Modeling and Simulation for Product Design Process

IFAC Proceedings Volumes, 2000

In today's increasingly competltlve markets, leading engineering organizations need to capitalize on every available opportunity to increase the quality of their products while reducing the length of the development cycle and manufacturing costs. This paper presents an integral approach for the reliability validation process of a product in the automotive industry. The stress/strength concepts are applied to estimate and validate the product reliability in which case both stress and strength are random occurring parameters within the reliability validation process. The simulation approach using vinual prototypes is presented as well.

2014

A large number of problems in manufacturing processes, production planning, finance and engineering design require an understanding of potential sources of variations and quantification of the effect of variations on product behavior and performance. Traditionally, in engineering problems uncertainties have been formulated only through coarse safety factors. Such methods often lead to overdesigned products. Different methods exist to describe model uncertainties and to calculate reliability and safety, but they all have a limited area of application. A new adaptive response surface method is introduced to analyse the design reliability with high accuracy and efficiency. Whereby the surrogate model is based on an improved moving least square approximation combined with an adaptive design of experiment. In order to obtain a fast simulation procedure on the response surface an adaptive importance sampling concept is used. Two numerical examples show the applicability of this concept fo...

9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 2002

During the past decade, numerous endeavors have been made to develop effective reliability-based design optimization (RBDO) methods. Because the evaluation of probabilistic constraints defined in the RBDO formulation is the most difficult part to deal with, a number of different probabilistic design approaches have been proposed to evaluate probabilistic constraints in RBDO. In the first approach, statistical moments are approximated to evaluate the probabilistic constraint. Thus, this is referred to as the approximate moment approach (AMA). The second approach, called the reliability index approach (RIA), describes the probabilistic constraint as a reliability index. Last, the performance measure approach (PMA) was proposed by converting the probability measure to a performance measure. A guide for selecting an appropriate method in RBDO is provided by comparing probabilistic design approaches for RBDO from the perspective of various numerical considerations. It has been found in the literature that PMA is more efficient and stable than RIA in the RBDO process. It is found that PMA is accurate enough and stable at an allowable efficiency, whereas AMA has some difficulties in RBDO process such as a second-order design sensitivity required for design optimization, an inaccuracy to measure a probability of failure, and numerical instability due to its inaccuracy. Consequently, PMA has several major advantages over AMA, in terms of numerical accuracy, simplicity, and stability. Some numerical examples are shown to demonstrate several numerical observations on the three different RBDO approaches. β = βt = MPP in first-order inverse reliability analysis

dynalook.com

This paper presents a holistic simulation-driven system design methodology considering multiple performance objectives, performance constraints including formability criterion defined herein, using a genetic algorithm based multi-objective optimization software GDOT, developed in-house. This tool treats multiple objective functions separately without combining them in any form. A decision-making criterion is subsequently invoked to select the "best" subset of solutions from the obtained non-dominated Pareto optimal solutions under multiple performance constraints along with a formability index. Geometric properties, associated material properties (yield strength / plastic strain to failure) are considered as design variables. An example involving the frontal impact on a rail section is used to demonstrate the methodology. This process can further suggest requirements for synthesizing new materials that will result in optimal product performance. The objective of this study is to establish an 'optimized' set of design parameters with the dual aim of (i) minimizing the structural weight and (ii) maximizing energy absorption efficiency of the front rail system during frontal impact. The performance constraints being maximum transmitted force, maximum intrusion, pulse efficiency and formability criterion. This study also looks at the effect of parameter uncertainty on the optimal design. This study is composed in two stages. The first stage attempts to solve the multi-objective optimization problem, which is attempted using proprietary GDOT optimization code. Stage two performs reliability-based multi-objective optimization to generate a 'reliable' pareto optimal front. A 2 nd order meta model is developed using responses, including formability index, computed from physics-based finite element models using LS-DYNA TM analysis code. Looking at a broader picture, this methodology can potentially fill the gap between numerically optimized system development and simulation-driven digital product development. This, in turn, will help realize numerical simulation-driven product development process by aiming to achieve designs that are "first time right".

2006

A large number of problems in manufacturing processes, production planning, finance and engineering design require an understanding of potential sources of variations and quantification of the effect of variations on product behavior and performance. Traditionally, in engineering problems uncertainties have been formulated only through coarse safety factors. Such methods often lead to overdesigned products. First, this paper gives an overview over the state of the art in reliability analysis methods to describe model uncertainties and to calculate reliability and safety. Different methods exist, but they all have a limited area of application. The aim of the first part is to investigate the applicability of certain methods for specific problems and to give a general indication, which method is appropriate for certain problem classes, implemented in optiSLang. In addition, a new adaptive response surface method is introduced to analyse the design reliability with high accuracy and efficiency. Whereby the surrogate model is based on an improved moving least square approximation combined with an adaptive design of experiment. In order to obtain a fast simulation procedure on the response surface an adaptive importance sampling concept is developed. Several numerical examples show the applicability of this concept for highly nonlinear state and limit state functions and multiple design points and separated unsafe domains. Furthermore, within a structural reliability analysis example a discretization of random fields in combination with a robustness evaluation to identify the relevant random variables is introduced. The so-called nonparametric structural reliabilty analysis is a new method to estimate the safety and reliability of finite element structures in cases where a CAD-based parametrization is not possible or not meaningful. The probabilistic and structural analysis tasks are performed with the optiSLang , SoS and SL ang software packages.

Computers & Structures, 2004

Deterministic optimum designs that are obtained without consideration of uncertainties could lead to unreliable designs, therefore calling for reliability-based design optimization (RBDO). However, it has been reported in literatures that when RBDO involves evaluation of probabilistic constraints it is prohibitively expensive or even diverges for many large-scale applications. Therefore, the hybrid mean value (HMV) method had been proposed by authors for highly efficient and stable RBDO by evaluating the probabilistic constraint effectively. However, even with the HMV method, the RBDO process could be still expensive for large-scale applications or applications where efficient design sensitivity analysis method is unavailable. To alleviate this difficulty, a new RBDO methodology developed integrating the HMV method with a proposed response surface method, which is specifically developed for reliability analysis and optimization. A large-scale example problem is employed to demonstrate the proposed RBDO method.

International Journal of Reliability and Safety, 2006

Several inverse reliability measures (e.g. Probabilistic Performance Measure (PPM) and Probabilistic Sufficiency Factor (PSF)) that are essentially equivalent have been introduced in recent years as measures of safety. The different names for essentially the same measure reflect the fact that different researchers focused on different advantages of inverse measures. These advantages include improved computational efficiency of Reliability-Based Design Optimisation (RBDO), accuracy in Response Surface Approximations (RSAs) and easy estimates of resources needed for achieving target safety levels. This paper surveys these inverse measures and describes their advantages compared with the direct measures of safety such as probability of P. Ramu et al.

2010

This dissertation aims at developing a generic reliability analysis and design framework that enables reliability prediction and design improvement with random parameter, field, and process variables. The capability of this framework is further improved by predicting and managing reliability even with a dearth of data that can be used to characterize random variables. To accomplish the research goal, three research thrusts are set forth. First, advanced techniques are developed to characterize the random field or process. The fundamental idea of these techniques is to model the random field or process with a set of important field signatures and random variables. These techniques enable the use of random parameter, field, and process variables for reliability analysis and design even with a dearth of data. Second, a generic reliability analysis framework is proposed to accurately assess system reliability in the presence of random parameter, field, and process variables. An advanced probability analysis technique, the Eigenvector Dimension Reduction (EDR) method, is developed by integrating the Dimension Reduction (DR) method with three proposed improvements: 1) an eigenvector sampling approach to obtain statistically independent samples over a random space; 2) a Stepwise Moving Least Square (SMLS) method to accurately approximate system responses over a random space; and 3) a Probability Density Function (PDF) generation method to accurately approximate the PDF of system responses for reliability analysis. Third, a generic Reliability-Based Design Optimization (RBDO) framework is developed to solve engineering design problems with random parameter, field, and process variables. This design framework incorporates the EDR method into RBDO. To illustrate the effectiveness of the developed framework, many numerical and engineering examples are employed to conduct the reliability analysis and RBDO with random parameter, field, and process variables. This dissertation demonstrates that the developed framework is very accurate and efficient for the reliability analysis and RBDO of engineering products and processes.

AIAA Journal, 2004

During the past decade, numerous endeavors have been made to develop effective reliability-based design optimization (RBDO) methods. Because the evaluation of probabilistic constraints defined in the RBDO formulation is the most difficult part to deal with, a number of different probabilistic design approaches have been proposed to evaluate probabilistic constraints in RBDO. In the first approach, statistical moments are approximated to evaluate the probabilistic constraint. Thus, this is referred to as the approximate moment approach (AMA). The second approach, called the reliability index approach (RIA), describes the probabilistic constraint as a reliability index. Last, the performance measure approach (PMA) was proposed by converting the probability measure to a performance measure. A guide for selecting an appropriate method in RBDO is provided by comparing probabilistic design approaches for RBDO from the perspective of various numerical considerations. It has been found in the literature that PMA is more efficient and stable than RIA in the RBDO process. It is found that PMA is accurate enough and stable at an allowable efficiency, whereas AMA has some difficulties in RBDO process such as a second-order design sensitivity required for design optimization, an inaccuracy to measure a probability of failure, and numerical instability due to its inaccuracy. Consequently, PMA has several major advantages over AMA, in terms of numerical accuracy, simplicity, and stability. Some numerical examples are shown to demonstrate several numerical observations on the three different RBDO approaches. β = βt = MPP in first-order inverse reliability analysis

Journal of Mechanical Design, 2004

Because deterministic optimum designs obtained without taking uncertainty into account could lead to unreliable designs, a reliability-based approach to design optimization is preferable using a Reliability-Based Design Optimization (RBDO) method. A typical RBDO process iteratively carries out a design optimization in an original random space ͑X-space) and a reliability analysis in an independent and standard normal random space ͑U-space). This process requires numerous nonlinear mappings between X-and U-spaces for various probability distributions. Therefore, the nonlinearity of the RBDO problem will depend on the type of distribution of random parameters, since a transformation between X-and U-spaces introduces additional nonlinearity into the reliability-based performance measures evaluated during the RBDO process. The evaluation of probabilistic constraints in RBDO can be carried out in two ways: using either the Reliability Index Approach (RIA), or the Performance Measure Approach (PMA). Different reliability analysis approaches employed in RIA and PMA result in different behaviors of nonlinearity for RIA and PMA in the RBDO process. In this paper, it is shown that RIA becomes much more difficult to solve for non-normally distributed random parameters because of the highly nonlinear transformations that are involved. However, PMA is rather independent of probability distributions because it only has a small involvement with a nonlinear transformation.