Experimental investigation and artificial neural network modeling of warm galvanization and hardened chromium coatings thickness effects on fatigue life of AISI 1045 carbon steel

Kashyzadeh, K. R ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1007/s11668-017-0362-8
  3. Abstract:
  4. In the present study, the main purpose is investigation of the coatings thickness effect on the fatigue life of AISI 1045 steel. Herein, two different coatings of warm galvanization and hardened chromium have been used on the specimens. Fatigue tests were performed on specimens with different coating thicknesses of 13 and 19 µm. In the high-cycle level, S–N curves are extracted with 13 points for each sample. The results show that the galvanized coating is the most appropriate coating with low thickness, but with significant increasing of coating thickness, the best choice is hardened chromium coating. However, artificial neural network (ANN) has been used as an efficient approach instead of various and costly tests to predict and optimize the engineering problems. In this study, fatigue life of AISI 1045 steel was modeled by means of ANN. Back propagation (BP) error algorithm is developed to network’s training. The experimental data are employed in order to train the network. ANN’s testing is accomplished using test data which were not used during networks training. Amplitude stress and thickness of coatings are regarded as input parameters, and fatigue life is gathered as an output parameter of the network. A comparison was made between experimental and predicted data. The predicted results were in admissible agreement with experimental ones, which indicate that developed neural network can be used for modeling the mentioned process. © 2017, ASM International
  5. Keywords:
  6. Artificial neural network ; Coating ; Fatigue life ; Hardened chromium ; Backpropagation ; Backpropagation algorithms ; Carbon ; Carbon steel ; Chromium ; Cost engineering ; Fatigue crack propagation ; Fatigue of materials ; Fatigue testing ; Galvanizing ; Hardening ; Metal coatings ; Neural networks ; Thickness measurement ; Artificial neural network modeling ; Chromium coatings ; Coating thickness ; Engineering problems ; Experimental investigations ; Galvanized coatings ; Output parameters ; Warm galvanization ; Coatings
  7. Source: Journal of Failure Analysis and Prevention ; Volume 17, Issue 6 , 2017 , Pages 1276-1287 ; 15477029 (ISSN)
  8. URL: https://link.springer.com/article/10.1007/s11668-017-0362-8