Reliability-Based Optimal Design and Tolerancing for Multibody Systems Using Explicit Design Space Decomposition

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]  Nello Cristianini,et al.  Support Vector Machines and Kernel Methods: The New Generation of Learning Machines , 2002, AI Mag..

[2]  G. Gary Wang,et al.  Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007, DAC 2006.

[3]  B. Youn,et al.  Adaptive probability analysis using an enhanced hybrid mean value method , 2005 .

[4]  Raphael T. Haftka,et al.  A convex hull approach for the reliability-based design optimization of nonlinear transient dynamic problems , 2007 .

[5]  Vicente J. Romero,et al.  Comparison of pure and "Latinized" centroidal Voronoi tessellation against various other statistical sampling methods , 2006, Reliab. Eng. Syst. Saf..

[6]  Ramana V. Grandhi,et al.  Improved Distributed Hypercube Sampling , 2002 .

[7]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[8]  Achintya Haldar,et al.  Probability, Reliability and Statistical Methods in Engineering Design (Haldar, Mahadevan) , 1999 .

[9]  R. H. Myers,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[10]  Fouad Bennis,et al.  Tolerance Synthesis of Mechanisms: A Robust Design Approach , 2003, DAC 2003.

[11]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[12]  Michael S. Eldred,et al.  Reliability-Based Design Optimization for Shape Design of Compliant Micro-Electro-Mechanical Systems. , 2006 .

[13]  R. Ghanem,et al.  Stochastic Finite Elements: A Spectral Approach , 1990 .

[14]  G. Kharmanda,et al.  Efficient reliability-based design optimization using a hybrid space with application to finite element analysis , 2002 .

[15]  Samy Missoum,et al.  Two alternative schemes to update SVM approximations for the identification of explicit decision functions , 2008 .

[16]  Antonio Harrison Sánchez,et al.  Limit state function identification using Support Vector Machines for discontinuous responses and disjoint failure domains , 2008 .

[17]  Ching-Shin Shiau,et al.  Optimal tolerance allocation for a sliding vane compressor , 2006 .

[18]  Parviz E. Nikravesh Planar Multibody Dynamics: Formulation, Programming and Applications , 2007 .

[19]  Samy Missoum Controlling structural failure modes during an impact in the presence of uncertainties , 2007 .

[20]  Kyung K. Choi,et al.  Selecting probabilistic approaches for reliability-based design optimization , 2004 .

[21]  P. Sharma Mechanics of materials. , 2010, Technology and health care : official journal of the European Society for Engineering and Medicine.

[22]  A. Basudhar,et al.  Adaptive explicit decision functions for probabilistic design and optimization using support vector machines , 2008 .

[23]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.