Intelligent Reasoning And Measurement Point Optimization Of Geometric Error Measurement Scheme Based On Ontology

Nowadays,geometric error measurement is basically manual selection of measurement solutions.The quality of measurement depends heavily on the level and experience of the measurement personnel.For the same part,different measurement personnel may choose different measurement solutions,which leads to an increase in the cost of part measurement and ultimately affects the part acceptance.In addition,the existing tolerance representation model is still at the stage of tolerance design without realizing integration with measurement information.It still relies on humans to understand the engineering semantics of tolerances.For the extraction methods given by the standard,current research methods provide more measurement points,and ultimately cannot meet the needs of intelligent measurement.In response to this need to reduce the uncertainty in the selection of measurement schemes.This article will introduce the latest research results in the field of artificial intelligence Ontology technology.A method of intelligent reasoning and measurement point optimization based on the ontology-based geometric error measurement solutions is proposed.The main research contents are as follows.Construct the information representation of the measurement solutions.Based on the existing representation model,an information representation model with seven hierarchical structures is established.Analyze and summarize the information in each layer,and then use the adjacency matrix to abstractly describe the relationship between each layer and between layers.This model lays the foundation for the intelligent reasoning of the basic measurement solutions.An intelligent reasoning method based on ontology-based geometric error basic measurement solutions is proposed.First,on the basis of the information representation model,construct the ontology model of the basic measurement solutions that can be extracted and explained by the computer,which includes the use of description logic assertion formulas to describe the matching relationship of the part layer,the structural relationship of the geometric entity layer,and the element layer contact relationship,the association relationship between the labeling layer and the element layer.Using descriptive logic terms and formulas to describe the labeling information,measurement solutions information and tolerance zone information.Secondly,the generation relationship of the tolerance zone,the mapping relationship between the measurement solutions and the parts are transformed into Jena rules.Again,the tolerance zone and the basic measurement solutions are obtained by intelligent inference of the Jena inference engine.Finally,the effectiveness of the proposed method is verified by an example.An optimization method for measuring points of geometric error measurement is proposed.On the basis of the extraction method obtained by reasoning.Firstly,the error source of the actual part is analyzed,and the dimensional error of the actual part is obtained.Secondly,according to the engineering semantics of dimensional error and straightness tolerance,a simulation function of the surface shape of the part is constructed.Then,according to the principle of error theory,the relationship between the measurement distance and the error evaluation value is analyzed on the basis of the simulation function,so as to construct the accuracy function of the measuring point set.Thirdly,on this basis,for the given tolerance and processing method,the optimal number of measuring points is adaptively generated based on the Monte Carlo simulation principle.Finally,two examples are used to verify the accuracy and effectiveness of the proposed method.Compared with the theoretical method,the number of measurement points in the axial and radial directions of Example 1(Example 2)is reduced by 1994(226)and 42(396)respectively.The proposed method improves the measurement efficiency under the condition of satisfying accuracy.Develop the intelligent reasoning prototype system of the measurement solutions.The intelligent reasoning prototype system of the measurement solutions is developed in the Eclipse environment using Java language.The main functional modules of this prototype system include the construction of the ontology model,the intelligent reasoning of the tolerance zone,the intelligent reasoning of the measurement solutions and the optimization of measurement points.Finally,an example is used to verify the effectiveness of the system.