AUTONOMOUS METHOD FOR DETECTING CUTTING TOOL AND MACHINE TOOL ANOMALIES IN MACHINING

机械加工中检测刀具和机床异常的自主方法

基本信息

批准号：
EP/T024291/1
负责人：
Zi-Qiang Lang
金额：
$ 131.67万
依托单位：
University of Sheffield
依托单位国家：
英国
项目类别：
Research Grant
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=EP%2FT024291%2F1
关键词：
AUTONOMOUS METHOD DETECTING CUTTING TOOL

项目摘要

In advanced manufacturing, there exists a rising demand for both high productivity and producing high-performance components with tighter tolerances. In order to meet these requirements, monitoring cutting tool conditions and machine tool health is needed to improve dimensional accuracy of workpiece, extend the cutting tool life, minimise machine tool down time and eliminate scrap and re-work costs. Traditionally, monitoring cutting tool conditions and machine tool health is carried out by operators who perform a manual inspection, which often causes unnecessary stoppages of machine tools and, as a result, costs incurred from lost productivity. However, without a timely inspection of both cutter status and machine tool working conditions, cutter wear or breakage and machine tool malfunction can take place during machining causing significant damage to workpieces. Some researchers have estimated that the amount of machine tool downtime due to these problems is around 6.8% while others put the figure closer to 20%. Therefore, manufacturing costs can be significantly higher than necessary when either cutters are changed before the end of their useful life or after cutter wear and breakage or machine tool malfunction have caused damage to workpieces. Consequently, a real time and automatic inspection of cutting tool status and machine tool health conditions is needed to profoundly address these problems. This project aims to propose a fundamental solution to the challenges faced by current technologies and develop innovative techniques that can autonomously detect cutting tool and machine tool anomalies in machining for advanced manufacturing. This innovative solution will be based on a novel approach known as sensor data modelling and model frequency analysis, which is uniquely developed by the PI's team at Sheffield and has recently found applications in the condition monitoring and fault diagnosis of a wide range of engineering systems and structures. The project will involve a close multi-disciplinary collaboration of ACSE academics, AMRC engineers, and industrial partners. The novel project idea and this unique research collaboration are expected to fundamentally resolve many challenges and produce urgently needed diagnostic technologies for autonomously detecting cutting tool and machine tool anomalies in machining for advanced manufacturing industry in UK.

在先进的制造业中，对高生产率和产生具有更高公差的高性能组成部分的需求不断上升。为了满足这些要求，需要监视切割工具条件和机床健康以提高工件的尺寸准确性，延长切割工具寿命，最大程度地减少机床停机时间并消除废料和重新工作成本。传统上，监视切割工具条件和机床健康是由执行手动检查的操作员进行的，这通常会导致不必要的机床停止，因此，损失的生产力产生了成本。但是，如果没有及时检查切割机状态和机床工作条件，可以在加工过程中发生切割器磨损或断裂以及机床功能，从而对工件产生重大损害。一些研究人员估计，由于这些问题，机床停机的数量约为6.8％，而另一些则使该数字接近20％。因此，当任何一个切割机在使用寿命结束之前或切割机磨损和断裂或机床功能故障之后更换刀具时，制造成本可能会大大高于必要。因此，需要实时和自动检查切割工具状态和机床健康状况，以深刻解决这些问题。该项目旨在为当前技术面临的挑战提供基本解决方案，并开发创新技术，这些技术可以自主检测用于高级制造的加工中的切割工具和机床异常。这种创新的解决方案将基于一种新型方法，称为传感器数据建模和模型频率分析，该方法是由Sheffield的PI团队独特开发的，最近在调查和故障诊断的各种工程系统和结构中发现了应用。该项目将涉及ACSE学者，AMRC工程师和工业合作伙伴的近距离学科合作。新颖的项目思想和这种独特的研究合作有望从根本上解决许多挑战，并生产出急需的诊断技术，用于自主检测英国高级制造业的加工中的切割工具和机床异常。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate Control Chart

基于非线性输出频率响应函数和多元控制图的工具状态监测

DOI：
10.37965/jdmd.2023.472
发表时间：
2023
期刊：
Journal of Dynamics, Monitoring and Diagnostics
影响因子：
0
作者：
Gui Y
通讯作者：
Gui Y

Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis

DOI：
10.1109/tim.2023.3343825
发表时间：
2024
期刊：
IEEE Transactions on Instrumentation and Measurement
影响因子：
5.6
作者：
Zepeng Liu;Zi-Qiang Lang;Yufei Gui;Yun-Peng Zhu;Hatim Laalej;David Curtis
通讯作者：
Zepeng Liu;Zi-Qiang Lang;Yufei Gui;Yun-Peng Zhu;Hatim Laalej;David Curtis

Time-Sensor Domain Data Decomposition and Analysis for Fault Diagnosis of Cutting Tools

DOI：
10.1109/icarcv57592.2022.10004293
发表时间：
2022-12
期刊：
2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)
影响因子：
0
作者：
Yufei Gui;Z. Lang;Zepeng Liu;Yunpeng Zhu;H. Laalej
通讯作者：
Yufei Gui;Z. Lang;Zepeng Liu;Yunpeng Zhu;H. Laalej

Online Rotor Systems Condition Monitoring Using Nonlinear Output Frequency Response Functions Under Harmonic Excitations