During the last three decades, simulating the forging process has developed into a mainstream tool. Numerous success stories have been reported by companies around the world. Applications have included die fill, defect formation, tool failure and overall process optimization. Initially, the emphasis was to simulate one forging operation independently of prior history.
The distinction between forging a cast and wrought microstructure was captured in the flow-stress model. This empirical method was generally successful. Very quickly, the sequence of operations was modeled because a production problem may occur early in the process. Hot forging models use a non-isothermal model and heat transfer between operations to capture the temperature influence on the workpiece flow stress. A cold-forming progression could be analyzed by maintaining the strain distribution in the workpiece to account for work hardening.
In the early 1990s, various processes required the simulation of sequential processes of a different pedigree. This opened up the topic of vertical integration. Recently, researchers and leading users of simulation software have migrated toward simulating the entire manufacturing process chain. For the most part, geometry development is mature in commercial software today. On the other hand, the microstructure models are the source of considerable and ongoing effort.
Figure 1. A typical forging sequence in which the simulation closely matches actual part production (Courtesy of LC Manufacturing, Inc.).
Simulating an individual forging operation was noteworthy 20 years ago. Simulation is typically used to predict die fill, defects, forging load and stress on tooling. While mature, the technology is still relatively new to some smaller shops or those with parts and processes perceived to be very simple. Optimizing the forging process using simulation is a quantum leap from the development tools available 25 years ago, which were creativity, experience and shop trials. Figure 1 shows a typical forging sequence where the simulation (left) matches the production parts with excellent accuracy
During the early years, it was clear that forging simulations would need to include influences from prior operations. When a part was heated in a furnace with an extended transfer time to the press or hammer, it was necessary to include transient heat-transfer models to establish the thermal gradient in the workpiece prior to forging. In cold forming, simulating the drawing operations prior to forming captured the plastic strain that influenced work hardening. In both cases, the prior operations could have a significant influence on predicted load and metal flow, but this was only scratching the surface. Events during melting or billet conversion could influence the forging. The cooling rate from the press could change the mechanical properties or residual stress of the final part. Changing a preform could dramatically influence one or more mechanical properties after heat treatment.
Figure 2. Manufacturing process alternatives
Current Material Modeling Examples
Figure 3. Simulations can also be used to predict microstructural properties of finished-forged parts.
As a separate example, predicting a quench crack during heat treatment is a challenge. During the PRO-FAST (DLA sponsored) Program in 2005, a team investigated a crack formed in a camshaft (Figure 4a) used in the Sikorsky Blackhawk helicopter. This Pyrowear 53 component was experiencing a high scrap rate due to a crack of unknown origin. A thorough analysis of the forging process was conducted, and nothing in the observation of the process or simulation indicated that the crack could have occurred during the forging.
On the other hand, a heat-treatment simulation predicted very high stresses during quenching (red in Figure 4b). The investigation was very thorough, and all participants were convinced that the fracture was initiated during quenching. Unfortunately, the lack of a concise fracture model (and criteria) diluted an otherwise very successful project.
Figure 4. Investigation of a helicopter part crack (a); computer-simulated determination of high stresses in quenching as the cause of the crack (b).
As a final example, modeling machining distortion is practical for industrial application in axisymmetric cases modeled in 2-D. In turbine-disk applications, heat treatment (especially quenching) induces significant residual stresses. A nickel-based superalloy disk illustrates this. After forging, the disk is heat treated at the solution temperature and quenched. To illustrate the effect of cooling rate on the residual stress, a slow quench (Figure 5a) and a fast quench (Figure 5b) were modeled in DEFORM-2D. As expected, the faster cooling rate produced significantly higher tensile residual stresses in the quenched disk. In Figure 5, compressive stress is shown in green while tensile stress is red.
After heat treatment, multiple machining passes were performed to reach the dimensions of the final part shape. Material was removed during each pass, resulting in a change to the stress field and geometry (Figure 6a and 6b). This distortion is the result of the disk finding a new equilibrium after material is removed. After machining was completed, the residual stresses were higher in the disk that was quenched at the higher cooling rate. Residual stresses in the final component will affect the part performance in service.
Experimental findings showed that the faster quench should have higher residual stress and, therefore, larger distortion upon machining. This is exactly what was observed in the simulation. The deflection in the fast-quenched disk was over five times that observed in the disk quenched at the slower cooling rate.
In this example, the distortion during a slot broaching was simulated after heat treatment. To predict the final distortion, the prior thermo-mechanical processing was considered. The axisymmetric disk was initially forged, solutionized and quenched. Machining passes were then modeled in 2-D to represent machining the top and bottom surfaces as well as the bore in the inside diameter.
Broaching slots in the rim of the disk required the model to be run in 3-D. The 2-D results were converted to a 3-D model for this operation. When the first slot was machined, the residual-stress distribution changed, causing distortion in the slot area. Each subsequent slot modified the stress field and distortion pattern further. While this example is geometrically simple, it represents a step in the development that will lead to the simulation of machining distortion for more complex parts in the future.
Figure 7. Sequenced simulation of slot broaching on a forged part. Broaching process pictured in gray. Red indicates higher residual stress.
Material Modeling Limitations
Strategically, the federal government is promoting a Materials Genome Initiative in conjunction with the Department of Defense, the National Science Foundation, national labs and the Department of Energy. This will permeate the project world for the foreseeable future. More than 60 companies and universities have pledged support. Tactically, leading companies are pushing the envelope with their current simulation tools and production problems. Material modeling and data to support this effort are key elements to both. Commercial software providers are actively integrating material models into mature applications. Researchers and analysts are pushing new capabilities to and past their limits.
This reminds me very much of the 1980s, when forging models were being developed.
Author John Walters is vice president of Scientific Forming Technologies Corporation, Columbus, Ohio, and a frequent contributor to FORGE magazine. He may be reached at 614- 451-8330 or firstname.lastname@example.org