In most trendsetting industries like the aerospace, automotive and medical industry functionally critical parts are of highest importance. Due to strict legal requirements regarding the securing of the functionality of high-risk parts, both production costs and quality costs contribute significantly to the manufacturing costs. Thus, both types of costs have to be taken into consideration during the stage of technology planning. Due to the high variety of potential interactions between individual component properties as well as between component properties and manufacturing processes, the analysis of the influence of the manufacturing history on an efficient design of inspection processes and inspection strategies is extremely complex. Furthermore, the effects of inspection strategies and quality costs on the planning of manufacturing process sequences cannot be modeled to date. As a consequence, manufacturing and inspection processes are designed separately and thus a high cost reduction potential remains untapped. In this paper, a new approach for an integrative technology and inspection planning is presented and applied to a case study in medical industry. At first, existing approaches with regard to technology and inspection planning are reviewed. After a definition of relevant terms, the case study is introduced. Following, an approach for an integrative technology and inspection planning is presented and applied to the case study. In the presented approach, the complex causalities between technology planning, manufacturing history, and inspection planning are considered to enable a cost-effective production process and inspection sequence design.

References

References
1.
Pehrsson
,
L.
,
Ng
,
A. H. C.
, and
Bernedixen
,
J.
,
2016
, “
Automatic Identification of Constraints and Improvement Actions in Production Systems Using Multi-Objective Optimization and Post-Optimality Analysis
,”
J. Manuf. Syst.
,
39
, pp.
24
37
.
2.
Slack
,
N.
,
Chambers
,
S.
, and
Johnston
,
R.
,
2007
,
Operations Management
,
5th ed.
,
Prentice Hall
,
New York
.
3.
Glock
,
C. H.
, and
Grosse
,
E. H.
,
2015
, “
Decision Support Models for Production Ramp-Up: A Systematic Literature Review
,”
Int. J. Prod. Res.
,
53
(
21
), pp.
6637
6651
.
4.
Ehrlenspiel
,
K.
,
Kiewert
,
A.
, and
Lindemann
,
U.
,
2007
,
Cost-Efficient Design
,
Springer
,
Berlin
.
5.
Colledani
,
M.
,
Tolio
,
T.
,
Fischer
,
A.
,
Lung
,
B.
,
Lanza
,
G.
,
Schmitt
,
R.
, and
Váncza
,
J.
,
2014
, “
Design and Management of Manufacturing Systems for Production Quality
,”
CIRP Ann.-Manuf. Technol.
,
63
(
2
), pp.
773
796
.
6.
Westkämper
,
E.
,
2003
, “
Assembly and Disassembly Processes in Product Life Cycle Perspectives
,”
CIRP Ann.-Manuf. Technol.
,
52
(
2
), pp.
579
588
.
7.
Fleischer
,
J.
,
Weule
,
H.
, and
Lanza
,
G.
,
2004
, “
Quality Simulation for Optimization During Production Ramp-Up
,”
Prod. Eng. Res. Dev.
,
11
(
2
), pp.
147
150
.
8.
Milberg
,
J.
, and
Müller
,
S.
,
2007
, “
Integrated Configuration and Holistic Evaluation of Technology Chains Within Process Planning
,”
Prod. Eng. Res. Dev.
,
1
(
4
), pp.
401
406
.
9.
Kessler
,
E. H.
, and
Chakrabarti
,
A. K.
,
1999
, “
Concurrent Development and Product Innovations
,”
The Dynamics of Innovation
,
K.
Brockhoff
,
A. K.
Chakrabarti
, and
J.
Hauschildt
, eds.,
Springer
,
Berlin
, pp.
281
299
.
10.
Cooper
,
R. G.
,
2014
, “
What’s Next?: After Stage-Gate
,”
Res.-Technol. Manage.
,
57
(
1
), pp.
20
31
.
11.
Klocke
,
F.
,
Fallböhmer
,
M.
,
Kopner
,
A.
, and
Trommer
,
G.
,
2000
, “
Methods and Tools Supporting Modular Process Design
,”
Rob. Comput.-Integr. Manuf.
,
16
(
6
), pp.
411
423
.
12.
Reinhart
,
G.
, and
Schindler
,
S.
,
2011
, “
Strategic Evaluation of Technology Chains for Producing Companies
,”
Enabling Manufacturing Competitiveness and Economic Sustainability
,
H. A.
ElMaraghy
, ed.,
Springer
,
Berlin
, pp.
391
396
.
13.
Stauder
,
J.
,
Buchholz
,
S.
,
Mattfeld
,
P.
, and
Rey
,
J.
,
2016
, “
Evaluating the Substitution Risk of Production Systems in Volatile Environments
,”
Prod. Eng. Res. Dev.
,
10
(3), pp. 305–318.
14.
Biermann
,
D.
,
Gausemeier
,
J.
,
Hess
,
S.
,
Petersen
,
M.
, and
Wagner
,
T.
,
2013
, “
Planning and Optimisation of Manufacturing Process Chains for Functionally Graded Components—Part 1: Methodological Foundations
,”
Prod. Eng. Res. Dev.
,
7
(
6
), pp.
657
664
.
15.
Biermann
,
D.
,
Gausemeier
,
J.
,
Heim
,
H. P.
,
Hess
,
S.
,
Petersen
,
M.
,
Ries
,
A.
, and
Wagner
,
T.
,
2015
, “
Planning and Optimisation of Manufacturing Process Chains for Functionally Graded Components—Part 2: Case Study on Self-Reinforced Thermoplastic Composites
,”
Prod. Eng. Res. Dev.
,
9
(3), pp. 405–416.
16.
Klocke
,
F.
,
Buchholz
,
S.
, and
Stauder
,
J.
,
2014
, “
Technology Chain Optimization: A Systematic Approach Considering the Manufacturing History
,”
Prod. Eng. Res. Dev.
,
8
(
5
), pp.
669
678
.
17.
Gookins
,
E. F.
,
1999
, “
Inspection and Test
,”
Juran’s Quality Handbook
,
J. M.
Juran
and
A. B.
Godrey
, eds.,
McGraw-Hill
,
New York
.
18.
Pfeifer
,
T.
,
2002
,
Quality Management: Strategies, Methods, Techniques
,
Hanser
,
Munich, Germany
.
19.
Kukulies
,
J.
,
Falk
,
B.
, and
Schmitt
,
R.
,
2014
, “
Digital Planning of Harmonised Quality Testing Activities Throughout the Product Life Cycle
,”
Procedia CIRP
,
25
, pp.
351
360
.
20.
Crostack
,
H. A.
,
Höfling
,
M.
, and
Liangsiri
,
J.
,
2005
, “
Simulation in Quality Management—An Approach to Improve Inspection Planning
,”
Acta Polytech.
,
45
(
3
), pp.
10
16
.https://ojs.cvut.cz/ojs/index.php/ap/article/view/696
21.
Basse
,
I.
,
Janßen
,
C.
,
Schmitt
,
S.
, and
Schmitt
,
R.
,
2013
, “
A Decision Model for Cost-Optimized Inspection Planning
,” International Conference on Engineering, Technology and Innovation (ICE) & IEEE International Technology Management Conference (
ITMC
), Hague, The Netherlands, June 24–26, pp. 1–15.
22.
Wuest
,
T.
,
Irgens
,
C.
, and
Thoben
,
K. D.
,
2014
, “
An Approach to Monitoring Quality in Manufacturing Using Supervised Machine Learning on Product State Data
,”
J. Intell. Manuf
,
25
(
5
), pp.
1167
1180
.
23.
Wuest
,
T.
,
Liu
,
A.
,
Lu
,
S. C. Y.
, and
Thoben
,
K. D.
,
2014
, “
Application of the Stage Gate Model in Production Supporting Quality Management
,”
Procedia CIRP
,
17
, pp.
32
37
.
24.
Salomons
,
O.
,
Houten
,
F.
, and
Kals
,
H.
,
1993
, “
Review of Research in Feature-Based Design
,”
J. Manuf. Syst.
,
12
(
2
), pp.
113
132
.
25.
Schindler
,
S.
,
2014
, “
Strategische Planung von Technologieketten für die Produktion
,” Doctoral dissertation, Herbert Utz, Munich, Germany.
26.
Denkena
,
B.
,
Henjes
,
J.
, and
Henning
,
H.
,
2011
, “
Simulation-Based Dimensioning of Manufacturing Process Chains
,”
CIRP J. Manuf. Sci. Technol.
,
4
(
1
), pp.
9
14
.
27.
Wuest
,
T.
,
Klein
,
D.
,
Seifert
,
M.
, and
Thoben
,
K. D.
,
2012
, “
Method to Describe Interdependencies of State Characteristics Related to Distortion
,”
Materialwiss. Werkstofftech.
,
43
(
1–2
), pp.
186
191
.
28.
Gelca
,
R.
, and
Andreescu
,
T.
,
2007
,
Putnam and Beyond
,
Springer
,
New York
.
29.
Klocke
,
K.
,
2011
,
Manufacturing Processes 1
,
1st ed.
,
Springer
,
Berlin
.
30.
Klocke
,
F.
,
Müller
,
J.
,
Mattfeld
,
P.
,
Kukulies
,
J.
, and
Stauder
,
J.
,
2016
, “
Integrative Technology and Inspection Planning of Medical Devices
,”
Procedia CIRP
,
51
, pp.
105
110
.
31.
Zaeh
,
M.
,
Reinhart
,
G.
,
Karl
,
F.
,
Schindler
,
S.
,
Pohl
,
J.
, and
Rimpau
,
C.
,
2010
, “
Cyclic Influences Within the Production Resource Planning Process
,”
Prod. Eng.
,
4
(
4
), pp.
309
317
.
32.
Lanza
,
G.
,
Peters
,
S.
, and
Herrmann
,
H. G.
,
2012
, “
Dynamic Optimization of Manufacturing Systems in Automotive Industries
,”
CIRP J. Manuf. Sci. Technol.
,
5
(
4
), pp.
235
240
.
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