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Defect Free Hybrid Additive Manufacturing

Defect Free Hybrid Additive Manufacturing
无缺陷混合增材制造
批准号:
2599010
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --

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中文摘要
翻译
增材制造允许创建高度定制和个性化的产品,然而,这一优势也带来了检查和鉴定零件的挑战。与传统的批量制造相比,增材制造不允许破坏性测试,并且由于零件之间的固有差异,它难以适应统计过程控制方法。额外的,不正确的或缺失的材料放置的缺陷可能导致零件不具有所需的机械完整性,性能和可靠性。最终,这种产量损失会导致浪费,同时增加生产成本和时间,从而限制了增材制造的优势。该研究旨在克服这一问题,通过使用计算机控制的混合3D打印技术,结合添加剂沉积和减法工艺,实现无缺陷生产。实时监控过程将允许在创建层时识别缺陷。然后,该层可以通过减法操作重新加工,并在正确的加工条件下重新沉积。由于生产工程陶瓷的潜在广泛受益者和挑战,该学生将专注于这一应用领域。本研究的目标是:1)检查不同的成像技术,以确定每层的最佳检查方法。2)利用图像分析方法对缺陷进行量化。3)开发机器学习以改进定制零件的缺陷识别。4)生成工具路径规划操作,允许对特定层进行返工。5)示范无缺陷陶瓷件的生产。
英文摘要
Additive Manufacturing allows the creation of highly customised and personalised products, however, this advantage also creates challenges with inspecting and qualifying parts. Compared with traditional mass manufacturing, additive manufacturing does not allow destructive testing and due to the inherent differences between parts, it struggles to accommodate statistical process control methods. Defects from additional, incorrect or missing material placement can result in parts that do not have the required mechanical integrity, performance and reliability. Ultimately this yield loss restricts the advantages of Additive Manufacturing by causing wastage alongside increased production cost and time. This studentship seeks to overcome this issue to enable defect-free production through the use of computer-controlled hybrid 3D Printing which combines additive deposition and subtracting processes. Monitoring the process in real-time will allow the identification of defects when creating a layer. This layer can then be re-worked through a subtractive operation and redeposited with the correct processing conditions. Due to the potential widespread beneficiaries and challenges with producing engineered ceramics, this studentship will focus on this application area. The objectives of this studentship are: 1) Examination of different imaging techniques to determine the optimal inspection methods per layer. 2) Using image analysis methods to quantify defects. 3) Develop machine learning to improve defect identification for bespoke parts. 4) Generation of toolpath planning operations that allow reworking of a specific layer. 5) Demonstrating the production of defect-free ceramic parts.
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