Fault Tolerant Algorithms for In-orbit Manufacture

在轨制造的容错算法

• 批准号: 2488842
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2488842
• 关键词: Fault; Tolerant; Algorithms; orbit; Manufacture

There is a large drive for in-orbit manufacturing to help realise ambitious large-scale missions to the moon and beyond, and for use in space-based services such as telecommunications and Earth observation. However, there are several issues within the field related to the detection of failures within the manufacturing process and the autonomous assembly of these structures directly on orbit.Investigating methods for the failure detection and recovery of objects in manufacture within a space environment is the prime focus for this project. Effort will be made to determine if the use of physical sensors, machine vision, and other inputs can correctly detect the different failure modes of a 3D printed object, such as delamination and surface/structural defects. This would require simulation and comparison of both the dynamics of the printer throughout the printing process, and the printed object itself at any stage for visual comparison. This initial body of work will inform the rest of the project. It is expected that machine vision will be needed to detect non-printer related faults. There is only limited research available within this area. It is envisioned that a combination of physical sensors on the printer and the machine vision algorithms will be able to detect faults and defects within the printed objects in real-time.Once a robust method for detecting faults has been developed, methods to evaluate and recover the printing object can be investigated; such as the removal of stray material via robotic arm, or addition of material in the failure region. Again, there is limited work available in this area and there are multiple solutions that can be envisioned. Initial efforts will be based on the severity of the fault. For example, small imperfections, such as warping, may be recoverable in real-time with the addition of single layers to compensate. Algorithms for deciding on the severity of the error and how the error can be recovered will be a significant output of this work.Recovery of an object requires significant research into simulation techniques to determine which, if any, methods will allow a print to continue. Optimisation algorithms can be used to establish what the optimal method of recovery should be, while, for more serious defects, the use of a robotic manipulators could remove affected regions of an object for reprinting.Since target application is intended to support in-orbit manufacturing, the extreme space environment will provide constraints upon the design solutions. It is vital that the print success rate is as high as possible given the nature of the mission where manual intervention is impractical and the cost to launch feedstock is so great. This is even more so in the case of large-scale structural manufacture.

有一个巨大的驱动力，在轨制造，以帮助实现雄心勃勃的大规模任务，月球和超越，并用于天基服务，如电信和地球观测。然而，在这一领域内，有几个问题与制造过程中的故障检测和这些结构直接在轨道上的自主组装有关，研究在空间环境中制造物体的故障检测和恢复方法是这一项目的主要重点。将努力确定使用物理传感器、机器视觉和其他输入是否可以正确检测3D打印物体的不同故障模式，例如分层和表面/结构缺陷。这将需要模拟和比较整个打印过程中打印机的动态特性，以及任何阶段的打印对象本身，以便进行视觉比较。这一初步工作将为项目的其余部分提供信息。预计将需要机器视觉来检测与打印机无关的故障。在这一领域的研究非常有限。据设想，打印机上的物理传感器和机器视觉算法的组合将能够实时检测打印对象内的故障和缺陷。一旦开发出检测故障的鲁棒方法，就可以研究评估和恢复打印对象的方法;例如通过机械臂去除杂散材料，或在故障区域添加材料。同样，这一领域的工作有限，可以设想多种解决方案。最初的努力将基于故障的严重程度。例如，小的缺陷，例如翘曲，可以通过添加单层来补偿而实时恢复。用于确定错误严重性以及如何恢复错误的算法将是这项工作的重要成果。恢复对象需要对模拟技术进行大量研究，以确定哪些方法（如果有的话）将允许打印继续。优化算法可用于确定最佳修复方法，而对于更严重的缺陷，可使用机器人操作器移除物体的受影响区域以便重新打印，由于目标应用旨在支持在轨制造，极端空间环境将对设计解决方案造成限制。鉴于手动干预不切实际且发射原料成本如此之高的使命性质，打印成功率尽可能高至关重要。在大规模结构制造的情况下更是如此。