Many manufacturing enterprises have large collections of solid models and text-based assembly processes to support assembly operations. These data are often distributed across their extended enterprise. As these enterprises expand globally, there is often an increase in product and process variability which can often lead to challenges with training, quality control, and obstacles with change management to name a few. Thus, there is a desire to increase the consistency of assembly work instructions within and across assembly locations.
The objective of this research is to retrieve existing 3d models of components and assemblies and their associated assembly work instructions. This is accomplished using 3d solid model similarity and text mining of assembly work instructions.
Initially, a design study was conducted in which participants authored assembly work instructions for several different solid model assemblies. Next, a geometric similarity algorithm was used to compute similarity scores between solid models and latent semantic analysis is used to compute the similarity between text-based assembly work instructions. Finally, a correlation study between solid model-assembly instruction tuples is computed. A moderately strong positive correlation was found to exist between solid model similarity scores and their associated assembly instruction similarity scores. This indicates that designs with a similar shape have a similar assembly process and thus can serve as the basis for authoring new assembly processes. This aids in resolving differences in existing processes by linking three-dimensional solid models and their associated assembly work instructions.