An Intelligent Approach to Identifying and Setting Dual-Level Assembly Semantics
Assembly semantics,as a vital content of an assembly model,is mainly set interactively by designers,which is usually time-consuming and inefficient.To account for this,an intelligent approach to identifying and setting dual-level assembly semantics is proposed.First,the existing graph attention network is improved where it is extended to a dual-level identification network,identifying all typical kinematic pair interfaces on each part model that have various geometric shapes but consistent kinematic semantics.After that,the existing back-propagation artificial neural network structure is modified for improving performance,recognizing all as-sembly constraint types(as well as their associated geometric entities)that are embodied in each kinematic pair interface.Based on the above-identified information,the mating kinematic pair interfaces and mating assem-bly-constraint geometric entities between arbitrary two-part models can be searched automatically,and the full assembly semantics between them can be rapidly and semi-automatically set.To train the aforementioned net-work,a dataset containing 2787 CAD models is generated.Experiments show that the accuracy of identification on the kinematic pair interface or on the assembly constraint type(as well as its associated geometric entity)is more than 93.0％.Besides,compared to recent related works,the proposed approach also has some advantages and potentials to set the assembly semantics for various assembly models rapidly.
万方数据
维普
