3D Technologies for Manufacturing and Maintenance
FTL has worked with most every kind of image acquisition platform, including UAV’s, UUV’s, and USV’s. Emerging “digital twin” approaches for maintaining electronic records of asset maintenance require the efficient combination of many types of images and data. To help the Navy electronically manage asset maintenance, FTL developed “DADTMA”, or Distributed Acquisition Digital Twin Maintenance Architecture, a software tool for acquiring and managing maintenance data in the field. FTL’s DADTMA quickly and inexpensively enables fleetwide trend monitoring, predictions, and informed planning. DADTMA acts as middleware between existing maintenance and data reporting software currently in use, automates maintenance procedures with serial connected inspection tools and intelligent software tools, and stores the design and maintenance history of military and commercial assets on the cloud in a hierarchical, relational database architecture. The result is a vast, secure, searchable asset digital twin consisting of disparate datatypes throughout asset and fleet lifetimes, accessible to any authorized personnel for planning, data collection and analysis activities. This new technology is asset, datatype, and procedure agnostic, aiding maintenance and assessment of any asset type from military fighter jets to offshore wind turbines, fitting seamlessly into current maintenance procedures. DADTMA is currently being piloted at the Navy’s Fleet Readiness Center Southeast in Jacksonville, Florida.
FTL has current and active machine vision programs for the Navy, Air Force, and Northrop Grumman, most notably FTL’s FODHAT (FOD Or Defect Hazard Analysis Twin) machine vision and inspection systems. FODHAT uses neural network-enhanced 2D and 3D imaging for autonomous inspection guidance during manufacturing. FTL has experience designing high fidelity vision capture and sensor systems including video, still, time-of-fight, and LIDAR. For FODHAT, FTL has combined high-resolution video capture with artificial intelligence algorithms to enable adaptive target identification detecting millimeter-sized discrepancies in fasteners, brackets, and connectors. This system has the potential to save aircraft manufacturers millions in manual inspection, which is currently a time-consuming process highly susceptible to human error.
FTL works with the Center for eDesign to develop tools for next-generation manufacturing systems. VAME (Volumetric Additive Manufacturing Metadata Engine) is a software tool that brings metallic Additive Manufacturing (AM) capabilities and defect detection into the digital design process. It uses a voxel representation on top of conventional CAD part models to store design and process information that affects the viability of AM parts. VAME provides actionable alerts on AM design issues and pinpoints exact voxels where an issue has arisen, improving dependability and certification of AM parts.
Using VAME, 3D printer data can be recorded during the fabrication of an AM part and stored geometrically by voxel to provide a pedigree that details the fabrication process. The software tool is mature and real-time data collection and voxelization of 3 million data points during an AM test fabrication has been demonstrated, capturing laser power, melt pool diameter, melt pool temperature, surface finish, build head dynamics, and other parameters. Using VAME, a part data package with 1TB of data can be easily browsed, correlated, and evaluated.