English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

. In this work, the effect of the quenching media (brine,water, and two types of naphthenic mineral oils) and the temperingtemperature (200, 400, 600  ∘ C) on the static mechanical propertiesand the fatigue life has been investigated using 300 fatigue and 36 statictension tests. S–N curves and standard deviations of fatigue life under eachheat treatment condition were calculated and shown. The fracture surfaces ofthe selected 11 specimens were observed by the scanning electron microscopeand the reasons of affecting the fatigue life were discussed. To estimatethe mean fatigue life under the conditions of any given temperingtemperature and cycle stress amplitude based on 300 fatigue tests, the meanfatigue life estimation method based on RBF neural network was presented andverified by 12 other fatigue tests. The test results have shown that (1) themean fatigue life decreases with the increase of tempering temperature forthe same quenching media, (2) the mean fatigue life using brine is more thanwater which is more than naphthenic mineral oils for the same temperingtemperature, and (3) the proposed method based on RBF neural network couldaccurately estimate the mean fatigue life when the tempering temperature andcyclic stress amplitude are given for each quenching medium.

mechanical sciences

Effect of Quenching Media and Tempering Temperature on Fatigue Property and Fatigue Life Estimation Based on RBF Neural Network of 0.44&amp;thinsp;% Carbon Steel