Failure Analysis of Hydraulic Expanding Assembled Camshafts Using BP Neural Network and Failure Tree Theory

Abstract

Due to the complex and changeable working environment of assembled camshafts using tube hydroforming (THF) technology, the manifestations of failure, the causes of failure and the preventive measures for these failures are a major concern. Therefore, in view of this new connection technology for assembled camshafts, it is important to put forward a prediction and evaluation method of failure for hydraulic expanding assembled camshafts. In this study, an isometric-trilateral profile cam was used to complete the hydroforming connection with the hollow shaft (tube) under different hydraulic pressures. Orthogonal torsion experiment and laser measurement experiment were performed. Finite element analysis was carried out using ABAQUS 6.14 software, and relevant research data were obtained. A more accurate BP neural network model was constructed to predict the main failure factors of assembled camshafts. The failure manifestations of assembled camshafts are displayed by the experiment from the microscopic perspective. The causes of failure are analyzed by using the minimum cut set in the failure Tree (FT) theory. The effect of basic causes on the subsystems is analyzed, and the weight distribution of the main events in the FT is given. Finally, the specific measures to prevent failure are proposed from a macro perspective. The research is of great significance to study the failures of assembled camshafts in service to further the production, manufacturing, failure prevention, faults monitoring and performance improvement of assembled camshafts in the engine industry.