Research On Design Expert System Of Hydraulic Excavator

The design process of an excavator is complex and time-consuming. Because the features of design knowledge are large amounts of knowledge, complex relevance between constraints and variables and many numerical computations, this kind of knowledge is hard to represent as the knowledge in decision and detection systems. In those systems the production rule method was usually used. This thesis firstly collects the excavator design knowledge and analyses the features of the knowledge, then presents some new knowledge representation methods. Rule matrix is used to represent the reasoning control knowledge which is used in the first design stage. Relevance matrix is used to represent the empirical knowledge that is used in the redesign stage. An excavator design expert system is developed based on these theories. By this system, the excavator designer can save lots of time and gets a feasible scheme effectively. The full text is divided into the following five chapters:The first chapter elaborates the research background and significance of this project. Furthermore, it summarizes the research status in excavator, mechanical design expert system, knowledge representation and reasoning fields, then points out the lack of research on excavator design expert system. Thus the main research contents of this project are made clear.The second chapter collects the knowledge used for excavator design and divides it into several kinds, then analyses the features of the knowledge. It is hard to represent these kinds of knowledge by the traditional knowledge representation methods. A hybrid knowledge representation method is present to represent the above knowledge.The third chapter describes the inference machine of the excavator design system. Flexible design can achieve by the rule matrix. The correctness of the rule matrix and the rationality of the relevance matrix are proved. An improved reasoning control strategy is present to suit for the irrational relevance matrix.In chapter four, the frame of the expert system is established. The whole system including knowledge base, inference machine, and interpretation system is implemented. A checking system is developed to show the performance of the designed scheme graphically. A design example of a 76T excavator is present to verify the feasibility and efficiency of this system.Chapter five concludes the whole paper, and some suggestions on further research are followed.