外文翻译---基于事例推理的夹具设计研究与应用

附录一

英文文献

Application and development Of case based reasoning in fixture design

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, i2th feature with the attributetime,

is the similarity measure of the attribute

of the

of relative feature of the j-th case in the case library. At the same

, 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.