Buildtech Solutions LLC

Moog Gmbh

George S Baggs

Aaron W Golding, Jr

Paul K Guerrier

George L Small

James F Van Oss

James A Regenor

Jason C Jones

A method for verifying and authenticating additive manufactured products utilizing extraterrestrial communication including generating a product geometry file, recording to a distributed transaction register stored on a server network having a plurality of node servers a first transaction reflecting the product geometry file, the first transaction having a first output associated with the first transaction and including a blockchain address, transmitting the first output between a terrestrial transceiver that is communicatively connected to the server network and an extraterrestrial transceiver that is communicatively connected to the terrestrial transceiver, and printing, with a 3D additive printer, a product that utilizes the product geometry file.

Outer space digital logistics system

The inventive disclosures pertain to an improved roofing-protection system that features enhanced leak-detection and analytical capabilities. The improved system features RFID-enabled moisture-sensing elements contained in a moisture-sensing membrane, and can measure impedance changes across the moisture-sensing membrane. In some variations, the system is designed to detect and measure moisture leakage and roof loading by way of biplanar capacitance measurements. RFID tags for moisture sensors are read by drones or robots and wirelessly transmitted to the Cloud/Internet for remote data analytics.

RFID-based moisture-sensing and leak-detection for roofing structures and methods of use

An additive manufacturing system uses a trained artificial intelligence module as part of a closed-loop control structure for adjusting the initial set of build parameters in-process to improve part quality. The closed-loop control structure includes a slow control loop taking into account in-process build layer images, and may include fast control loop taking into account melt pool monitoring data. The artificial intelligence module is trained using outputs from a plurality of convolutional neural networks (CNNs) tasked with evaluating build layer images captured in-process and images of finished parts captured post-process. The post process images may include two-dimensional images of sectioned finished parts and three-dimensional CAT scan images of finished parts.

Convolutional neural network evaluation of additive manufacturing images, and additive manufacturing system based thereon

The inventive disclosures are directed to an improved waterproofing membrane that features a polymer backing membrane with an integrated electrically-conductive layer that is used to create improved asphaltic waterproofing membranes and sodium-bentonite-clay waterproofing panels with intrinsic leak-detection capability for applications in below-grade waterproofing systems and above-grade air-barrier systems and horizontal roofing and deck-coating applications. Additionally, improved composite fluid-applied waterproofing membranes with intrinsic leak-detection capability is revealed.

Integrated waterproofing and drainage system with intrinsic leak detection for building structures and methods of use

The inventive disclosures are directed to a factory-controlled process for making improved, risk-optimized commercial-building waterproofing systems. The improved waterproofing-panel systems also include improved intrinsic leak-detection capabilities.

A complete decreased mistake-proof high-reliability waterproofing system is revealed that integrates the waterproofing membrane, drain panel, and abrasion-protected filter fabric, using a factory-controlled process; furthermore, the system incorporates intrinsic devices for installation verification and leak-detection, and the potential for in situ mapping of the functional topography of the waterproofing installation over time.

Growing community of inventors

Hamburg, NY, United States of America

George S Baggs