LAUSR

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.