Machining centre performance monitoring with calibrated artefact probing. (August 2021)

• Record Type: Journal Article
• Authors: Rooker, Tim; Stammers, Jon; Worden, Keith; Potts, Graeme; Kerrigan, Kevin; Dervilis, Nikolaos
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 235:Number 10(2021)
• Page Start: 1569
• Page End: 1587
• Publication Date: 2021-08
• Subjects: Computer numerical control (CNC) -- five-axis machining -- performance monitoring -- artefact probing -- Gaussian process -- support vector machines; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405420954728
• Languages: English
• ISSNs: 0954-4054

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.