Weibull Analysis of Mechanical Data for Castings II: Weibull Mixtures and Their Interpretation

Tiryakioğlu, Murat

doi:10.1007/s11661-014-2610-9

Cited by 19 publications

(7 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that for B201, fatigue life and quality index data was found to have Weibull mixtures, as indicated in Table 2. In such cases, the cumulative probability is expressed as [26];…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

On the Relationship between Structural Quality Index and Fatigue Life Distributions in Aluminum Aerospace Castings

Özdeş¹,

Tiryakioğlu²

2016

Metals

Self Cite

View full text Add to dashboard Cite

Abstract:Tensile and fatigue testing results of D357 and B201 aluminum alloy aerospace castings reported in the literature have been reanalyzed. Yield strength-elongation bivariate data have been used as a measure of the structural quality of castings, and converted into quality index. These results as well as fatigue data have been analyzed by using Weibull statistics. A distinct relationship has been observed between expected fatigue life and quality index. Moreover, probability of survival in fatigue life was found to be directly linked to the proportions of the quality index distributions in two different regions, providing further evidence about the strong relationship between elongation, i.e., structural quality and fatigue performance.

show abstract

“…Note that for B201, fatigue life and quality index data was found to have Weibull mixtures, as indicated in Table 2. In such cases, the cumulative probability is expressed as [26];…”

Section: Resultsmentioning

confidence: 99%

“…Note that for both Q T and N f , there are inflection points in the probability plots which are indicative of Weibull mixtures [25][26][27].…”

Section: Discussionmentioning

confidence: 99%

On the Relationship between Structural Quality Index and Fatigue Life Distributions in Aluminum Aerospace Castings

Özdeş¹,

Tiryakioğlu²

2016

Metals

Self Cite

View full text Add to dashboard Cite

show abstract

“…Consistent with the observations stated previously about Figure 2, it was noticed that in the BM region there were two Fe-containing particle size distributions: coarse particles in bands, and finer particles within the matrix. The probability density function (f) for the mixture of two distributions is written as [21]:…”

Section: Characterization Of the Effect Of Fsp On Microstructurementioning

confidence: 99%

Characterization of Microstructural Refinement and Hardness Profile Resulting from Friction Stir Processing of 6061-T6 Aluminum Alloy Extrusions

Netto

Tiryakioğlu

Eason

2018

Metals

Self Cite

View full text Add to dashboard Cite

In this study, the change in microstructure and microhardness adjacent to the tool during the friction stir processing (FSP) of 6061-T6 extrusions was investigated. Results showed that the as-received extrusions contained Fe-rich constituent particles with two distinct size distributions: coarse particles in bands and finer particles in the matrix. After FSP, Fe-containing particles exhibited single-size distribution and the coarse particles appeared to be completely eliminated through refinement. Microhardness tests showed the presence of four distinct zones and that hardness increased progressively from the dynamically recrystallized closest to the tool, outward through two distinct zones to the base material. The similarities and differences between the results of this study and others in the literature are discussed in detail.

show abstract

“…[15][16][17] The goodness-of-fit of linear regression line (i.e., R 2 ) was accordingly used to determine the Weibull behavior of the datasets. [18,19] Tiryakioglu [19] developed the following equation for the critical R 2 value to determine the Weibull behavior of a dataset:…”

Section: The Weibull Distribution Has Been Widely Used Tomentioning

confidence: 99%

“…[2,[9][10][11][12][13] Previous researchers suggested that this deviation could be due to the nature of the physical flaws (i.e., defects, such as porosity, low melting point intermetallic compounds, and segregation) in the material, [14,30] and the corresponding data points were interpreted to follow an underlying 3-p or mixed Weibull distribution. [15][16][17] However, more analysis (Eq. [25]) is still required to distinguish what is the actual reason of the deviation.…”

Section: A Determination Of Weibull Behavior Of Datasetsmentioning

confidence: 99%

Weibull Modulus Estimated by the Non-linear Least Squares Method: A Solution to Deviation Occurring in Traditional Weibull Estimation

Griffiths

Chen

2017

Metall Mater Trans A

View full text Add to dashboard Cite

The Maximum Likelihood method and the Linear Least Squares (LLS) method have been widely used to estimate Weibull parameters for reliability of brittle and metal materials. In the last 30 years, many researchers focused on the bias of Weibull modulus estimation, and some improvements have been achieved, especially in the case of the LLS method. However, there is a shortcoming in these methods for a specific type of data, where the lower tail deviates dramatically from the well-known linear fit in a classic LLS Weibull analysis. This deviation can be commonly found from the measured properties of materials, and previous applications of the LLS method on this kind of dataset present an unreliable linear regression. This deviation was previously thought to be due to physical flaws (i.e., defects) contained in materials. However, this paper demonstrates that this deviation can also be caused by the linear transformation of the Weibull function, occurring in the traditional LLS method. Accordingly, it may not be appropriate to carry out a Weibull analysis according to the linearized Weibull function, and the Non-linear Least Squares method (Non-LS) is instead recommended for the Weibull modulus estimation of casting properties.

show abstract

Weibull Analysis of Mechanical Data for Castings II: Weibull Mixtures and Their Interpretation

Cited by 19 publications

References 47 publications

On the Relationship between Structural Quality Index and Fatigue Life Distributions in Aluminum Aerospace Castings

On the Relationship between Structural Quality Index and Fatigue Life Distributions in Aluminum Aerospace Castings

Characterization of Microstructural Refinement and Hardness Profile Resulting from Friction Stir Processing of 6061-T6 Aluminum Alloy Extrusions

Weibull Modulus Estimated by the Non-linear Least Squares Method: A Solution to Deviation Occurring in Traditional Weibull Estimation

Contact Info

Product

Resources

About