Speeding Up Assembly Sequence Planning Through Learning Removability Probabilities

Abstract

Industry 4.0 facilitates a high number of product variants, posing significant challenges for modern manufacturing. One of them is the automatic creation of assembly sequences. This can be achieved with the assembly-by-disassembly (AbD) approach, which is currently highly inefficient. We aim at speeding up AbD by leveraging deep learning. AbD relies on iteratively testing parts for removal, which makes the order in which parts are tested highly relevant for its run-time. We optimize this order by training a graph neural network (GNN) based on the shape of parts and the shape of local part connections. For each part, it predicts a removability probability. We use these probabilities to optimize the order in which parts are tested for removal. This reduces the number of parts tested by approximately 64%-90%, depending on the tested product. Further improvements are achieved by combining our approach with bookkeeping, another approach for speeding up AbD. Finally, we separately analyze the impact of the parts and their connections on the removability probabilities predicted by the GNN. We found that most of the important information regarding a part's removability can be derived from its connections alone.