CNC spindle signal investigation for the prediction of cutting tool health

Hill, Jacob, Prickett, Paul ORCID: https://orcid.org/0000-0001-8985-7278, Grosvenor, Roger ORCID: https://orcid.org/0000-0001-8942-4640 and Hankins, Gareth 2018. CNC spindle signal investigation for the prediction of cutting tool health. Presented at: Fourth European Conference of the PHM Society, Utrecht, The Netherlands, 3-6 July 2018. Published in: Kulkarni, Chetan S. and Tinga, Tiedo eds. Proceedings of the European Conference of the PHM Society. , vol.4 (1) Prognostics and Health Management Society,

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (498kB) | Preview

Abstract

The deterioration of cutting tools plays a significant role in the progression of subtractive manufacturing and substantially affects the quality of machined parts. Recognising this most organisations have implemented conventional methods for tool management. These reduce the economic loss associated with time-dependent and stochastic tool wear, and limit the damage arising from tools at end-of-life. However, significant costs still remain to be addressed and more development towards tool and process prognostics is desirable. In response, this work investigates process deterioration through the acquisition and processing of selected machine signals. This utilises the internal processor of a CNC Vertical Machining Centre and considers the possible applications of such an approach for the prediction of tool and process health. This paper considers the prediction of tool and process condition with a discussion of the assumptions, benefits, and limitations of such approaches. Furthermore, the efficacy of the approach is tested using the correlation between an offline measurement of part accuracy and an active measure of process variation.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TS Manufactures
Publisher:	Prognostics and Health Management Society
Funders:	Engineering and Physical Sciences Research Council and Renishaw jointly fund this research under an iCASE award, reference number 16000122.
Date of First Compliant Deposit:	20 August 2018
Last Modified:	05 Jan 2024 04:45
URI:	https://orca.cardiff.ac.uk/id/eprint/114166

Actions (repository staff only)

Edit Item