In-Process Monitoring and Control of Computer Numerical Control Machine Tool Operations

计算机数控机床操作的过程监控

• 批准号: 2599318
• 金额: --
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2599318
• 关键词: Process; Monitoring; Control; Computer; Numerical

This project will develop technology to create high-efficiency and reliability in Computer Numerical Control (CNC) machine operations using Acoustic Emission (AE), leading to an advanced tool control strategy.Machine tools are subject to wear and possible failure during their operational life that can lead to poorly manufactured parts or scrapping of a component. The aim of this four year studentship is therefore to use Acoustic Emissions to monitor the tool during the CNC process to move from the current approach of periodic wear measurements to an 'as required' approach leading to high-efficiency and importantly prevent tool failure. This project will develop a detailed understanding of AE signatures associated with the CNC process, released by the tool and the component, in normal operations and during defect development. Using Machine Learning a data interrogation approach that can identify wear will be developed leading to a control system capable of detecting tool damage that will automatically adjust the CNC process during manufacture. This will aid efficiency by removing the need to periodically measure tool wear and importantly prevent tool failure.

该项目将开发技术，利用声发射 (AE) 实现计算机数控 (CNC) 机器操作的高效率和可靠性，从而实现先进的刀具控制策略。机床在其使用寿命期间会受到磨损和可能的故障，因此，可能会导致零件制造不良或组件报废。因此，这个四年制学生的目标是在数控加工过程中使用声发射来监控刀具，从当前的定期磨损测量方法转变为“按需”方法，从而实现高效率并重要的是防止刀具故障。该项目将详细了解与 CNC 流程相关的 AE 签名，这些签名由工具和组件在正常操作和缺陷开发过程中发布。使用机器学习，将开发一种可以识别磨损的数据询问方法，从而形成一个能够检测刀具损坏的控制系统，该系统将在制造过程中自动调整 CNC 工艺。这将消除定期测量刀具磨损的需要，并重要的是防止刀具故障，从而提高效率。