A novel transfer learning framework for chatter detection using convolutional neural networks

Hakki Ozgur Unver () and Batihan Sener
Additional contact information
Hakki Ozgur Unver: TOBB University of Economics and Technology
Batihan Sener: TOBB University of Economics and Technology

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 3, No 10, 1105-1124

Abstract: Abstract Detection and avoidance of regenerative chatter play a crucial role in ensuring the high quality and efficiency of machining operations. Predominant analytical approaches provide stability lobe diagrams for machining processes. Deep learning is a general term given to the most recent and successful group of machine learning methods that proved great promise in many areas of human life. This study purposes a novel transfer learning framework that combines analytical solutions and convolution neural network (CNN) under a novel transfer learning framework. Stability lobes and numerical time-domain solutions of analytical methods are used to train and label, arguably one of the most successful CNN architectures, AlexNet. This approach eliminates the need for a time-consuming and costly experimental data collection phase for training. Furthermore, an ensemble empirical mode decomposition based signal pre-processing method is developed. An IMF-based multi-band ensemble approach is proposed where only intrinsic mode functions relevant to each modal frequency of the system are selected based on their entropy increase and used in training multiple AlexNet instances. The measured data were collected during shoulder milling from a CNC-vertical milling machine. The results revealed considerable success in several scenarios ranging from 82 to 100%, without using any experimentally measured data in training.

Keywords: Transfer learning; Chatter detection; CNN; EEMD (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-021-01839-3 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:34:y:2023:i:3:d:10.1007_s10845-021-01839-3

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

DOI: 10.1007/s10845-021-01839-3

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 ().