Abstract

Early and efficient harmonization between product design and manufacturing represents one of the most challenging tasks in engineering. Concepts such as simultaneous engineering aim for a product creation process, which addresses both, functional requirements as well as requirements from production. However, existing concepts mostly focus on organizational tasks and heavily rely on the human factor for the exchange of complex information across different domains, organizations, or systems. Nowadays product and process design make use of advanced software tools such as computer-aided design, manufacturing, and engineering systems (CAD/CAM/CAE). Modern systems already provide seamless integration of both worlds in a single digital environment to ensure a continuous workflow. Yet, for the holistic harmonization between product and process design, the following aspects are missing: (i) the digital environment does not provide a complete and data consistent digital twin of the component; this applies especially to the process design and analysis environment, (ii) due to the lack of process and part condition data in the manufacturing environment, an adaptation of product and process design for a balanced functionality and manufacturability is hindered, and (iii) systematic long-term data analytics across different product and process designs with the ultimate goal to transfer knowledge from one product to the next and to accelerate the entire product development process is not considered. This paper presents an exploration concept which couples product design (CAD), process design (CAM), process simulation (CAE), and process adaptation in a single software system. The approach provides insights into correlations and dependencies between input parameters of product/process design and the process output. The insights potentially allow for a knowledge-based adaptation, tackling well-known optimization issues such as parameter choice or operation sequencing. First results are demonstrated using the example of a blade integrated disk (blisk) .

References

1.
Eversheim
,
W.
, and
Schuh
,
G.
,
2005
,
Integrierte Produkt- Und Prozessgestaltung
,
Springer-Verlag
,
Berlin Heidelberg
.
2.
Hubner
,
E.
, and
Eglseer
,
M.
, February
2009
, “
Effizienzsteigerung in Der Entwicklung – Zeitersparnis Als Wettbewerbsvorteil Bei Komplexen Interdisziplinären Prozessen
,”
Roi Dialog
,
28
, pp.
3
4
.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.
Altintas
,
Y.
,
Kersting
,
P.
,
Biermann
,
D.
,
Budak
,
E.
,
Denkena
,
B.
, and
Lazoglu
,
I.
,
2014
, “
Virtual Process Systems for Part Machining Operations
,”
CIRP Ann.
,
63
(
2
), pp.
585
605
.10.1016/j.cirp.2014.05.007
5.
Altintas
,
Y.
, and
Tulsyan
,
S.
,
2015
, “
Prediction of Part Machining Cycle Times Via Virtual CNC
,”
CIRP Ann.
,
64
(
1
), pp.
361
364
.10.1016/j.cirp.2015.04.100
6.
Altintas
,
Y.
,
Brecher
,
C.
,
Weck
,
M.
, and
Witt
,
S.
,
2005
, “
Virtual Machine Tool
,”
CIRP Ann.
,
54
(
2
), pp.
115
138
.10.1016/S0007-8506(07)60022-5
7.
Wiederkehr
,
P.
, and
Siebrecht
,
T.
,
2016
, “
Virtual Machining: Capabilities and Challenges of Process Simulations in the Aerospace Industry
,”
CIRP Procedia Manuf.
,
6
, pp.
80
87
.10.1016/j.promfg.2016.11.011
8.
Biermann
,
D.
,
Kersting
,
P.
,
Wagner
,
T.
, and
Zabel
,
A.
,
2015
, “
Modeling and Optimization of Machining Problems
,”
Springer Handbook of Computational Intelligence
,
Kacprzyk
,
J.
, and
Pedrycz
,
W.
, eds.,
Springer Handbooks
,
Springer, Berlin, Heidelberg
.
9.
Brecher
,
C.
,
Wellmann
,
F.
, and
Epple
,
A.
,
2017
, “
Quality-Predictive CAM Simulation for NC Milling
,”
CIRP Procedia Manuf.
,
11
, pp.
1519
1527
.10.1016/j.promfg.2017.07.284
10.
Li
,
Z.-L.
,
Tuysuz
,
O.
,
Zhu
,
L.-M.
, and
Altintas
,
Y.
,
2018
, “
Surface Form Error Prediction in Five-Axis Flank Milling of Thin- Walled Parts
,”
Int. J. Mach. Tools Manuf.
,
128
, pp.
21
32
.10.1016/j.ijmachtools.2018.01.005
11.
Bergs
,
T.
,
Gierlings
,
S.
,
Auerbach
,
T.
,
Kling
,
A.
,
Schraknepper
,
D.
, and
Augspurger
,
T.
,
2021
, “
The Concept of Digital Twin and Digital Shadow in Manufacturing
,”
Proceedings of the 9th CIRP Conference on High Performance Cutting
, Accepted in Procedia CIRP.
12.
Zhang
,
Y.
, and
Yang
,
Q.
,
2018
, “
An Overview of Multi-Task Learning
,”
Natl. Sci. Rev.
,
5
(
1
), pp.
30
43
.10.1093/nsr/nwx105
13.
Pan
,
S. J.
, and
Yang
,
Q.
,
2010
, “
A Survey on Transfer Learning
,”
IEEE Trans. Knowledge Data Eng.
,
22
(
10
), pp.
1345
1359
.10.1109/TKDE.2009.191
14.
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
15.
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
16.
Döbel
,
I.
,
Leis
,
M.
,
Vogelsang
,
M.
,
Neustroev
,
D.
,
Petzka
,
H.
,
Riemer
,
A.
,
Rüping
,
S.
,
Voss
,
A.
,
Wegele
,
M.
, and
Welz
,
J.
,
2018
,
Maschinelles Lernen – Eine Analyse zu Kompetenzen, Forschung Und Anwendung
,
Fraunhofer-Gesellschaft
,
München
.
17.
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
18.
Möhring
,
H.-C.
,
Wiederkehr
,
P.
,
Erkorkmaz
,
K.
, and
Kakinuma
,
Y.
,
2020
, “
Self-Optimizing Machining Systems
,”
CIRP Ann.
,
69
(
2
), pp.
740
763
.10.1016/j.cirp.2020.05.007
19.
Ma
,
H.
,
Liu
,
W.
,
Zhou
,
X.
,
Niu
,
Q.
, and
Kong
,
C.
,
2020
, “
An Effective and Automatic Approach for Parameters Optimization of Complex End Milling Process Based on Virtual Machining
,”
J. Intell. Manuf.
,
31
(
4
), pp.
967
984
.10.1007/s10845-019-01489-6
20.
Fricke, K., Gierlings, S., Ganser, P., Venek, T., and Bergs
,
T.
,
2021
, “
Geometry Model and Approach for Future Blisk LCA
,”
IOP Conf. Ser.: Mater. Sci. Eng.
, 1024, p. 012067.10.1088/1757-899X/1024/1/012067
21.
Minoufekr
,
M.
,
2015
, “
Macroscopic Simulation of Multi-Axis Machining Processes
,” Diss., Apprimus,
Aachen
.
22.
Cabral
,
G.
,
2015
, “
Modeling and Simulation of Tool Engagement and Prediction of Process Forces in Milling
,” Diss., Apprimus,
Aachen
.
23.
Altintas
,
Y.
,
2012
,
Manufacturing Automation
,
Cambridge University Press
, New York.
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