An intelligent method to generate liaison graphs for truss structures

Abstract

Liaison graph is a necessary prerequisite of assembly sequence planning for mechanical products. Traditionally, it is generated via shape matching of joints among parts, but this strategy is invalid to truss structures because they lack patterns for shape matching. In this context, this article presents an intelligent method based on support vector machine to obtain liaison graphs of truss products automatically. This method defined three kinds of oriented bounding boxes to embody the relationships of the joints in truss structures. Based on them, a series of factors are deduced as training data for support vector machine. Furthermore, two algorithms are introduced to calculate oriented bounding boxes to facilitate the data extraction. By these processes, this method established the knowledge of joints and realized the intelligent construction of liaison graph without shape matching reasoning. To verify the effect of the method, an experimental implementation is presented. The results suggest that the proposed method could recognize most joint types and construct liaison graph automatically with sufficient sample training. The correct recognition rate is more than 85%. Comparing with back-propagation neural network, support vector machine is more accurate and stable in this case. As an alternative method, it could help the engineers to arrange the assembly plan for truss structures and other similar assemblies.