Prediction of the Deformation of Aluminum Alloy Drill Pipes in Thermal Assembly Based on a BP Neural Network

The connection between the steel joint and aluminum alloy pipe is the weak part of the aluminum alloy drill pipe. Practically, the interference connection between the aluminum alloy rod and the steel joint is usually realized by thermal assembly. In this paper, the relationship between the cooling water flow rate, initial heating temperature and the thermal deformation of the steel joint in interference thermal assembly was studied and predicted. Firstly, the temperature data of each measuring point of the steel joint were obtained by a thermal assembly experiment. Based on the theory of thermoelasticity, the analytical solution of the thermal deformation of the steel joint was studied. The temperature function was fitted by the least square method, and the calculated value of radial thermal deformation of the section was finally obtained. Based on the BP neural network algorithm, the thermal deformation of steel joint section was predicted. Besides, a prediction model was established, which was about the relationship between cooling water flow rate, initial heating temperature and interference. The magnitude of interference fit of steel joint was predicted. The magnitude of the interference fit of the steel joint was predicted. A polynomial model, exponential model and Gaussian model were adopted to predict the sectional deformation so as to compare and analyze the predictive performance of a BP neural network, among which the polynomial model was used to predict the magnitude of the interference fit. Through a comparative analysis of the fitting residual (RE) and sum of squares of the error (SSE), it can be known that a BP neural network has good prediction accuracy. The predicted results showed that the error of the prediction model increases with the increase of the heating temperature in the prediction model of the steel node interference and related factors. When the cooling water velocity hit 0.038 m/s, the prediction accuracy was the highest. The prediction error increases with the increase or decrease of the velocity. Especially when the velocity increases, the trend of error increasing became more obvious. The analysis shows that this method has better prediction accuracy.

Effect of Preheating on Martensitic Transformation in the Laser Beam Welded AH36 Steel Joint: A Numerical Study

In this work, the effects of preheating temperatures on martensitic transformations in a laser beam-welded AH36 steel joint were observed using a numerical study. In the same weld, the martensitic contents increased slightly from the upper area, the middle area to the lower area, and simulated martensite contents in the fusion zone were slightly lower than that in the HAZ (Heat Affected Zone). Under different preheating temperatures, simulated martensitic contents decrease with the increase of the preheating temperature. According to the simulated results, the average cooling rate and the CCT (Continuous Cooling Transformation) diagram were drawn to analyze the relationships between preheating temperatures and martensitic transformations. Simulated martensitic contents agreed well with the experimental metallographic microstructures. Moreover, the measured microhardness was reduced with the increasing preheating temperature, and measured microhardness in HAZ was higher than that in the fusion zone. The accuracy of the simulation results was further confirmed. The main significance of this work is to provide a numerical model to design martensitic contents in order to control the performances of the weld, avoiding many tests.