Analysis and Control of Cracking and Wrinkling at the End of Seamless Steel Tube with Multi-Pass Large Deformation Diameter-Reducing

Axial cracking and circumferential wrinkling are found at the end of seamless steel tubes during multi-pass large deformations pushing diameter-reducing (PDR), which seriously affects product quality. However, the cracking and wrinkling mechanism of PDR has not been elucidated yet. In this paper, the Equation of circumferential residual stress at the end was deduced from the warping deformation and shear stress. It is revealed that the circumferential residual stress in the end warping area from the inner to outer surface is tensile, and the generation mechanism of circumferential wrinkling on the inner wall at the end was revealed through the deformation analysis of PDR. The geometric model of the tube with periodic alternating variation of wall thickness was established to reveal the generation and development of circumferential wrinkling. In addition, the four-pass PDR experiments and simulations were developed to reveal the influence of reducing pass and wall thickness deviation on the end warpage, unevenness and circumferential residual tensile stress. The pushing-pulling diameter-reducing (PPDR) method was proposed to control the wrinkling and cracking. The simulation and experimental results showed that the end warpage, unevenness and circumferential residual tensile stress are all greatly decreased, and the risk of axial cracking and circumferential wrinkling is eliminated.

Numerical Analysis of Temperature Fields and Thermal Stress Fields in Heating Process of Large-Diameter Seamless Steel Pipe Blanks

In the piercing process of large-diameter seamless steel pipe blanks after heating, severe lateral cracks easily occur on the surface of pierced pipe blanks owing to the effects of the large temperature difference and thermal stress during the heating phase, influencing the finished product ratio and the safety and stability of use. Therefore, obtaining pipe blanks with uniform temperature distributions and low thermal stress peaks is the basis for the quality assurance of seamless pipes. In this paper, a study was conducted with large-diameter TP321 seamless pipe blanks with an example. The heating process of the pipe blank was investigated from two perspectives, temperature fields and thermal stress fields. Moreover, the effects of heating rate, initial furnace temperature, and hot-charging temperature were quantitatively analyzed. It was found that the peaks of the temperature difference and thermal stress both occurred at the early stage of heating. The temperature field and thermal stress distribution of the pipe blank gradually changed during heating. At the initial stage of heating, the temperature at the outer diameter edge was the highest, and the maximum thermal stress zones were concentrated on the inner hole edge and external surface. At the late stage of heating, the highest temperature zone and the maximum thermal stress zone were both focused on the inner hole surface. Lower initial furnace temperature and higher hot-charging temperature were more conducive to decreasing the maximum temperature difference and peak thermal stress, while the changes in heating rate had insignificant effects.

Prediction of Mechanical Properties of Seamless Steel Tubes Using Artificial Neural Networks

A bottleneck of laboratory analysis in process industries including steelmaking plants is the low sampling rate. Inference models using only variables measured online have then been used to made such information available in advance. This study develops predictive models for key mechanical properties of seamless steel tubes, by strength, ultimate tensile strength and hardness. A plant in Brazil was used as the case study. The sample sizes of some steel tube families given namely, yield a particular property are discrepant and sometimes very small. To overcome this sample imbalance and lack of representativeness, committees of predictive neural network models based on bagging predictors, a type of ensemble method, were adopted. As a result, all steel families for all properties have been satisfactorily described showing the correlations between targets and model estimates close to 99%. These results were compared to multiple linear regression, support vector machine and a simpler neural network. Such information available in advance favors corrective actions before complete tube production mitigating rework costs in general.