AI Toolpath Generation

Real-time deformation and toolpath AI will achieve on-line part qualification milestone.

ForgeNest's AI-driven toolpath generation system will achieve a critical milestone: on-line part qualification. This will mean the system can determine — during the forging process itself — whether a part will meet dimensional and metallurgical specifications, and adjust the remaining forming operations accordingly. We are actively building this capability now.

The system will combine three AI subsystems working in concert:

Deformation Prediction Model -- A physics-informed neural network that will be trained on thousands of forging simulations and real-world sensor data. It will predict material flow and final geometry several steps ahead of the current operation.

Adaptive Toolpath Planner -- Given the predicted deformation state, this module will generate optimal press motions, die selections, and thermal management commands to converge on the target geometry.

In-Process Qualification Engine -- Using data from laser profilometry and embedded strain sensors, this module will continuously evaluate whether the evolving part shape remains within the qualification envelope.

The practical impact will be significant. Traditional forging requires extensive trial-and-error to develop process parameters for each new part, often consuming dozens of test billets. Our system will converge on acceptable parameters within 1-3 parts, and in many cases will produce a qualifying part on the first attempt.

This capability will be foundational to our expeditionary manufacturing concept, where the system will need to produce unfamiliar parts on demand without the luxury of iterative process development.

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