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

在先进制造业中，对高生产率和生产具有更严格公差的高性能部件的需求不断增加。为了满足这些要求，需要监控切削刀具状况和机床健康状况，以提高工件的尺寸精度，延长切削刀具寿命，最大限度地减少机床停机时间，并消除废料和返工成本。 传统上，监控切削刀具状况和机床健康状况是由执行手动检查的操作员执行的，这通常会导致机床不必要的停机，并因此导致因生产率损失而产生的成本。然而，如果不及时检查刀具状态和机床工作条件，则在加工过程中可能发生刀具磨损或断裂以及机床故障，从而对工件造成重大损害。一些研究人员估计，由于这些问题造成的机床停机时间约为6.8%，而其他人则认为这一数字接近20%。因此，当刀具在其使用寿命结束之前或在刀具磨损和破损或机床故障已经对工件造成损坏之后更换刀具时，制造成本可能显著高于所需成本。因此，需要对刀具状态和机床健康状况进行真实的实时和自动的检测，以深刻地解决这些问题。该项目旨在为当前技术所面临的挑战提出根本解决方案，并开发能够自主检测先进制造加工中刀具和机床异常的创新技术。这种创新的解决方案将基于一种称为传感器数据建模和模型频率分析的新方法，该方法由PI在谢菲尔德的团队独特开发，最近在广泛的工程系统和结构的状态监测和故障诊断中得到了应用。 该项目将涉及ACSE学者，AMRC工程师和工业合作伙伴的密切多学科合作。新颖的项目理念和这种独特的研究合作有望从根本上解决许多挑战，并产生急需的诊断技术，用于自动检测英国先进制造业加工中的刀具和机床异常。

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.