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

该项目将开发利用声发射（AE）技术在计算机数控（CNC）机床操作中实现高效率和高可靠性的技术，从而实现先进的刀具控制策略。机床在使用寿命期间会受到磨损和可能的故障，这可能导致零件制造不良或部件报废。因此，这个为期四年的学生计划的目的是在CNC加工过程中使用声发射来监测刀具，从目前的定期磨损测量方法转变为“按需”方法，从而提高效率，并重要地防止刀具故障。本项目将详细了解与CNC过程相关的AE特征，由工具和组件在正常操作和缺陷开发期间发布。使用机器学习，可以识别磨损的数据询问方法将被开发，从而导致能够检测工具损坏的控制系统，该系统将在制造过程中自动调整CNC过程。这将通过消除定期测量刀具磨损的需要来提高效率，并且重要的是防止刀具故障。

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.