Marine crankshaft forgings are the key parts in the Marine power system, and their fatigue life has an important influence on the safe operation and economic benefit of the ship. Therefore, how to predict the fatigue life of Marine crankshaft forgings has become an important problem. This paper introduces the methods and application of fatigue life prediction for Marine crankshaft forgings, in order to provide useful reference for enterprises.
Fatigue life prediction method
Fatigue test method: Through the fatigue test in the laboratory or the field, simulate the actual working conditions of crankshaft forgings, obtain the fatigue performance data under different stress levels and cycles, so as to predict its fatigue life.
Finite element analysis: The finite element software is used to analyze the stress of crankshaft forgings, predict the stress distribution and stress concentration area under different working conditions, and then evaluate its fatigue life.
Fracture mechanics method: Based on fracture mechanics theory, the fatigue life of crankshaft forgings is predicted by analyzing the mechanism of crack initiation and propagation.
Neural network method: Using neural network algorithm to learn and train a large number of fatigue test data, establish a prediction model, and realize the fast prediction of fatigue life of crankshaft forgings.
Progress in applied research
Improvement of fatigue test technology: With the continuous development of test technology and equipment, the accuracy and efficiency of fatigue test have been significantly improved. At the same time, the fatigue test technology under special working conditions such as multi-axis fatigue and high temperature fatigue has been widely used.
Optimization of finite element analysis method: The continuous updating and improvement of finite element software makes the stress analysis and fatigue life prediction of crankshaft forgings more accurate and reliable. At the same time, the application of advanced functions such as multi-physics coupling analysis and material nonlinear analysis further improves the prediction accuracy.
Expansion of neural network algorithm: Neural network algorithm is more and more widely used in predicting fatigue life of crankshaft forgings. By optimizing the neural network structure and training algorithm, the accuracy and generalization ability of the prediction model can be improved, which provides strong support for practical application.
The promotion of interdisciplinary research: the fatigue life prediction of Marine crankshaft forgings involves many disciplines such as material science, mechanics and computer science. Interdisciplinary research is helpful to make comprehensive use of knowledge and technology in various fields and promote the innovation and development of fatigue life prediction methods.
The fatigue life prediction methods of Marine crankshaft forgings are constantly developing and improving, and the application range and accuracy of various prediction methods are also constantly improving. With the continuous progress of testing technology, finite element analysis method and neural network algorithm, as well as the advancement of multi-disciplinary research, the fatigue life prediction of Marine crankshaft forgings will be more accurate and reliable, and provide strong support for the safe operation of ships and the improvement of economic benefits.
