A Method for Estimating a Probabilistic Design Factor

Osakue, Edward E.; Anetor, Lucky

doi:10.15623/ijret.2017.0608018

Cited by 4 publications

(11 citation statements)

References 9 publications

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“…The reliability and variability parameters define the reliability factor for a specific design. The lognormal model [44] has been applied in the re-design of different types of components and comparison with previous results showed very good to excellent agreement. These include the design of a tension bar and crane girder [45], design of a bolt and flange joint [46], design of a cyclically loaded cantilever [47], and design of shafts for bending and torsion [48].…”

Section: Nominal Probabilistic Design Factormentioning

confidence: 81%

“…However, higher reliability levels are usually required in practical situations; therefore, some probabilistic considerations cannot be avoided. Osakue and Anetor [44] formulated a lognormal reliability-based design factor model that considers design parameters as random variables and characterized them with mean values and coefficients of variation (cov). The cov of each design parameter is estimated using sensitivity analysis of the first order Taylor's series expansion.…”

Section: Nominal Probabilistic Design Factormentioning

confidence: 99%

“…In the lognormal reliability-based design factor model [44], the lognormal standard deviation of a design capacity model is expressed as:…”

Section: Nominal Probabilistic Design Factormentioning

confidence: 99%

“…Therefore it is necessary to adjust these nominal values for other reliability values. Table B2 gives values for some other reliability levels and were estimated using the method of [44]. The difference between these reliability factor values in Table B2 and those of AGMA is that the former is based on the lognormal probability density function while the latter is based on the standard normal probability distribution function [65].…”

Section: B30 Reliability Factormentioning

confidence: 99%

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An estimate of the pitting strength of steel materials

Osakue¹,

Anetor²,

Harris³

2021

FME Transactions

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A single expression for estimating the nominal pitting strength of steel materials, based on surface hardness, is developed from first principles for a reliability of 99% at 107 load cycles. It requires the hardness values to be measured in Vicker's hardness scale. The expression may be used for any steel material processed by hot rolling, cold drawing, quenching and tempering or case-hardening. The formulation incorporates a nominal design factor at 99% reliability which is estimated from a probabilistic model based on the lognormal probability density function. Pitting strength estimates from the expression are compared with those of American Gear Manufacturers Association (AGMA) estimates and data from other sources as indicated in Tables 3 and 4. The expression predicts lower values at low hardness but higher values at high hardness. The variance is between - 15.21% and 10.13% for through-hardened steels. For case-hardened steels, the variances range from 14.23% to 20.26% between the estimates and available data. These variances appear to be reasonable considering the many factors involved in pitting resistance. The main advantage of this study is that pitting strength of new steel materials may be estimated for initial design sizing without long and costly contact fatigue testing which of course is necessary for design validation. Also, the estimation method developed may be applied to other materials, metallic and non-metallic. Suggestions are made for estimating some pertinent pitting strength adjustment factors when considering field or service pitting strength.

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Section: Nominal Probabilistic Design Factormentioning

confidence: 81%

Section: Nominal Probabilistic Design Factormentioning

confidence: 99%

“…In the lognormal reliability-based design factor model [44], the lognormal standard deviation of a design capacity model is expressed as:…”

Section: Nominal Probabilistic Design Factormentioning

confidence: 99%

Section: B30 Reliability Factormentioning

confidence: 99%

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An estimate of the pitting strength of steel materials

Osakue¹,

Anetor²,

Harris³

2021

FME Transactions

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“…When tensile strength data is available, then for uneven materials such as gray cast iron and FA materials: In the lognormal reliability-based design factor model [40], the standard deviation of a design capacity model is expressed as:…”

Section: A10 Contact Stress Variabilitymentioning

confidence: 99%

Pitting strength estimate for cast iron and copper alloy materials

Osakue¹,

Anetor²,

Harris³

2021

FME Transactions

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An attempt is made to predict the pitting strength of cast iron and copper alloy materials from their compressive yield or compressive proof strength for a reliability of 99% at 107 load cycles. The compressive yield or compressive proof strength is related to the tensile strength of ductile cast iron and copper alloy materials by a proportionality factor. Two proportionality factors are used for brittle cast iron materials. The pitting strength formulation incorporates a nominal design factor at 99% reliability which is estimated from a probabilistic model based on the lognormal probability density function. Pitting strength estimates from the predictions are compared with those of American Gear Manufacturers Association (AGMA) estimates and data from other sources. The predicted values for gray cast irons had variances in the range of -11.28% to 25%. Ductile cast iron pitting strength estimates deviated from those of AGMA by -30.28% to 1.73% and 16.76% to 36.34% for Austempered ductile irons. The variances obtained for cast bronze were from 11.17% and 14.73%, but the sample size was small. These variances appear to be reasonable due to the many factors that can influence pitting resistance. Since pitting strength data for many grades of cast iron and copper alloys are not available (especially in the public domain), they may be estimated by the expressions developed in this study for initial design sizing. Also, the pitting strength of new cast iron and copper alloy materials could likewise be estimated for initial design sizing. This will eliminate long and costly contact fatigue testing at the initial design phases, which of course is necessary for design validation.

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Reliability Analysis of the Shaft Subjected to Twisting Moment and Bending Moment for The Exponential and Weibull Distributed Strength and Stress

Pasha,

Devi,

Maheswari

2024

IJST

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Objectives: The purpose of the present work is to design a shaft subjected to twisting moment, bending moment and combined twisting and bending moment by determine the reliability of the shaft. Methods: Probabilistic approach is considered to find the lifetime of shaft by taking stress as random variable. Exponential and Weibull distributions are used to find lifetime of the equipment stress is considered as exponential and Weibull random variable. When the shaft is subjected to combined twisting and bending moment, the stress is found by using the two theories: (i) The maximum shear stress theory is used for ductile materials such as mild steel. (ii) The maximum normal stress theory is used for brittle materials such as cast iron. Findings: Reliability of the shaft is derived subjected to twisting and bending moments. The reliability is computed and compared for changing of the twisting moment, bending moment and diameter of the shaft. Novelty: To design a shaft by using reliability theory and to find reliability of the shaft by using the exponential and Weibull distribution is the novel idea. Keywords: Reliability, Weibull distribution, Shaft, Twisting moment, Bending moment, Combined twisting and bending moment, Exponential distribution, Maximum normal stress theory, Maximum shear stress theory

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A Method for Estimating a Probabilistic Design Factor

Abstract: A probabilistic method for determining a design factor is presented based on the lognormal probability density function. Design parameters are characterized by mean values and coefficients of variation (covs)

Cited by 4 publications

References 9 publications

An estimate of the pitting strength of steel materials

An estimate of the pitting strength of steel materials

Pitting strength estimate for cast iron and copper alloy materials

Reliability Analysis of the Shaft Subjected to Twisting Moment and Bending Moment for The Exponential and Weibull Distributed Strength and Stress

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