This paper introduces a new approach for the optimal geometric design and tolerancing of multibody systems. The approach optimizes both the nominal system dimensions and the associated tolerances by solving a reliability-based design optimization (RDBO) problem under the assumption of truncated normal distributions of the geometric properties. The solution is obtained by first constructing the explicit boundaries of the failure regions (limit state function) using a support vector machine, combined with adaptive sampling and uniform design of experiments. The use of explicit boundaries enables the treatment of systems with discontinuous or binary behaviors. The explicit boundaries also allow for an efficient calculation of the probability of failure using importance sampling. The probability of failure is subsequently approximated over the whole design space (the nominal system dimensions and the associated tolerances), thus making the solution of the RBDO problem straightforward. The proposed approach is applied to the optimization of a web cutter mechanism.

1.
Huang
,
Y. M.
, and
Shiau
,
C. S.
, 2006, “
Optimum Tolerance Allocation for a Sliding Vane Compressor
,”
ASME J. Mech. Des.
0161-8458,
128
(
1
), pp.
98
107
.
2.
Caro
,
S.
,
Bennis
,
F.
, and
Wenger
,
P.
, 2005, “
Tolerance Synthesis of Mechanisms: A Robust Design Approach
,”
ASME J. Mech. Des.
0161-8458,
127
(
1
), pp.
86
94
.
3.
Myers
,
R. H.
, and
Montgomery
,
D. C.
, 2002,
Response Surface Methodology
, 2nd ed.,
Wiley
,
New York
.
4.
Wang
,
G.
, and
Shan
,
S.
, 2007, “
Review of Metamodeling Techniques in Support of Engineering Design Optimization,”
ASME J. Mech. Des.
0161-8458,
129
(
4
), pp.
370
380
.
5.
Haldar
,
A.
, and
Mahadevan
,
S.
, 2000,
Probability, Reliability, and Statistical Methods in Engineering Design
,
Wiley
,
New York
.
6.
Ghanem
,
R.
, and
Spanos
,
P. D.
, 1991,
Stochastic Finite Elements: A Spectral Approach
,
Springer
,
New York
.
7.
Kharmanda
,
G.
,
Mohamed
,
A.
, and
Lemaire
,
M.
, 2002, “
Efficient Reliability Based Design Optimization Using a Hybrid Space With Application to Finite Element Analysis
,”
Struct. Multidiscip. Optim.
1615-147X,
24
, pp.
233
245
.
8.
Youn
,
B. D.
, and
Choi
,
K. K.
, 2004, “
Selecting Probabilistic Approaches for Reliability Based Design Optimization
,”
AIAA J.
0001-1452,
42
(
1
), pp.
124
131
.
9.
Adams
,
B. A.
,
Eldred
,
M. S.
, and
Wittwer
,
J. W.
, 2006, “
Reliability Based Design Optimization for Shape Design of Compliant Micro-Electro-Mechanical Systems
,”
Proceedings of the 11th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization
, Portsmouth, VA.
10.
Trosset
,
M. W.
,
Alexandrov
,
N. M.
, and
Watson
,
L. T.
, 2003, “
New Methods for Robust Design Using Computer Experiments
,”
Proceedings of the Section on Physical and Engineering Sciences
, American Statistical Association.
11.
Burges
,
C. J. C.
, 1998, “
A Tutorial on Support Vector Machines for Pattern Recognition
,”
Data Min. Knowl. Discov.
1384-5810,
2
(
2
), pp.
121
67
.
12.
Romero
,
V. J.
,
Burkardt
,
J. V.
,
Gunzburger
,
M. D.
, and
Peterson
,
J. S.
, 2006, “
Comparison of Pure and Latinized Centroidal Voronoi Tesselation Against Various Other Statistical Sampling Methods
,”
Reliab. Eng. Syst. Saf.
0951-8320,
91
, pp.
1266
80
.
13.
Beachkofski
,
B. K.
, and
Grandhi
,
R.
, 2002, “
Improved Distributed Hypercube Sampling
,” AIAA Paper No. AIAA-2002-1274.
14.
Basudhar
,
A.
, and
Missoum
,
S.
, 2008, “
Adaptive Explicit Decision Functions for Probabilistic Design and Optimization Using Support Vector Machines
,”
Comput. Struct.
0045-7949,
86
(
19–20
), pp.
1904
1917
.
15.
Basudhar
,
A.
, and
Missoum
,
S.
, 2008, “
Two Alternative Schemes to Update SVM Approximations for the Identification of Explicit Decision Functions
,”
Proceedings of the 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
, Victoria, BC, Canada.
16.
Missoum
,
S.
,
Ramu
,
P.
, and
Haftka
,
R. T.
, 2007, “
A Convex Hull Approach for the Reliability-Based Design of Transient Dynamic Problems
,”
Comput. Methods Appl. Mech. Eng.
0045-7825,
196
, pp.
2895
2906
.
17.
Missoum
,
S.
, 2007, “
Controlling Structural Failure Modes During an Impact in the Presence of Uncertainties
,”
Struct. Multidiscip. Optim.
1615-147X,
34
(
6
), pp.
463
472
.
18.
Basudhar
,
A.
,
Missoum
,
S.
, and
Harrison Sanchez
,
A.
, 2008, “
Limit State Function Identification Using Support Vector Machines for Discontinuous Responses and Disjoint Failure Domains
,”
Probab. Eng. Mech.
0266-8920,
23
(
1
), pp.
1
11
.
19.
Cristianini
,
N.
, and
Schölkopf
,
B.
, 2002, “
Support Vector Machines and Kernel Methods: The New Generation of Learning Machines
,”
AI Mag.
0738-4602,
23
(
3
), pp.
31
41
.
20.
Shawe-Taylor
,
J.
, and
Cristianini
,
N.
, 2004,
Kernel Methods for Pattern Analysis
,
Cambridge University Press
,
Cambridge, England
.
21.
Gunn
,
S. R.
, 1998, “
Support Vector Machines for Classification and Regression
,” Technical Report No. ISIS-1-98, Department of Electronics and Computer Science, University of Southampton.
22.
Youn
,
B. D.
,
Choi
,
K. K.
, and
Du
,
L. L.
, 2005, “
Adaptive Probability Analysis Using an Enhanced Hybrid Mean Value Method
,”
Struct. Multidiscip. Optim.
1615-147X,
29
, pp.
134
148
.
23.
Smola
,
A. J.
, and
Schoelkopf
,
B.
, 2004, “
A Tutorial on Support Vector Regression
,”
Stat. Comput.
0960-3174,
14
, pp.
199
222
.
24.
Nikravesh
,
P. E.
, 2008,
Planar Multibody Dynamics: Formulation, Programming and Applications
,
CRC
,
Boca Raton, FL
.
25.
Gere
,
J.
, 2000,
Mechanics of Materials
,
Brooks-Cole
,
Belmont, MA
.
26.
Schnell
,
W.
,
Gross
,
D.
, and
Hauger
,
W.
, 2002,
Technische Mechanik
,
Springer-Verlag
,
Berlin
.
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