A Safety Factor Method for Reliability-Based Component Design

Yin, Jianhua; Du, Xiaoping

doi:10.1115/1.4049881

Cited by 13 publications

(5 citation statements)

References 44 publications

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“…Gupta et al 28) conducted stress analysis (von Mises) for various alloy materials and loading cases using ANSYS, concluding that aluminum alloys are lightweight but prone to deformation 29) . Therefore, components experiencing the highest von Mises stress should be reinforced in accordance with design safety standards, as a manufacturing consideration following ASME Standard 30) . Safety factor assisted in evaluating safety of a component or structure, even when using minimal dimensions 31) .…”

Section: Resultsmentioning

confidence: 99%

The Prediction of Stress and Safety Factor of Shock Absorber Based on Cyclic Loading Using Finite Element Method

Endra Dwi Purnomo,

Azka,

Hotma

et al. 2023

Evergreen

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The aim of this study was to predict the strength of shock absorber under cyclic loading, with the goal of supporting the development of suspension components for electric vehicles. Shock absorber was designed with specific material specifications to effectively dampen vibrations and dissipate kinetic energy. The process began with the initial drafting of the geometric structure, followed by the determination of boundary conditions using cyclic loading forces. Furthermore, mesh grid independence and convergence tests were carried out to assess the accuracy of the simulation, using Finite Element Method (FEM) with ANSYS software. The simulation was conducted to analyze deformation, von Mises stress, and safety factor. The results showed that the mesh grid independence test and convergence test assisted in determining the appropriate mesh size for model optimization and efficiency. Additionally, FEM simulation provided stress and deformation values that could identify critical areas within the components. Another important result was the gradual decline in safety factor value, starting from the sixth second and continuing until the end of the simulation under cyclic loading conditions. This transient behavior of safety factor value should be taken into consideration for design safety.

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Section: Resultsmentioning

confidence: 99%

The Prediction of Stress and Safety Factor of Shock Absorber Based on Cyclic Loading Using Finite Element Method

Endra Dwi Purnomo,

Azka,

Hotma

et al. 2023

Evergreen

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“…The relationship between SF and R has been demonstrated by Mischke [52] as an improvement [53,54]. Additionally, the SF technique has been proposed for reliability-based design optimization to reduce the complexity involved in reliability analysis [55]. Over-designed products can arise when engineers choose excessively high safety factors.…”

Section: Uncertainty Quantification With Latin Hypercube Sampling (Lhs)mentioning

confidence: 99%

Reliability-Based Topology Optimization with a Proportional Topology for Reliability

Kumkam,

Sleesongsom

2024

Aerospace

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This research proposes an efficient technique for reliability-based topology optimization (RBTO), which deals with uncertainty and employs proportional topology optimization (PTO) to achieve the optimal reliability structure. The recent technique, called proportional topology optimization for reliability (PTOr), uses Latin hypercube sampling (LHS) for uncertainty quantification. The difficulty of the double-loop nested problem in uncertainty quantification (UQ) with LHS can be alleviated by the power of PTO, enabling RBTO to be performed easily. The rigorous advantage of PTOr is its ability to accomplish topology optimization (TO) without gradient information, making it faster than TO with evolutionary algorithms. Particularly, for reliability-based topology design, evolutionary techniques often fail to achieve satisfactory results compared to gradient-based techniques. Unlike recent PTOr advancement, which enhances the RBTO performance, this achievement was previously unattainable. Test problems, including an aircraft pylon, reveal its performances. Furthermore, the proposed efficient framework facilitates easy integration with other uncertainty quantification techniques, increasing its performance in uncertainty quantification. Lastly, this research provides computer programs for the newcomer studying cutting-edge knowledge in engineering design, including UQ, TO, and RBTO, in a simple manner.

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“…These generally apply optimisation search of an objective function computed based on uncertainty. Reliability based optimisation methods can also be used to solve for the most probable point solution to the robust design problem (Jiang et al 2013;Hu & Du 2019;Yin & Du 2021). Surrogate models of the mean and variance can be fit as functions of design variables (Chen et al 1996;Fang, Li, & Sudjianto 2005;Allen et al 2006;Chen, Jin, & Sudjianto 2006).…”

Section: Related Workmentioning

confidence: 99%

“…RDM is more than a statistical experiment, it involves a multiple step workflow including identifying possible sources of variability, quantifying their relative contribution with noise experiments, generating ideas for design changes that may promote variation reduction and then quantifying the ability of design changes to reduce this variability through a further set of experiments. Modern computer-based methodological updates include use of uncertainty quantification (UQ), surrogate modelling, global sensitivity analysis (GSA) and optimisation (Chen et al 1996;Du & Chen 2001;Jin, Chen, & Simpson 2001;Du, Sudjianto, & Chen 2004;Fang, Li, & Sudjianto 2005;Allen et al 2006;Chen, Jin, & Sudjianto 2006;Jiang et al 2013;Jiang, Chen, & German 2016;Hu & Du 2019;Otto & Sanchez 2019;Otto, Wang, & Uyan 2019;Nellippallil et al 2020;Sanchez, Björkman, & Otto 2020;Yin & Du 2021).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty quantification and reduction using Jacobian and Hessian information

Sánchez

Otto

2021

Des. Sci.

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Robust design methods have expanded from experimental techniques to include sampling methods, sensitivity analysis and probabilistic optimisation. Such methods typically require many evaluations. We study design and noise variable cross-term second derivatives of a response to quickly identify design variables that reduce response variability. We first compute the response uncertainty and variance decomposition to determine contributing noise variables of an initial design. Then we compute the Hessian second-derivative matrix cross-terms between the variance-contributing noise variables and proposed design change variables. Design variable with large Hessian terms are those that can reduce response variability. We relate the Hessian coefficients to reduction in Sobol indices and response variance change. Next, the first derivative Jacobian terms indicate which design variable can shift the mean to maintain a desired nominal target value. Thereby, design changes can be proposed to reduce variability while maintaining a targeted nominal value. This workflow finds changes that improve robustness with a minimal four runs per design change. We also explore further computation reductions achieved through compounding variables. An example is shown on a Stirling engine where the top four variance-contributing tolerances and design changes identified through 16 Hessian terms generated a design with 20% less variance.

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A Safety Factor Method for Reliability-Based Component Design

Cited by 13 publications

References 44 publications

The Prediction of Stress and Safety Factor of Shock Absorber Based on Cyclic Loading Using Finite Element Method

The Prediction of Stress and Safety Factor of Shock Absorber Based on Cyclic Loading Using Finite Element Method

Reliability-Based Topology Optimization with a Proportional Topology for Reliability

Uncertainty quantification and reduction using Jacobian and Hessian information

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