Research on intelligent tool condition monitoring based on data-driven: a review

Galling wear, a severe form of wear, is a known problem in sheet metal forming. As the wear state is not directly observable in closed tribosystems, such as in industrial sheet metal forming processes, indirect tool wear monitoring techniques for inferring the wear state of the tool from suitable signal characteristics are the subject of intense research. The analysis of acoustic emissions is a promising technique for tool condition monitoring. This research has explored feature selection using t-tests, linear regression models, and cluster analysis of the data. This analysis has been conducted both with and without the inclusion of control variables, friction, and roughness to discriminate between the behavior of the acoustic emissions during different stages of galling wear. Scratch testing at slow sliding speed (1 mm/s) has been used to produce the galling wear between a tool steel indenter and aluminum sheet at 10 N applied load, for which the acoustic emissions were recorded. The bursts of the acoustic emission signal were processed and investigated to observe how the bursts changed with increasing galling damage (increasing material removal and transfer). Novel parameters in the field of galling wear have been identified, and novel models for observing the change in galling wear have been identified, thus furthering the development of acoustic emissions analysis as a non-invasive condition monitoring system, particularly for sheet metal forming processes.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Research on intelligent tool condition monitoring based on data-driven: a review

Cited by 4 publications

References 62 publications

Analysis of tool wear mechanism and wear effect of drill thread mill machining

Analysis of tool wear mechanism and wear effect of drill thread mill machining

Dynamic milling force model considering vibration and tool flank wear width for monitoring tool states in machining of Ti-6AI-4V

Acoustic Emission Characteristics of Galling Behavior from Dry Scratch Tests at Slow Sliding Speed

Contact Info

Product

Resources

About