Manufacturing and
Materials Processing
Journal of
Communication
Machine Learning of Surface Layer Property Prediction for
Milling Operations
Eckart Uhlmann
1,2
, Tobias Holznagel
2,
*, Philipp Schehl
2
and Yannick Bode
2
Citation: Uhlmann, E.; Holznagel, T.;
Schehl, P.; Bode, Y. Machine Learning
of Surface Layer Property Prediction
for Milling Operations. J. Manuf.
Mater. Process. 2021, 5, 104. https://
doi.org/10.3390/jmmp5040104
Academic Editors: Arkadiusz Gola,
Izabela Nielsen and Patrik Grznár
Received: 27 August 2021
Accepted: 27 September 2021
Published: 30 September 2021
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1
Fraunhofer Institute for Production Systems and Design Technology IPK, Pascalstraße 8-9, 10587 Berlin,
Germany; eckart.uhlmann@ipk.fraunhofer.de
2
Institute for Machine Tools and Factory Management, Technische Universität Berlin, Pascalstraße 8-9,
10587 Berlin, Germany; p.schehl@campus.tu-berlin.de (P.S.); y.bode@campus.tu-berlin.de (Y.B.)
* Correspondence: tobias.holznagel@iwf.tu-berlin.de; Tel.: +49-30-314-23998
Abstract:
Tool wear and cutting parameters have a significant effect on the surface layer properties
in milling. Since the relation between tool wear, cutting parameters, and surface layer properties
is mostly unknown, the latter cannot be controlled during production and may vary from part
to part as tool wear progresses. To account for this uncertainty and to prevent premature failure,
components often need to be oversized or surface layer properties need to be adjusted in subsequent
manufacturing processes. Several approaches have been made to obtain models that predict the
surface layer properties induced by manufacturing processes. However, those approaches need to
be calibrated with a considerable number of experimental trials. As trials are time-consuming and
surface layer measurements are laborious, no industrial applications have been realized. Complex
models have one major drawback. They have to be re-parameterized as soon as process characteristics
change. Therefore, manual experimental parameterization does not appear to be a feasible approach
for industrial application. A highly automated approach for the machine learning of the relation
between tool wear, cutting parameters and surface layer properties is presented in this paper. The
amount of obtained measurement data allows a fundamental analysis of the approach, which paves
the way for further developments.
Keywords:
surface engineering; milling; machine learning; evolution strategy; surface layer
properties
1. Introduction
Surface layer properties (SLP) of a workpiece, such as microhardness, martensite
content, and compressive or tensile residual stresses, are influenced by temperatures and
temperature gradients, which occur in the cutting zone during milling [
1
]. Mechanical
tension and high strain rates might lead to severe plastic deformation and thus to the
formation of nanocrystalline structures, resulting in an increase in tensile strength [
2
].
Accordingly, SLP have a significant impact on component life and premature failure, e.g.,
compressive residual stresses in the surface layer that prevent crack propagation [
3
–
5
].
Since plastic deformation and cutting temperatures are the result of forming, shear, friction,
material separation, and material redirection, which are subjected to disturbances such as
increasing tool wear and rising tool temperature, the manufactured SLP vary considerably
over tool life.
The SLP are in most cases not monitored by quality control in the manufacturing
industry or used as a feedback value for further production. To prevent premature failure
of a critical part caused by lack of process knowledge in manufacturing, it is often necessary
to design parts with safety factors [
6
]. This results in heavy and oversized components.
Downstream processes such as heat treatments [
7
], plasma nitriding [
8
], deep rolling [
9
],
or shot peening [
10
,
11
] offer an industrially applicable possibility for the defined configu-
ration of SLP. Major disadvantages of these approaches are the significant time and cost
J. Manuf. Mater. Process. 2021, 5, 104. https://doi.org/10.3390/jmmp5040104 https://www.mdpi.com/journal/jmmp
