Additive Manufacturing Model-based Process Metrics (AM-PM)

Modeling additive manufacturing processes can be difficult due to the scale difference between the active processing point (e.g., a sub-millimeter melt pool) and the part itself. Typically, the tools used to model these processes are either too computationally intensive (due to high physical fidelity or inefficient computations) or are focused solely on either the microscale (e.g., microstructure) or macroscale (e.g., cracks). These pitfalls make the tools unsuitable for fast and efficient evaluations of additive manufacturing build files and parts. Failures in parts made by laser powder bed fusion (L-PBF) often come when there is a lack of fusion or overheating of the metal powder that causes areas of high porosity. AM-PM uses a point field-based method to model L-PBF process conditions from either the build instructions (pre-build) or in situ measurements (during the build). The AM-PM modeling technique has been tested in several builds including a Ti-6Al-4V test article that was divided into 16 parts, each with different build conditions. With AM-PM, calculations are performed faster than similar methods and the technique can be generalized to other additive manufacturing processes. The AM-PM method is at technology readiness level (TRL) 6 (system/subsystem model or prototype demonstration in a relevant environment) and is available for patent licensing. Additive manufacturing enables unrivaled design freedom and flexible fabrication of components from a wide range of materials including metals, composites, polymers, and ceramics. The near net shape parts are made by processes like sequential melting or layer-by-layer material deposition with a complex set of processing variables. The sequential nature of the process means that every step can impact the next and thus, tools to evaluate that risk before and during manufacturing are necessary. Inventors at the NASA Langley Research Center have developed a novel method to model and ingest point-wise process data to evaluate an additive manufacturing build and its file for issues by highlighting potential anomalies or other areas where the build may have issues with fusion of the material. The technique was originally developed for use in tandem with powder bed fusion additive manufacturing for aerospace parts and is capable of being used on consumer grade computers.