【導(dǎo)讀】由于塑料具有許多金屬和其它非金屬所無法比擬的優(yōu)點,所以近年來發(fā)展尤為迅速。在現(xiàn)代工業(yè)和日用產(chǎn)品中，越來越多的塑料制品代替了以往的多類產(chǎn)品，而模具作為塑件的成型技術(shù)也得到了迅速發(fā)展。本次畢業(yè)設(shè)計就是在Pro/ENGINEER平臺上對速度表主動齒輪進行的注射模具設(shè)計。塑料注射模是模具中的一種，也是當(dāng)今市場最常用、最具發(fā)展前景的模具之一。通過本次設(shè)計，不僅掌握了注射模具的基本設(shè)計過程和設(shè)計要領(lǐng)，并了解了塑料的基本成型工藝，以及軟件Pro/ENGINEER的實體建模、裝配建模、制圖基礎(chǔ)、工過程動畫設(shè)計等各個環(huán)節(jié)的基本操作規(guī)則及運用方法，從而可以利用軟件Pro/ENGINEER或其它三維設(shè)計軟件完成模具的整體設(shè)計。千萬不要刪除行尾的分節(jié)符，此行不會被打印。在目錄上點右鍵“更新域”，然后“更新整個目錄”。打印前，不要忘記把上面“Abstract”這一行后加一空行

　　

【正文】 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。 position degree is divided there conditions。 tolerance grade is divided into there conditions。 Add up to ten conditions. Where condition is satisfied, its value is . Otherwise, its value is . Four output knots represent drilling, reaming, subfinish boring and finish boring respectively. Hidden level has nine knots. Table 1 is a part of learning sample to train BP network. Many neural network modules form neural network array to represent selection knowledge and link with the main system. Knowledge base and inference stratery of neural network bine tow into one. The knowledge representation of neural network is hidden. It’ inference stratery is realized by parallel putation.Table 1 The part learning sample to train BP networkInput knotoutput knot1234567891012344. Inference stratery The problem which should be solved in inference is: in every state of problem solution, how control the selection and application of knowledge, or determine control stratery and inference way.The process design task is divided into many subtasks to form a task tree in this paper. The hierarchy structure of inference is formed by the hierarchy program method, and the knowledge unit too solve subtask is found by the task control mechanism of inference. Its process is shown in .Because of the introduction of neural network and the realization of parallel mechanism the system has solved the problems such match conflict and bination explosion etc.5. Conclusion 1).The description of workpiece is plete, including geometry and manufacturing information. It is used not only in CAD, CAPP but also NC.2).According to characteristic of process design, the system employs new representation of multiknowledge, and realizes an organic bination of artificial intelligence and neural network.3).The hierarchy inference stratery provides convenience for realizing explanation of CAPP expert system. StartWorkpiece inforfationformation Knowledge base: frame structure production rule neural network Resolution of taskEngineering Data base YTranfer knowledge unit of subtask Is the task solve? NFailureDraw the knowledge of conclusion Put conclusion into the current data basesuccess The process diagram of the forward the inferenceReferences1 王先逵等. 2 3. Vant Er