论文标题
混合制造过程计划任意零件和工具形状
Hybrid Manufacturing Process Planning for Arbitrary Part and Tool Shapes
论文作者
论文摘要
混合制造(HM)技术在多模式过程计划中结合了添加剂和减法制造(AM/SM)功能,以利用每个过程的优势。尽管对HM技术的兴趣越来越大,但用于过程规划的软件工具尚未赶上硬件的进步,并且通常会限制限制设计和制造工程师系统地探索完整的设计和过程计划空间的能力。我们提出了一个通用框架,用于识别基于可访问性和支持要求构成HM过程计划的AM/SM操作,并使用允许考虑任意部分和工具几何形状的形态操作。为了利用多模式,我们定义了允许暂时过多的材料沉积或去除的措施,并了解了随后的动作可以为它们纠正,与单次单调的单次(仅AM仅或仅SM-SM-SM-SM-SM-NOMELLY)过程不同。我们使用此框架来生成一个有效的,潜在的非单调的,可能是任意形状的特定部分的过程计划,任意形状的AM/SM工具的集合,以及一组相对旋转(为每个动作固定),代表$ 3- $ AXIS机器上的构建/固定指示。最后,我们使用一个简单的目标函数来量化材料成本和工作时间的成本和操作时间,并使用搜索算法来探索有效过程的指数较大空间,以找到“成本优势”的解决方案。我们证明了我们方法在3D示例中的有效性。
Hybrid manufacturing (HM) technologies combine additive and subtractive manufacturing (AM/SM) capabilities in multi-modal process plans that leverage the strengths of each. Despite the growing interest in HM technologies, software tools for process planning have not caught up with advances in hardware and typically impose restrictions that limit the design and manufacturing engineers' ability to systematically explore the full design and process planning spaces. We present a general framework for identifying AM/SM actions that make up an HM process plan based on accessibility and support requirements, using morphological operations that allow for arbitrary part and tool geometries to be considered. To take advantage of multi-modality, we define the actions to allow for temporary excessive material deposition or removal, with an understanding that subsequent actions can correct for them, unlike the case in unimodal (AM-only or SM-only) process plans that are monotonic. We use this framework to generate a combinatorial space of valid, potentially non-monotonic, process plans for a given part of arbitrary shape, a collection of AM/SM tools of arbitrary shapes, and a set of relative rotations (fixed for each action) between them, representing build/fixturing directions on $3-$axis machines. Finally, we use define a simple objective function quantifying the cost of materials and operating time in terms of deposition/removal volumes and use a search algorithm to explore the exponentially large space of valid process plans to find "cost-optimal" solutions. We demonstrate the effectiveness of our method on 3D examples.