Representation: Extracting Mate Complexity From Assembly Models to Automatically Predict Assembly Times

The work in this paper uses neural networks to develop a relationship model between assembly times and complexity metrics applied to defined mate connections within SolidWorks assembly models. This model is then used to develop a Design for Assembly (DFA) automation tool that can predict a product’s assembly time using defined mate connections within SolidWorks assembly models. The development of this new method consists of: creating a SolidWorks (SW) Add-in to automatically extract the mate connections from SW assembly models, parsing the mate connections into graphs, implementing a new complexity training algorithm to predict assembly times based on mate graphs, and evaluating the effectiveness of the new method. The motivation, development, and evaluation of the new automated DFA method are presented in this paper. Ultimately, the method that is trained on both fully defined and partially defined assembly models is shown to provide assembly time prediction results that are typically within 25% of target time, but with one outlier at 95% error, suggesting that a more robust training set is needed.Copyright © 2012 by ASME