Representation: Structural Complexity of Assemblies to Create Neural Network Based Assembly Time Estimation Models

Abstract

Assembly time estimation is traditionally a time intensive manual process requiring detailed geometric and process information to be available to a human designer. As a result of these factors, assembly time estimation is rarely applied during early design iterations. This paper explores the possibility that the assembly time estimation process can be automated while reducing the level of design detail required. The approach presented here trains artificial neural networks (ANNs) to estimate the assembly times of vehicle sub-assemblies at various stages using properties of the connectivity graph at that point as input data. Effectiveness of estimation is evaluated based on the distribution of estimates provided by a population of ANNs trained on the same input data using varying initial conditions. Results suggest that the method presented here can complete the time estimation of an assembly process with +/− 15% error given an initial sample of manually estimated times for the given sub-assembly.