Research On Machining Feature Recognition Based On Delta-volume Decomposition And Optimal Combination

Automatic recognition technology of machining features is an important part of CAD/CAPP integration,has aroused the attention and research of many scholars for many years.However,there continue to be some problems in the existing feature recognition methods.The reason is processing of the feature intersection,which is easy to cause multiple interpretation problems.In addition,current feature recognition methods rarely consider the actual machining condition,which causes the identified features cannot be processed.Aiming at the status quo of feature recognition technology,this paper proposes a feature recognition method based on delta-volume decomposition and optimal combination for turning parts and milling parts,and gives the classification scheme of turning features and milling features combined with actual machining conditions.Based on this scheme,feature recognition operations of delta-volume generation and decomposition,cutting unit search and optimal combination,combined feature description and matching are studied.All work of the paper is as follows:(1)Based on the development status of CAPP technology,the definitions and related concepts of existing feature classification and feature recognition methods are briefly introduced.With their shortcomings expounded,the feature recognition method based on delta-volume decomposition and optimal combination is introduced,and the detail of its feature recognition concept,process framework and processing module is showing.(2)A specific process framework for feature identification of turning parts is proposed.Through constructing the rotating 2D mesh model of the delta-volume,the genetic algorithm is used to optimize the feature parameters which affecting the machining process in the feature search process,and then the optimal combination features are obtained.A classification scheme for turning features is constructed and a turning feature matching method based on turning feature descriptors is proposed.(3)A specific process framework for the feature recognition of milling parts is proposed.Firstly,the 3D solid model of the milling delta-volume is generated,and the rule-based method is used to decompose the hole class features,then the single feature in the current delta-volume is identified,the 3D mesh model is constructed for the remaining delta-volume,and the optimal combination feature is obtained by the optimization algorithm.The milling feature classification scheme is constructed and the milling feature matching method based on the milling feature non-machined surface adjacency matrix is proposed.(4)Based on UG-NX8.5 modeling platform,the feature recognition system of this paper is built by using NXOpen-API.The development environment and the project configuration of the system are described,and feature recognition function modules such as delta-volume generation and decomposition,cutting unit search and optimization combination and feature classification are realized.The practice shows that the machining feature recognition method based on the delta-volume decomposition and optimal combination can solve the problem of intersecting feature recognition.And using the optimal combination to generate the optimal combination feature,which avoids the problem of multiple interpretation of features and effective realization of automatic recognition of machining features.