Community Detection to Split Large-scale Assemblies in Subassemblies

The motivation for the preprocessing of large-scale CAD models for assembly-by-disassembly approaches. The assembly-by-disassembly is only suitable for assemblies with a small number of parts (n_{parts} < 22). However, when dealing with large-scale products with high complexity, the CAD models may not contain feasible subassemblies (e.g. with connected and interference-free parts) and have too many parts to be processed with assembly-by-disassembly. Product designers' preferences during the design phase might not be ideal for assembly-by-disassembly processing because they do not consider subassembly feasibility and the number of parts per subassembly concisely. An automated preprocessing approach is proposed to address this issue by splitting the model into manageable partitions using community detection. This will allow for parallelised, efficient and accurate assembly-by-disassembly of large-scale CAD models. However, applying community detection methods for automatically splitting CAD models into smaller subassemblies is a new concept and research on the suitability for ASP needs to be conducted. Therefore, the following underlying research question will be answered in this experiments:

Underlying research question 2: Can automated preprocessing increase the suitability of CAD-based assembly-by-disassembly for large-scale products?

A hypothesis is formulated to answer this research question, which will be utilised to design experiments for hypothesis testing.

Hypothesis 2: Community detection algorithms can be applied to automatically split large-scale assemblies in suitable candidates for CAD-based AND/OR graph generation.}