A DEFORM-3D simulation modeled the conversion of a superalloy ingot into billet, via open die cogging, and the grain size changes during the process.
Product: DEFORM Premier; DEFORM-3D
Summary:
Open die cogging processes convert a coarse, cast grain structure into a fine, wrought microstructure. Cogging sequences involve many heats, passes and forging bites during a defined processing schedule. The DEFORM-3D simulation of this Alloy 718 cogging sequence modeled deformation, thermal and microstructural response. It predicted where a refined, fine grain microstructure was produced (as shown in red) relative to an unrefined, coarse grain structure.
Case Study:
Cogging, an open die forging process, is used to convert cast ingot into forged billet. The material is locally compressed between two dies which “bite” their way along the length of the ingot. As a result, the cross section is reduced and the length increases. In addition, thermo-mechanical processing (TMP: plastic deformation at high temperature) breaks down the coarse, cast grains and provides a fine grained, recrystallized microstructure. Cogging is used in the supply of aerospace billet, which is subsequently cut up and forged into critical rotating components, such as turbine disks.
A typical cogging schedule contains a number of heats. A single heat is where the ingot is taken from the furnace, subjected to a number of cogging passes and returned to the furnace. Each heat contains possibly four or more cogging passes, with ingot rotations in-between. Each pass contains several small forging bites, with the dies generally operating from one end of the billet to the other.
Simulating cogging in a conventional manner would involve manually setting up each operation as a discrete simulation. In just a single heat, the engineer would have to model ingot transfer from furnace to forge, ingot reorientation for the first pass, ingot positioning for the first bite, the first forging bite, ingot repositioning for the second bite, the second bite and so on. An additional heat transfer simulation would be required to account for the time between passes. After the final pass, the ingot would be reheated in the furnace.
In a single heat with 16 passes, a user could be faced with setting up and performing nearly 300 back-to-back simulations. It is easy to understand the magnitude of this task. Manual processing would be impractical.
The DEFORM system offers a special preprocessor that enables an engineer to set up the complete cogging schedule in one easy-to-define project. Standard industrial billet, die and manipulator shapes are included, while a CAD interface allows flexible geometry input. Process inputs include the number of heats, number of passes, rotation information, bite size and time between bites/passes. Thanks to these tools, set up of the complex process definition will only take a fraction of the time of the simulation.
Forging a large diameter turbine disk to meet today’s requirements necessitates the use of a fine-grained billet of a corresponding large diameter. If the cast ingot diameter is only marginally larger than the required billet size, upsetting is initially carried out. This increases the diameter, reduces the length and the deformation provides thermo-mechanical processing that recrystallizes the microstructure. Subsequent cogging operations reduce the diameter to its original size and provide further thermo-mechanical processing.
As part of an aerospace material supply project, DEFORM was used to analyze this single heat upset/cogging schedule. After upsetting to large diameter, Alloy 718 was cogged for 16 passes in octagonal and bioctagonal configurations. The end result was a rough round billet at the original, pre-upset diameter. The upset-to-cogging cycle may be repeated numerous times to achieve the specified microstructure in a large diameter billet.
DEFORM provides the process designer with an efficient method of obtaining critical microstructure information. Simulation has been proven to allow engineers to develop optimized processes by trial and error on the computer. Improved process control is possible with a better understanding of the most important process parameters.
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