Establishment of Thermal Ductile Fracture Criterion for As-Cast 42CrMo Steel and Its Application in Hot Ring Rolling Process

The casting-rolling compound forming process of ring parts is an advanced plastic forming technology that has been developed due to the merits of high efficiency and energy and material saving. However, cracks often occur during the hot ring rolling process, especially at the edges of the ring parts, which severely affects the forming quality. To predict and try to avoid the occurrence of cracks in the casting-rolling compound forming process of ring parts, the high-temperature fracture behaviors of as-cast 42CrMo steel were investigated by thermodynamic experiment method. The high-temperature tensile tests were carried out using the Gleeble-3500D thermomechanical simulator at various temperatures and strain rates. Stress-strain curves and fracture morphology were examined, through which the sensitivity of stress to temperature and strain rate and the effect of dynamic recrystallization and cavity evolution on fracture were found. The law of critical fracture strains was analyzed, and the model of critical fracture strain as a function of temperature and strain rate was established. Based on Oyane criterion, the thermal ductile fracture criterion was established in conjunction with the model of critical fracture strain. By embedding this thermal damage model into the finite element (FE) model for hot ring rolling of an as-cast 42CrMo ring, the damage prediction for this process was realized, and the thermal ductile fracture criterion was proved to be reliable. From the FE results for hot ring rolling, mechanism of damage and fracture in the hot ring rolling process was analyzed. The damage threshold C f is small, and the damage ratio D is large at the top and bottom edges of the inner surface area of the ring, which have the greatest propensity to cracking in the course of hot ring rolling. This is of great significance in terms of improving the forming quality of ring parts in the casting-rolling compound forming process.

Investigation of cavity filling in profile ring rolling process for T-shape ring

In this paper, key parameters affecting the cavity filling in single and double T-shape profile rings are comprehensively investigated via numerical and experimental analysis. A three-dimensional finite element model was developed in Abaqus\Explicit to assess the influence of crucial ring rolling process parameters, including feed speed, main roll rotational velocity, the existence and the absence of axial rolls on the cavity filling of single and T-shape rings and the main roll torque. Besides, a ring rolling machine was built to conduct practical experiments and validate the numerical evaluation, while for the first time, the role of the axial roll and the main roll torque on the quality of the cavity filling is experimentally evaluated. Power requirements and the final ring profile geometry were obtained by the simulation method, and the results were confirmed by the experiments. The results showed that axial rollers significantly reduced the cavity filling rate, and in contrast, the effect of mandrel feed speed and the main roll rotational velocity was much lower. Also, the axial forces were considerably less than the radial forces. However, the rolling operation was done in both radial and axial directions. The existence of axial rolls had an intensive effect on the process’ required power, as a result the main roll torque increased more than three times in case of applying axial rolls, compared with not considering them. Severe effects of axial rollers on increasing force and decreasing cavity filling rate can be attributed to frictional forces between the ring and axial rolls, restricted ring motion, which has to be compensated by a higher torque of the main roll. When the axial rolls are used, the material flow in the ring’s height direction is restricted. Therefore, the material cannot move easily to form the profile. All experimental and simulation results, including mandrel force, cavity filling, and ring profile geometry, were in good agreement, and in all cases, the simulation error was less than 10%.

Ring Rolling speed rolls optimization to improve ring quality and reduce production time

In this paper, an analysis of the production time reduction as a function of the Idle and Axial rolls speed law in a Ring Rolling process was examined. Starting from an industrial case study, the authors defined a new milling curve able to produce a better ring quality with lower loads. From this result, the authors tested the effect of the production time reduction till the 40% of the initial one. The Ide roll velocity was varied in a range between 0.71 and 1.13 mm/s while the Axial roll between 0.35 and 1.70 mm/s. Geometrical and load parameters have been taken into account to compare the results achieved. The authors identified in the external ring diameter and in the Idle roll maximum load the most critical parameter to control; in particular, a break-even point was determined in order to select a set of rolls speed laws able to produce a good quality ring with lower production time (about 20%) and lower loads (about 10 %). In this research both experimental and numerical approaches were followed.

Application of artificial neural network and genetic algorithm to predict and optimize load and torque in T-section profile ring rolling

Artificial neural network is implemented to predict the required load and torque in T-section profile ring rolling process for the first time in this study. Moreover, an optimal condition of T-section profile ring rolling process for specific limit of input factor is acquired using genetic algorithm technique. Various three-dimensional finite element simulations are carried out for different collections of process variables to obtain initial data for training and validation of the neural network. Besides, the finite element model is verified via comparison with the experimental results of the other investigators. The back-propagation algorithm is utilized to develop Levenberg–Marquardt feed-forward network and the optimum architecture is achieved by estimating the performance considering different number of hidden layers and neurons. It is concluded that results of artificial neural network predictions have an appropriate conformity with those ones from simulation and experiments. Moreover, a reasonable accuracy is obtained from the implemented model by which the prediction of ring rolling load and torque in different conditions can be achieved.