V. Mendoza, Carpenter Technology Corporation, Reading, PA
The main goals of process simulation in manufacturing are to reduce manufacturing/part development time and cost as well as increasing quality and productivity. The usage of finite element analysis techniques for the modeling of the metal forming process has become well established in recent years and the techniques have been successfully developed and applied with the increased computational capability of modern computers. The current effort is to demonstrate metal forming analysis, using the FE method, to understand the ultrasonic performance of several hot rolling pass schedules. i.e., porosity reduction and the evolution of the grain size. Results are very helpful is selecting the optimum geometry and process parameters to achieve the required specifications.
Summary: During continuous casting as well as conventional ingot casting, centerline defects are formed. It is well known that even if it is possible to improve the quality of the as cast material by means of the casting and cooling practice, it is not possible to avoid the problem entirely. Many efforts have been made to eliminate the porosity during hot rolling, by both purely empirical investigations and analytical models as a function of rolling parameters.
Metal flow is complicated in shape rolling; however by FE modeling it was found that the stress-state is closely related to porosity elimination. Due to inhomogeneous deformation, tensile stress is always present at the center in the axial direction of the rolled bar. In some cases, stresses in the spread direction and even the draft direction turns over to tensile, which indicates that those billet rolling geometries will not be sufficient to eliminate the porosity introduced in the casting process.
The objective of this study is to understand the ultrasonic performance of stainless steel (SS) hot rolled bars, i.e., porosity level around the center of the bars, and compare different pass schedule designs. Moreover, thermomechanical model is fully coupled with a recrystallization model to predict grain size evolution.
