| contributor author | Feng, Shaw C.;Moges, Tesfaye;Park, Hyunseop;Yakout, Mostafa;Jones, Albert T.;Ko, Hyunwoong;Witherell, Paul | |
| description abstract | Additive manufacturing (AM) for metals is rapidly transitioning to an accepted production technology, which has led to increasing demands for data analysis and software tools. The performance of laser-based powder bed fusion of metals (PBF-LB/M), a common metal AM process, depends on the accuracy of data analysis. Advances in data acquisition and analysis are being propelled by an increase in new types of in situ sensors and ex situ measurement devices. Measurements taken with these sensors and devices rapidly increase the volume, variety, and value of PBF-LB/M data but decrease the veracity of that data simultaneously. The number of new, data-driven software tools capable of analyzing, modeling, simulating, integrating, and managing that data is also increasing; however, the capabilities and accessibility of these tools vary greatly. Issues associated with these software tools are impacting the ability to manage and control PBF-LB/M processes and qualify the resulting parts. This paper investigates and summarizes the available software tools and their capabilities. Findings are then used to help derive a set of functional requirements for tools that are mapped to PBF-LB/M lifecycle activities. The activities include product design, design analysis, process planning, process monitoring, process modeling, process simulation, and production management. PBF-LB/M users can benefit from tools implementing these functional requirements implemented by (1) shortening the lead time of developing these capabilities, (2) adopting emerging, state-of-the-art, PBF-LB/M data and data analytics methods, and (3) enhancing the previously mentioned AM product lifecycle activities. | |
