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

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.

The proposed project will develop an innovative sensor data modelling and model frequency analysis-based approach, which would create a paradigmatic shift to the current way of monitoring of cutting tool condition and machine tool health, realising autonomously detecting cutting tool and machine tool anomalies in machining for advanced manufacturing. Thanks to more robust to noises and use of physically meaningful features, the new approach can more effectively reveal cutting tool physical characteristic changes and machine tool spindle conditions. This would fundamentally address challenges faced by existing visual aid or direct sensor signal analysis-based monitoring technologies. The potential impacts of the research outcomes are as follows:

-The project will open a new research area of direct industrial relevance for system and control scientists as well as provide innovative monitoring technologies for researchers working on cutting tool and machine tool operation and maintenance. The project will study and apply a novel nonlinear model frequency analysis based structural system feature extraction. This is also of significance for the application of machine learning to condition monitoring and fault diagnosis of a wide range of complicated engineering systems and structures where feature extraction is a challenging but necessary and important step.

-The developed approach can be extended to address process monitoring related research challenges in many other industrial processes including, e.g., safety critical systems such as robotic arms used in nuclear power plant maintenance and quality critical manufacturing such as drilling in aircraft manufacturing.

-The autonomous diagnostic system developed in the project is expected to result in radical improvements in both cost efficiency and product quality in advanced manufacturing, allowing work to move back to the UK from low-wage economies, strengthening UK supply chains, and boosting the development of the UK economy.

-The market opportunity for the developed diagnostic system is significant as metal machining represents 10% and remains the most important of all manufacturing processes in the UK. The adoption of the new approach based technologies will produce direct benefits to many industrial sectors in the UK, particularly, the UK aerospace industry which has recently seen sales of £35.9bn with a work force of over 111,000 (Source: UK ADS 2018) .

These cover a wide range of impact areas including academic, economic, industrial, and related social impacts.