Xiao Xinyi, Xiao Hanbin
Mechanical and Manufacturing Engineering Department, Miami University, Oxford, OH 45069, USA.
School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China.
Materials (Basel). 2021 Dec 29;15(1):247. doi: 10.3390/ma15010247.
Robotic additive manufacturing (AM) has gained much attention for its continuous material deposition capability with continuously changeable building orientations, reducing support structure volume and post-processing complexity. However, the current robotic additive process heavily relies on manual geometric reasoning that identifies additive features, related building orientations, tool approach direction, trajectory generation, and sequencing all features in a non-collision manner. In addition, multi-directional material accumulation cannot ensure the nozzle always stays above the building geometry. Thus, the collision between these two becomes a significant issue that needs to be solved. Hence, the common use of a robotic additive is hindered by the lack of fully autonomous tools based on the abovementioned issues. We present a systematic approach to the robotic AM process that can automate the abovementioned planning procedures in the aspect of collision-free. Typically, input models to robotic AM have diverse information contents and data formats, hindering the feature recognition, extraction, and relations to the robotic motion. Our proposed method integrates the collision-avoidance condition to the model decomposition step. Therefore, the decomposed volumes can be associated with additional constraints, such as accessibility, connectivity, and trajectory planning. This generates an entire workspace for the robotic additive building platform, rotatability, and additive features to determine the entire sequence and avoid potential collisions. This approach classifies the uniqueness of autonomous manufacturing on the robotic AM system to build large and complex metal components that are non-achievable through traditional one-directional AM in a computationally effective manner. This approach also paves the path in constructing an in situ monitoring and closed-loop control on robotic AM to control and enhance the build quality of the robotic metal AM process.
机器人增材制造（AM）因其具有连续可变的构建方向的连续材料沉积能力而备受关注，这减少了支撑结构的体积和后处理的复杂性。然而，当前的机器人增材制造过程严重依赖于人工几何推理，这种推理以无碰撞的方式识别增材特征、相关的构建方向、工具接近方向、轨迹生成以及对所有特征进行排序。此外，多方向材料堆积无法确保喷嘴始终保持在构建几何形状上方。因此，这两者之间的碰撞成为一个需要解决的重大问题。因此，由于上述问题导致缺乏完全自主的工具，阻碍了机器人增材制造的普遍应用。我们提出了一种用于机器人增材制造过程的系统方法，该方法可以在无碰撞方面自动执行上述规划程序。通常，机器人增材制造的输入模型具有多样的信息内容和数据格式，这阻碍了特征识别、提取以及与机器人运动的关系。我们提出的方法将碰撞避免条件集成到模型分解步骤中。因此，分解后的体积可以与诸如可达性、连通性和轨迹规划等附加约束相关联。这为机器人增材制造平台生成了一个完整的工作空间、旋转性和增材特征，以确定整个序列并避免潜在碰撞。这种方法在机器人增材制造系统上对自主制造的独特性进行分类，以计算有效的方式构建通过传统单向增材制造无法实现的大型复杂金属部件。这种方法还为在机器人增材制造上构建原位监测和闭环控制以控制和提高机器人金属增材制造过程的构建质量铺平了道路。
