Complexity Connectivity Metrics – Predicting Assembly Times with Low Fidelity Assembly CAD Models

Abstract

Expanding on previous work to predict assembly times from detailed assembly models, low fidelity part models are used in a series of predictive performance experiments. Results reveal that this tool can predict the assembly time of a product to within 40% of the target “as built” time using a high fidelity neural network and a low fidelity CAD model. The tool is based on structural complexity, representing the assembly graph as complexity vector of 29 metrics. The graphs are automatically compiled from examining part proximity (interference checks) regardless of the choice of mating constraints used in the modeling. A neural network is then used to build a relationship between the complexity vector (input) and the assembly time (output). Low-fidelity models can be used to predict assembly times, thereby supporting earlier inclusion of design for assembly methods in the design process.