Statistically Modeling the Fatigue Life of Copper and Aluminum Wires Using Archival Data
It has been known for at least 150 years that fatigue life data exhibits a considerable amount of variability. Furthermore, statistically modeling fatigue life adequately is challenging. Different empirical approaches have been used, each of which has merit; however, none is appropriate universally....
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/13/8/1419 |
| _version_ | 1797583856908369920 |
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| author | D. Gary Harlow |
| author_facet | D. Gary Harlow |
| author_sort | D. Gary Harlow |
| collection | DOAJ |
| description | It has been known for at least 150 years that fatigue life data exhibits a considerable amount of variability. Furthermore, statistically modeling fatigue life adequately is challenging. Different empirical approaches have been used, each of which has merit; however, none is appropriate universally. Even when a sufficiently robust database exists, the scatter in the fatigue lives may be extremely large and difficult to characterize. The purpose of this work is to review traditional and more modern empirically based methodologies for estimating the statistical behavior of fatigue data. The analyses are performed on two historic sets of data for annealed aluminum wire and annealed electrolytic copper wire tested in reverse torsion fatigue. These data are readily available In publications. Specifically, the review considers a traditional method for stress-cycle (S-N) analysis which includes linear regression through load dependent medians and mean square error (MSE) confidence bounds. Another approach that is used is Weibull distribution estimation for each loading condition, from which estimations for the median behavior and confidence bounds are determined. The preferred technique is the development of a cumulative distribution functions for fatigue life, which contains aspects of traditional reliability, classical S-N, and applied loading modeling. Again, confidence bounds are estimated for this technique. Even though it is an empirical technique, there are mechanistic aspects that underlie the empiricism. This approach is suggested because the method is very robust, and the estimation is more accurate than the other methods. |
| first_indexed | 2024-03-10T23:44:55Z |
| format | Article |
| id | doaj.art-c2b67a63f7174c4d8a53fb9da9456cfc |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-10T23:44:55Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-c2b67a63f7174c4d8a53fb9da9456cfc2023-11-19T02:10:55ZengMDPI AGMetals2075-47012023-08-01138141910.3390/met13081419Statistically Modeling the Fatigue Life of Copper and Aluminum Wires Using Archival DataD. Gary Harlow0Mechanical Engineering and Mechanics, Lehigh University, 27 Memorial Drive W, Bethlehem, PA 18015, USAIt has been known for at least 150 years that fatigue life data exhibits a considerable amount of variability. Furthermore, statistically modeling fatigue life adequately is challenging. Different empirical approaches have been used, each of which has merit; however, none is appropriate universally. Even when a sufficiently robust database exists, the scatter in the fatigue lives may be extremely large and difficult to characterize. The purpose of this work is to review traditional and more modern empirically based methodologies for estimating the statistical behavior of fatigue data. The analyses are performed on two historic sets of data for annealed aluminum wire and annealed electrolytic copper wire tested in reverse torsion fatigue. These data are readily available In publications. Specifically, the review considers a traditional method for stress-cycle (S-N) analysis which includes linear regression through load dependent medians and mean square error (MSE) confidence bounds. Another approach that is used is Weibull distribution estimation for each loading condition, from which estimations for the median behavior and confidence bounds are determined. The preferred technique is the development of a cumulative distribution functions for fatigue life, which contains aspects of traditional reliability, classical S-N, and applied loading modeling. Again, confidence bounds are estimated for this technique. Even though it is an empirical technique, there are mechanistic aspects that underlie the empiricism. This approach is suggested because the method is very robust, and the estimation is more accurate than the other methods.https://www.mdpi.com/2075-4701/13/8/1419fatigue lifemean square errorstatistical modelingstress–life modelingWeibull distribution function |
| spellingShingle | D. Gary Harlow Statistically Modeling the Fatigue Life of Copper and Aluminum Wires Using Archival Data Metals fatigue life mean square error statistical modeling stress–life modeling Weibull distribution function |
| title | Statistically Modeling the Fatigue Life of Copper and Aluminum Wires Using Archival Data |
| title_full | Statistically Modeling the Fatigue Life of Copper and Aluminum Wires Using Archival Data |
| title_fullStr | Statistically Modeling the Fatigue Life of Copper and Aluminum Wires Using Archival Data |
| title_full_unstemmed | Statistically Modeling the Fatigue Life of Copper and Aluminum Wires Using Archival Data |
| title_short | Statistically Modeling the Fatigue Life of Copper and Aluminum Wires Using Archival Data |
| title_sort | statistically modeling the fatigue life of copper and aluminum wires using archival data |
| topic | fatigue life mean square error statistical modeling stress–life modeling Weibull distribution function |
| url | https://www.mdpi.com/2075-4701/13/8/1419 |
| work_keys_str_mv | AT dgaryharlow statisticallymodelingthefatiguelifeofcopperandaluminumwiresusingarchivaldata |