3D Technologies for Manufacturing and Maintenance

Group of images showing a FODHAT


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.

An image used as an example for people to understand the summary of a DADTMA
An image of three cogs with three different colors on them
An image of an iPad mockup where the iPad is displaying a DADTMA experimentation


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. DADTMA employs many of FTL’s 3D scanning and machine learning object detection algorithms, and combines these with an easy-to-use interface for adding and browsing data based on 3D environments and real-time timeline playback. DADTMA has been developed as a collaboration between FTL and the Navy’s Fleet Readiness Center Southeast in Jacksonville, Florida.

An image of something being 3D printed through Vame


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.

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