Correlation analysis among audible sound emissions and machining parameters in hardened steel turning

Edielson P. Frigieri (), Carlos A. Ynoguti and Anderson P. Paiva ()
Additional contact information
Edielson P. Frigieri: National Institute of Telecommunication
Carlos A. Ynoguti: National Institute of Telecommunication
Anderson P. Paiva: Federal University of Itajuba

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 4, No 15, 1753-1764

Abstract: Abstract Nowadays, monitoring systems are essential tools for manufacturing processes. As the main objective in machining processes is to produce high-quality products with reduced time, many efforts are being made to find new indirect methods that does not require to interrupt the process and does not have an excessively cost. Motivated by this premise, results of investigation on the relationship between audible sound emitted during process and the machining parameters are reported in this paper. Through experiments with the AISI 52100 hardened steel, this work shows that such a correlation does exist, presenting strong evidences that principal components scores, extracted from the power spectra of audible sound, are correlated with different machining parameters, such as material removal rate, cutting speed and depth of cut, as well as with different surface roughness levels, which makes it a promising feature for real-time process quality monitoring systems.

Keywords: Process monitoring; Hard turning; Principal component analysis; Sound; Power spectra (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-017-1356-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1356-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-017-1356-9

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().