A kinematics-aware part clustering approach for part integration using additive manufacturing

Part integration is to integrate parts to be a fabrication and assembly unit. It can effectively reduce the fabrication and assembly unit quantity of a product and has been deemed as an effective way to promote the productivity of manufacturing. Although additive manufacturing (AM) has great potential to further promote the part integration for any product (assembly) model, part integration works using AM at present are often ad hoc, human-dependent and time-consuming. One main cause for this problem is that determining which parts in an assembly model can be integrated to be a fabrication and assembly unit automatically is still very difficult, especially when the model has kinematics (inner relative motions embodied by kinematic joints). In this paper, a novel part clustering approach is proposed, based on which, an input assembly model can smartly cluster all its parts to fewer sub-assembly models (each of them fits being integrated to be a fabrication and assembly unit in AM) according to its kinematics. To ensure that the input model after part integration can effectively realize its kinematics using AM, the criteria for part clustering are first defined. Accompanying with the criteria, the methods to determine the kinematics-related fabrication orientation for each part are proposed based on heuristic rules. Then, to make an accurate and efficient part clustering, an attributed part kinematic graph is put forward according to the above criteria. After that, by breaking through the detection automation challenges in sealing support structure and assembly feasibility, an efficient optimization objective function is defined based on the above criteria and graph. Finally, integrating a new adaptive perturbation strategy into the particle swarm optimization algorithm to avoid premature convergence, a novel graph-based part clustering optimization method is designed to cluster all the parts of the input model to be a high-quality (optimized) set of the above-mentioned sub-assembly models. Experiments and analyses are presented to verify the advantages of the proposed approach. Besides, complying with the general guidelines in AM, the proposed approach provides great potential to maximize part integration using AM in a wider application.