Abstract

The manufacturing process of blade-integrated disks (blisks) represents one of the most challenging tasks in turbomachinery manufacturing. The requirement is to machine complex, thin-walled blade geometries with high aspect ratios made of difficult-to-cut materials. In addition, extremely tight tolerances are required, since the smallest deviations can lead to a reduction in efficiency of the blisk in the later use. Nowadays, the ramp-up phase for the manufacturing of a new blisk is time and cost-intensive. To find a suitable manufacturing process that meets the required tolerances of the blisk, many experimental tests with different process parameters and strategies are necessary. The used approach is often trial and error, which offers limited testing opportunities, is time-consuming and waste of resources. Therefore, the objective of this paper is to develop a knowledge-based process design optimization in blisk manufacturing. For this purpose, this paper picks up the results from our previous work. Based on these results, an experimental validation of the two process design tasks “number of blocks” and “block transition” is conducted. As part of the validation, the results of machining tests on a demonstrator blisk made of Inconel 718 are presented and discussed.

References

1.
Eversheim
,
W.
, and
Schuh
,
G.
,
2005
,
Integrierte Produkt- Und Prozessgestaltung
,
Springer-Verlag
,
Berlin
.
2.
Hubner
,
E.
, and
Eglseer
,
M.
,
2009
, “
Effizienzsteigerung in Der Entwicklung – Zeitersparnis Als Wettbewerbsvorteil Bei Komplexen Interdisziplinären Prozessen
,”
Roi Dialog
,
28
, pp.
3
4
.https://www.roi.de/fileadmin/user_upload/dialog/import//DIALOG_28.pdf
3.
Arrazola
,
P. J.
,
Özel
,
T.
,
Umbrello
,
D.
,
Davies
,
M.
, and
Jawahir
,
I. S.
,
2013
, “
Recent Advances in Modelling of Metal Machining Processes
,”
CIRP Ann.
,
62
(
2
), pp.
695
718
.10.1016/j.cirp.2013.05.006
4.
Ganser
,
P.
,
Landwehr
,
M.
,
Schiller
,
S.
,
Vahl
,
C.
,
Mayer
,
S.
, and
Bergs
,
T.
,
2022
, “
Knowledge-Based Adaptation of Product and Process Design in Blisk Manufacturing
,”
ASME J. Eng. Gas Turbines Power
,
144
, p.
011023
.10.1115/1.4052029
5.
Zhang
,
Y.
, and
Yang
,
Q.
,
2018
, “
An Overview of Multi-Task Learning
,”
Natl. Sci. Rev.
,
5
(
1
), pp.
30
43
.10.1093/nsr/nwx105
6.
Pan
,
S. J.
, and
Yang
,
Q.
,
2010
, “
A Survey on Transfer Learning
,”
IEEE Trans. Knowl. Data Eng.
,
22
(
10
), pp.
1345
1359
.10.1109/TKDE.2009.191
7.
Weiss
,
K.
,
Khoshgoftaar
,
T. M.
, and
Wang
,
D.
,
2016
, “
A Survey of Transfer Learning
,”
J. Big Data
,
3
(
1
), p.
9
.10.1186/s40537-016-0043-6
8.
Parter
,
M.
,
Kashtan
,
N.
, and
Alon
,
U.
,
2008
, “
Facilitated Variation: How Evolution Learns From Past Environments to Generalize to New Environments
,”
PLoS Comput. Biol.
,
4
(
11
), p.
e1000206
.10.1371/journal.pcbi.1000206
9.
Narooei
,
K. D.
, and
Ramli
,
R.
,
2014
, “
Application of Artificial Intelligence Methods of Tool Path Optimization in CNC Machines: A Review
,”
Res. J. Appl. Sci.
,
8
(
6
), pp.
746
754
.10.19026/rjaset.8.1030
10.
Fricke
,
K.
,
Gierlings
,
S.
,
Ganser
,
P.
,
Venek
,
T.
, and
Bergs
,
T.
,
2020
, “
Geometry Model and Approach for Future Blisk LCA
,”
IOP Conference Series: Materials Science and Engineering
, 1024, p.
012067
.10.1088/1757-899X/1024/1/012067
11.
Landwehr
,
M.
,
Schmid
,
S.
,
Holla
,
V.
,
Ganser
,
P.
,
Bergs
,
T.
,
Ruess
,
M.
, and
Schröder
,
K.-U.
,
2021
, “
The Finite Cell Method for the Prediction of Machining Distortion Caused by Initial Residual Stresses in Milling
,”
Procedia CIRP
,
102
, pp.
144
149
.10.1016/j.procir.2021.09.025
12.
Altintas
,
Y.
,
2012
,
Manufacturing Automation
,
Cambridge University Press
,
New York
.
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