Abstract
Maintaining high levels of geometric accuracy in five-axis machining centres is of critical importance to many industries and applications. Numerous methods for error identification have been developed in both the academic and industrial fields; one commonly-applied technique is artefact probing, which can reveal inherent system errors at minimal cost and does not require high skill levels to perform. The primary focus of popular commercial solutions is on confirming machine capability to produce accurate workpieces, with the potential for short-term trend analysis and fault diagnosis through interpretation of the results by an experienced user. This paper considers expanding the artefact probing method into a performance monitoring system, benefitting both the onsite Maintenance Engineer and visiting specialist Engineer with accessibility of information and more effective means to form insight. A technique for constructing a data-driven tolerance threshold is introduced, describing the normal operating condition and helping protect against unwarranted settings induced by human error. A multifunctional graphical element is developed to present the data trends with tolerance threshold integration to maintain relevant performance context, and an automated event detector to highlight areas of interest or concern. The methods were developed on a simulated, demonstration dataset; then applied without modification to three case studies on data acquired from currently operating industrial machining centres to verify the methods. The data-driven tolerance threshold and event detector methods were shown to be effective at their respective tasks, and the merits of the multifunctional graphical display are presented and discussed.
Highlights
To remain competitive in the modern advanced manufacturing sector, it is becoming increasingly important to embrace and implement intelligent systems for process monitoring
A requirement for repeatable performance in the manufacturing process has led to the development of error identification methods for informing machine calibration cycles, or qualification to operate by assessment of machine capability
The unique compositions of indicators observed in different machine tools support the notion of signature comparison; just as the benchmark reports can be used to compare differing signatures of machine tools within a population, so too can the methods proposed in this paper be used for comparing signatures that represent machine tool usage
Summary
To remain competitive in the modern advanced manufacturing sector, it is becoming increasingly important to embrace and implement intelligent systems for process monitoring. The extent, effect and complexity of compounding error is unique to any particular machining centre and its life cycle, leading to the possibility that two otherwise identical systems may exhibit significant performance differences in their production output. It follows that the performance of a unique system will tend to drift over time,[5] as its compound error profile is affected through further operational use. A requirement for repeatable performance in the manufacturing process has led to the development of error identification methods for informing machine calibration cycles, or qualification to operate by assessment of machine capability
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