Comparative and assessment study of torsional fatigue life for different types of steel

Abstract

Different types of steel specimens were tested using low cycle torsional fatigue tests to evaluate the torsional behavior. During previous years many authors have developed empirical relationships related to stress amplitude with the life of failure in many types of steel materials. Studies continue to find the best experimental relationships for different subjects. In this study two main problems were considered: torsional fatigue study and comparing the behavior of different steel materials under the influence of torsional fatigue. The effect of temperature on the properties of these substances was also studied. A comparison and evaluation of torsional fatigue for different types of steel were found in this study. Three groups of steel specimen were selected for the present investigation, these included low carbon steel AISI 1020, stainless steel AISI 316L, and cold worked stainless steel AISI 304H. The tests were carried out for each group of the steel specimen using a fatigue machine under fully reversed low cycle at ambient temperature and 100 °C. The temperature range was chosen from room temperature to 100° C because the low carbon steel AISI 1020 material showed high ductility above 100 °C. The shear strain amplitude applied was selected between the max. and min. values of 0.18 and 0.02 respectively. A comparison was carried out between the three steel groups at ambient temperature, it was noticed that the ratio of life to failure for both AISI steels 316L and AISI 304H with respect to AISI 1020 showed an increase of 4 and 2.3 times respectively. Also, the ratio of life to failure showed an increase of 4 and 3.5 times respectively at 100 °C. That is mean the ratio of life to failure for AISI steel 316L with respect to AISI 1020 has no effect with the temperature change because their cycles of life have been affected in the same manner. AISI 304H showed a good withstand to the temperature change because the ratio of life to failure with respect to AISI 1020 has been increased.

Prediction model of low cycle fatigue life of 304 stainless steel based on genetic algorithm optimized BP neural network

“…The training of the BP neural network is to output the data neural network according to the system's input so that the trained BP network can predict the input and output of the system. According to the existing 500 sets of input and output data [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][40][41][42][43][44][45][46][47][48][49][50][51][52], 400 sets of them are selected as the training data of the network, and the remaining 100 sets are used as the test data to verify the fitting ability of the network. The training function uses the fast convergence L-M optimization algorithm trainlm function with fast convergence, and the specific parameters are set as training times 100, training accuracy 0.00001, and learning rate 0.1.…”