Metalworking Fluid Classification Based on Acoustic Emission Signals and Convolutional Neural Network
Acoustic emission (AE) which describes the transient stress
waves generated by the rapid release of energy from solid
sources has been widely used in nondestructive testing
(NDT) of materials and structures especially in health
monitoring. As a class of deep neural networks,
convolutional neural network (CNN) has applications in
many fields. Several investigations have been conducted on
the application of CNN in feature learning and fault
diagnosis and prognosis. Metalworking fluids (MWF) play a
significant role in manufacturing processes. By reducing
friction between tool and workpiece, the heat generation in
metalworking process is affected. Thread forming is a
transformative manufacturing process for generating threads
in ductile materials. As the thread geometry is manufactured
by cold forming of the material, lubricating properties of the
MWF strongly effect tool wear and workpiece quality. Up
to now, there are only a few papers on MWF classification
using the process variables like torque or released AE. In
this contribution, a novel approach combining AE signals
and CNN is raised for MWF classification. A tribometer is
used to carry out thread forming trials under well-controlled
experimental conditions. AE measurements are conducted in
context of thread forming. The AE signals are divided into
suitable samples and CNN is applied as classifier. The
results of MWF classification show that the new approach
could distinguish different types of MWF.
How to Cite
metalworking fluid, acoustic emission, convolutional neural network, classification
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.