Material Extrusion is the fundamental idea behind Fused Deposition Modeling, the most widespread Additive Manufacturing (AM) method

材料挤出是熔融沉积建模（最广泛使用的增材制造 (AM) 方法）背后的基本理念

• 批准号: 2739019
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2739019
• 关键词: Material; Extrusion; fundamental; idea; behind

Material Extrusion is the fundamental idea behind Fused Deposition Modeling, the most widespread Additive Manufacturing (AM) method. It enables in-house rapid prototyping of previously infeasible geometries, accelerating industries such as biomedical, aerospace. On the downside, the technology is limited as it is prone to errors, mostly in the form of low dimensional and geometric tolerances. More sever defects are also common, such as warping, over-extrusion, cracking and stringing. Usually, a human worker is essential, to monitor the process as it happens and follow a trial-and-error approach whenever something goes wrong. This requirement significantly adds to the operating costs and is time consuming, hindering the wider adaptation of the technology. The suggested project will aim to investigate how Artificial Intelligence can help automate the involved repetitive tasks while benefiting from AM's digital nature. Current research focuses on a recent breakthrough known as CAXTON, which enables the in-situ monitoring of the extrusion process using image sensors installed on multiple networked 3D printers. This enables quick and diverse data collection. By applying well established and state-of-art machine learning models on the collected data, different approaches will be explored and their performances will be reported.

材料挤压是熔融沉积建模的基本思想，熔融沉积建模是最广泛的添加制造(AM)方法。它使以前不可行的几何形状能够在内部快速成型，加速了生物医学、航空航天等行业的发展。不利的一面是，这项技术是有限的，因为它容易出现错误，主要是以低尺寸和几何公差的形式出现。翘曲、过挤、开裂、翘曲等严重缺陷也较常见。通常，人类工作人员是必不可少的，以便在过程发生时进行监控，并在出现问题时遵循反复试验的方法。这一要求大大增加了运营成本，而且非常耗时，阻碍了该技术的更广泛适应。建议的项目将旨在调查人工智能如何帮助自动化涉及的重复性任务，同时受益于AM的数字本质。目前的研究重点是最近的一项名为Caxton的突破，它能够使用安装在多台联网3D打印机上的图像传感器对挤出过程进行现场监控。这就实现了快速、多样的数据收集。通过在收集的数据上应用成熟和最先进的机器学习模型，将探索不同的方法，并报告它们的性能。