并且維護方面的工具的一致性關系，而加工。因為夾具的結構取決于產品的特點以及在企業(yè)中的地位的計劃，它的設計制造過程中的瓶頸，制約提高效率和交貨期。夾具的設計是一個復雜的過程，根據(jù)經驗，需要一系列的設計問題包括工件的結構綜合定性知識，制造過程與加工環(huán)境。這也是一個使用傳統(tǒng)的CAD工具時，非常耗費時間的工作（如詞素文字，CATIA、PRO/E），在進行詳細設計的任務是好的，但提供很少的利益利用以往設計經驗和資源，這正是提高效率的關鍵因素?；诎咐耐评恚–BR）方法適應以前解決的情況下為以下四個步驟建立一個新的問題的解決方案：檢索，重用，修改，并保留[ 1 ]。這是一個比一個專家系統(tǒng)模擬人類思維的運用更有用的方法，因為提出類似的案例和應用了一些修改似乎是自我解釋和更直觀的人類。于是各種基于案例的設計支持工具已經開發(fā)了眾多的地區(qū)[ 2-4 ]，如在注射成型設計，建筑設計，壓鑄模的設計，工藝規(guī)劃，并在夾具設計。太陽用六個數(shù)字組成，包括工件形狀，機械部分，該索引編碼套管，第一定位裝置，定位裝置和夾緊裝置第二[ 5 ]。但是該系統(tǒng)不能用于除鉆夾具其他燈具類型，并不能解決同一索引的代碼存儲問題需要被保留，而CBR [ 6 ]是很重要的。
1 一個案例檢索和案例庫的建設
1.1例指數(shù)
案例索引應該由工件的所有特征，是區(qū)別于不同的夾具。使用所有這些會使操作方便。因為地區(qū)的形式是多種多樣的，并在企業(yè)的制造技術的要求也不斷發(fā)展，作為案例索引的許多功能將使搜索速度慢，和主要特點不重要，因為相對權重分配給每個特征，必須減少。另一方面，它是很難包括案例索引的所有功能。
因此，從實用化、快速的設計需求，案例索引包括工件的主要特征和夾具結構。案例索引代碼由16位數(shù)：13位數(shù)的情況下識別號案件的特點和3個數(shù)字。
前13個數(shù)字代表13個特點。每個數(shù)字對應的特征的屬性，這可能是一個“*”，“？”，“1”，“2”，……，“A”，“B”，……，“Z”，……，等等。其中，“*”是指任何人，”？“不，”0“沒有什么。
系統(tǒng)規(guī)則：夾具，工件形狀，定位模型不能“*”或“？“。當系統(tǒng)的設計，該三個項目的屬性信息，沒有這些選項，這意味著一定的屬性必須選擇。
最后三位數(shù)字是如此的識別號碼，即案例特征的13位數(shù)字是相同的，和這三個數(shù)字的位數(shù)是用來區(qū)分。
該系統(tǒng)還規(guī)定：“000”是一個原型的情況下，這是用于檢索，和其他案件的“001”，“002”，……，這是用于要搜索的設計者參考案例。如果偶爾他們當中的一個需要改變?yōu)樵偷那闆r下，首先必須將改變一個“000”，和前改為自動參考案例。
的情況下，指數(shù)代碼結構如圖1所示。
1.2例庫
案例庫包含預定義的眾多案例。案例的表示是基于案例推理的最重要的問題。所以復合指標代碼，。
1.3個層次的案例
夾具的結構相似性表示作為整個夾具的相似性，相似性和相似性成分組成。所以整個夾具實例庫，組件的案例庫，案例庫的夾具元件形成相應。通常整個夾具設計信息是由工件信息和工件程序的信息，這是專門設計滿足功能需求的夾具。整個夾具實例是由功能部件，這是由功能部件的名稱和編號描述。組件的情況下代表成員。（功能組件和其他結構部件，主要驅動參數(shù)，數(shù)量，和他們的約束關系。）的組件的情況下（夾具的最低層）的功能組件和其他組件的結構。在現(xiàn)代燈具設計有參數(shù)化標準件和常用的非標準件的大量。這樣的組件的情況下，圖書館應記錄，它使他們這樣的規(guī)格參數(shù)。
2.案例檢索策略
基于案例的夾具設計，最重要的是相似性檢索，以獲得最相似的情況下，和降低的時間適應。根據(jù)夾具的設計要求，案例檢索策略相結合的方式，最近的鄰居和知識引導。那是，深度優(yōu)先搜索，然后在廣度；知識引導策略意味著對從根到對象的知識規(guī)則的搜索，這是由夾具類型首先搜索，然后通過工件的形狀，然后通過定位方法。例如，如果指數(shù)代碼包括夾具式銑床夾具，搜索是為所有的銑削夾具，然后箱工件形狀，對1plane + 2pine定位方法第三。如果沒有匹配的話，那么對深度搜索停止，并返回上一層，和檢索所有相關案件的廣度。
檢索算法：
1）根據(jù)夾具的案例庫的案例索引信息，搜索相關案例庫；
2）的情況下，指數(shù)代碼與每個案例的案例庫代碼匹配，并計算出的值的相似性度量；
3）的相似性度量的順序，最大的價值，這是最相似實例。
兩起案件之間的相似性是基于兩個案例之間的相似性。特征。相似性度量的計算取決于類型的特征。相似的值可以計算出的數(shù)值，例如，與50公斤的重量compareworkpiece 20公斤。值也可以計算非數(shù)值之間，例如，現(xiàn)在第一個13位數(shù)代碼都是非數(shù)值索引。一個夾具的相似性度量的計算如下：
其中S是當前夾具的相似性度量，n是指數(shù)的特征數(shù)量，是每個特征的重量，是用在案例庫中案例屬性的第j個相對特征的i2th特征屬性的相似性度量。同時，，價值數(shù)如下：
的第i個特征指標屬性的價值在哪里，是在案例庫的第j下相對i特征屬性的值。
所以選擇類比夾具的兩種方法。一是要設置的值。如果目前的情況下，相似度量值均小于給定值，這些案件將不被選擇作為類比案例。當案例庫的初步建立，只有少數(shù)情況下，該值可設定較小。如果有相似事例很多，應該得到更大的價值。另一個是建立類推的案件數(shù)量（如10），這是最大的值的排序順序的相似性度量。
3.例適應和案例存儲
3.1例適應
在夾具設計的相似實例的修改包括以下三例：
1）組件的替代和組件；
2）調整組件的尺寸和成分而形成仍然；
3）模型的設計。
如果零件和夾具組件是常見的對象，他們可以編輯和刪除，替換工具，已被設計。
3.2例存儲
在案例庫中保存新的夾具實例之前，設計者必須考慮的是節(jié)約寶貴的。如果不增加系統(tǒng)的知識，不需要存儲在案例庫。如果它是有價值的，那么設計師就必須分析它，拯救它是否是存儲為原型的情況下，或作為參考的情況下在。一個原型是一個表示可以描述個案家庭的主要特點。一例家族是指那些案件指標代碼庫中的情況下，具有相同的前13個數(shù)字和不同的最后三位數(shù)字。一個原型的情況下，最后三位數(shù)字是“000”。參考的情況下，屬于同一家族的原型的情況下，由不同的最后三位杰出的。
從已解釋的概念，采用以下策略：
1）如果一個新的案例匹配任何存在的情況下，家庭，它具有相同的前13個數(shù)字作為一個現(xiàn)有的原型實例，所以并不是因為它是由原型的情況下，很好的體現(xiàn)?；蛑皇亲鳛橐粋€參考案例（最后3位數(shù)字是不是“000”，并與別人不一樣）在案例庫。
2）如果一個新的案例匹配任何存在的情況下，家庭和被認為是更好的代表這種情況下的家庭比以前的原型實例，然后原型以新案子取代，和以前的原型的情況下被保存作為參考的情況下。
3）如果一個新的案件不符合任何現(xiàn)有情況的家庭，一個新的案例的家庭將自動生成和案例存儲在案例庫的原型實例。
4 CBR的夾具設計過程
根據(jù)夾具設計的特點，對夾具設計如夾具，部分的名稱的基本信息，產品設計師，必須先輸入等。然后夾文件自動設置，其中所有的夾具組件放在一起。然后對工件的模型輸入或設計。輸入的工件的詳細信息的情況下，指數(shù)代碼設置，然后開始搜尋相似案例推理，依靠的相似性度量，并篩選出最相似實例。如果需要的話，情況來滿足當前的設計，并恢復到案例庫。該過程的流程圖如圖3所示。
5CBR的夾具設計說明
這是一個工件（seefig。4）。材料為45 #鋼。它的名字是座。它的形狀是塊，和產品的批量大小中等，等。一個夾具，夾具，用于把車孔，需要設計。
特征，屬性值，指數(shù)代碼和工件的重量在選項卡顯示2 N。
通過搜索，計算相似度，最相似的案例的情況下，指數(shù)代碼為19325513321402000，和詳細信息顯示在標簽3
相似度的計算方法如下：
因此，需要設計的最為相似的案例案例庫的夾具相似性度量的值是0.806，和最相似實例的結構如圖5所示。
經取代的組件，修改了定位模型和夾具模型，和調整的相對尺寸，設計新的夾具，和圖顯示在圖6。
因為沒有在案例庫中相似的夾具，夾具的新恢復的案例庫。的情況下，指數(shù)代碼為19325513311402000。
6 結論
CBR，作為一個解決問題的方法，是一種比模擬人類思維的專家系統(tǒng)更有效的方法，并已在許多領域的知識是很難獲得的開發(fā)。案例推理方法的優(yōu)點如下：它類似于人類的思想更加緊密；一個案例庫，通過節(jié)約新病例的自我學習能力更容易，比一個規(guī)則庫的建設速度更快的建設；并支持更好的轉移和新知識的解釋，比規(guī)則庫的不同。提出了一種對CBR的夾具設計框架已用Visual C++實現(xiàn)，在U N圖形以Oracle為數(shù)據(jù)庫支持UG/Open API，也已與三維參數(shù)化通用組件庫集成，通用組件庫和典型夾具庫。這里開發(fā)的原型系統(tǒng)，，用于航空項目，和艾滋病的夾具設計人員提高設計效率和重用以前的設計資源。