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.

零件集成是将零件集成为一个制造和装配单元。它可以有效地减少产品的制造和组装单位数量，被认为是提高制造生产率的有效途径。尽管增材制造 (AM) 具有进一步促进任何产品（装配）模型的零件集成的巨大潜力，但目前使用增材制造的零件集成工作通常是临时的、依赖于人的且耗时的。此问题的一个主要原因是确定装配模型中的哪些零件可以自动集成为制造和装配单元仍然非常困难，尤其是当模型具有运动学（由运动学关节体现的内部相对运动）时。在本文中，提出了一种新颖的零件聚类方法，基于该方法，输入装配模型可以根据其运动学将其所有零件巧妙地聚类到更少的子装配模型（每个子装配模型都适合集成为 AM 中的制造和装配单元）。为了确保零件集成后的输入模型能够使用 AM 有效实现其运动学，首先定义零件聚类的标准。结合标准，提出了基于启发式规则确定每个零件的运动学相关制造方向的方法。然后，为了进行准确有效的零件聚类，根据上述标准提出了属性零件运动图。之后，通过突破密封支撑结构和装配可行性方面的检测自动化挑战，基于上述标准和图形定义了一个有效的优化目标函数。最后，将新的自适应扰动策略集成到粒子群优化算法中以避免早熟，设计了一种新颖的基于图的部分聚类优化方法，将输入模型的所有部分聚类为高质量（优化）的集合上述子装配模型。提出了实验和分析来验证所提出方法的优点。此外，遵守 AM 的一般准则，所提出的方法提供了在更广泛的应用中使用 AM 最大化零件集成的巨大潜力。设计了一种新颖的基于图的零件聚类优化方法，将输入模型的所有零件聚类为上述子装配模型的高质量（优化）集。提出了实验和分析来验证所提出方法的优点。此外，遵守 AM 的一般准则，所提出的方法提供了在更广泛的应用中使用 AM 最大化零件集成的巨大潜力。设计了一种新颖的基于图的零件聚类优化方法，将输入模型的所有零件聚类为上述子装配模型的高质量（优化）集。提出了实验和分析来验证所提出方法的优点。此外，遵守 AM 的一般准则，所提出的方法提供了在更广泛的应用中使用 AM 最大化零件集成的巨大潜力。