August 2025 Volume 7

FORGING RESEARCH

we can infer what is happening inside the “black box” of the metal of the workpiece - internal temperature, internal flow stress changes as a function of strain, strain rate, temperature and microstructure evolution. This knowledge allows us to infer flow stress and microstructure state, effectively illuminating the “black box” of what happens inside the material during forging. Provided we accurately know the surface temperature, the geometry, the material properties and adiabatic heating, we can know the thermal distribution within the part as well because there is a single solution for the thermal distribution.

Figure 2: Example of toolpath generation identifying die geometry, location and number of blows, and reheat operations. In addition to predicting part geometry as a function of “heating and beating,” we also can predict locally varying microstructure evolution across the part as a function of thermomechanical processing (e.g., grain size from recrystallization, grain growth, and phase transformation from heating and cooling rates). With this ability, we can make structure-property predictions and anticipate total component performance as a function of processing. Given that there are many different routes to achieve the same part geometry, we can pick the route that will not only achieve our target geometry but the optimum part performance as well.

Figure 5: Sequence of stills from thermal video. Left to right—dies approaching workpiece; dies forging workpiece (and cooling the workpiece surface while also heating themselves from contact); dies retracting (and retaining heat); workpiece heat redistributing.

Figure 6: Left – Tie rod being forged (and scanned with 2D laser profilometer) in an Agility Forge. Center – near real-time reconstruction of geometry of part. Right – thermal camera measurements of surface temperature mapped to 3D mesh of part.

Figure 3: Physics-based Finite Element Modeling of part geometry, temperature, strain, strain rate and microstructure distribution throughout a part being forged by many incremental blows, modeled in the FEM code DEFORM.

Figure 7: 2D Laser profilometry scan of hot part at beginning and end of scan. (Observe blue line on part).

Figure 4: Example of heating of part (top row) via induction heating, and forging of part (bottom row) via forging dies from tool changer, all within the Agility Forge device. This manufacturing technology augments its in-situ material sensing with a real-time digital twin. This combination allows us to take remote geometry sensing (both hot and cold geometries), thermal imaging of the workpiece surface and the tooling and load-stroke monitoring from the ram. With this information,

Figure 8: In-situ reconstruction of 3D geometry of part: hot (red) and cold (blue).

FIA MAGAZINE | AUGUST 2025 71