PanOptimization PanX simulation software targets 1,000mm-class metal AM builds

PanOptimization, headquartered in State College, Pennsylvania, has highlighted its PanX simulation software for large-scale metal additive manufacturing. The platform is designed for machines with 1,000 mm-class build volumes and multi-laser configurations, addressing the challenge of simulating fine features (0.2 mm-class) within large envelopes. PanX uses Periodic Adaptivity and Multi-Grid Modeling to handle finite element analysis models with hundreds of millions to billions of elements on a standard engineering workstation. In a demonstrated aerospike example, the software meshed 26 million elements and 57 million nodes, simulating the full part with powder in 3.5 hours on a 60-core Intel Xeon CPU with 120 GB of RAM.

This matters because the metal AM industry is moving toward production-scale parts, where failed builds on large-format multi-laser systems carry significant cost. Traditional FEA tools typically cap at around 5 million elements, forcing simplifications that compromise accuracy for large parts. PanOptimization addresses a critical gap: the ability to model loose powder and the entire build plate for accurate thermal history and distortion calculations. The company claims that ignoring powder effectively simulates a different process than the one being run, with interlayer temperatures up to 300°C higher when powder is included. This positions PanX as a tool for the "too large, too complex" regime that competing simulation packages struggle to reach.

For engineering teams scaling metal AM production, PanX offers a practical path to virtual iteration on large parts without requiring supercomputing resources. The key execution challenge will be integrating this simulation fidelity into existing workflow pipelines and qualification documentation, particularly for aerospace and defense customers who demand traceable simulation data. Users should evaluate whether the software's accuracy claims hold across their specific material and machine combinations before relying on it for production decisions.