Rapid induction bonding of composites, plastics, and metals

The Toroid Bonding Gun is and induction heating device. It is a self contained, portable, low powered induction welding system developed for bonding or joining plastic, ceramic, or metallic parts. Structures can be bonded in a factory or in a the field. This type of equipment allows for applying heat directly to the bond lines and/or to the adhesives without heating the entire structure, supports, and fixtures of a bonding assembly. The induction heating gun originally developed for use in the fabrication of space Gangs of bonders are now used to rapidly join composite sheet and structural components. Other NASA-developed applications of this bonding technique include the joining of thermoplastic composites, thermosetting composites, metals, and combinations of these materials

Ceramic susceptor for induction bonding of metals, ceramics, and plastics

A thin (.005) flexible ceramic susceptor (carbon) was discovered. It was developed to join ceramics, plastics, metals, and combinations of these materials using a unique induction heating process. Bonding times for laboratory specimens comparing state of the art technology to induction bonding were cut by a factor of 10 to 100 times. This novel type of carbon susceptor allows for applying heat directly and only to the bondline without heating the entire structure, supports, and fixtures of a bonding assembly. The ceramic (carbon film) susceptor produces molten adhesive or matrix material at the bond interface. This molten material flows through the perforated susceptor producing a fusion between the two parts to be joined, which in many instances has proven to be stronger than the parent material. Bonding can be accomplished in 2 minutes on areas submitted to the inductive heating. Because a carbon susceptor is used in bonding carbon fiber reinforced plastics and ceramics, there is no radar signature or return making it an ideal process for joining advanced aerospace composite structures

Method of Attaching Strain Gauges to Various Materials

A method is provided to bond strain gauges to various materials. First, a tape with an adhesive backing is placed across the inside of the fixture frame. The strain gauge is flatly placed against the adhesive backing and coated with a thin, uniform layer of adhesive. The tape is then removed from the fixture frame and placed, strain gauge side down, on the material to be tested. If the material is a high reluctance material, the induction heating source is placed on the tape. If the material is a low reluctance material, a plate with a ferric side and a rubber side is placed, ferric side down, onto the tape. The induction heating source is then placed upon the rubber side. If the material is an insulator material, a ferric plate is placed on the tape. The induction heating source is then placed on the ferric plate. The inductive heating source then generates frequenty from 60 to 70 kilocycles to inductively heat either low reluctance material, ferric side, of ferric plate and provides incidental pressure of approximately five pounds per square inch to the tape for two minutes, thoroughly curing the adhesive. The induction heating source, and, if necessary, the plate or ferric plate, are then removed from the tape after one minute. The tape is then removed from the bonded strain gauge

Deep Learning Assisted Robust Detection Techniques for a Chipless RFID Sensor Tags

In this paper, we present a new approach for robust reading of identification and sensor data from chipless RFID sensor tags. For the first time, Machine Learning (ML) and Deep Learning (DL) regression modelling techniques are applied to a dataset of measured Radar Cross Section (RCS) data that has been derived from large-scale robotic measurements of custom-designed, 3-bit chipless RFID sensor tags. The robotic system is implemented using the first-of-its-kind automated data acquisition method using an ur16e industry-standard robot. A large data set of 9,600 Electromagnetic (EM) RCS signatures collected using the automated system is used to train and validate four ML models and four 1-dimensional Convolutional Neural Network (1D CNN) architectures. For the first time, we report an end-to-end design and implementation methodology for robust detection of identification (ID) and sensing data using ML/DL models. Also, we report, for the first time, the effect of varying tag surface shapes, tilt angles, and read ranges that were incorporated into the training of models for robust detection of ID and sensing values. The results show that all the models were able to generalise well on the given data. However, the 1D CNN models outperformed the conventional ML models in the detection of ID and sensing values. The best 1D CNN model architectures performed well with a low Root Mean Square Error (RSME) of 0.061 (0.87%) for tag ID and 0.0241 (3.44%) error for the capacitive sensing

Minute-of-Arc Resolution Gamma ray Imaging Experiment—MARGIE

MARGIE (Minute-of-Arc Resolution Gamma-ray Imaging Experiment) is a large area(∼104 cm2), wide field-of-view (∼1 sr), hard X-ray/gamma-ray (∼20–600 keV) coded-mask imaging telescope capable of performing a sensitive survey of both steady and transient cosmic sources. MARGIE has been selected for a NASA mission-concept study for an Ultra Long Duration (100 day) Balloon flight. We describe our program to develop the instrument based on new detector technology of either cadmium zinc telluride (CZT) semiconductors or pixellated cesium iodide (CsI) scintillators viewed by fast-timing bi-directional charge-coupled devices (CCDs). The primary scientific objectives are to image faint Gamma-Ray Bursts (GRBs) in near-real-time at the low intensity (high-redshift) end of the logN-logS distribution, thereby extending the sensitivity of present observations, and to perform a wide field survey of the Galactic plane

MARGIE: A gamma-ray burst ultra-long duration balloon mission

We are designing MARGIE as a 100 day ULDB mission to: a) detect and localize gamma-ray bursts; and b) survey the hard X-ray sky. MARGIE will consist of one small field-of-view (FOV) and four large FOV coded mask modules mounted on a balloon gondola. The burst position will be calculated onboard and disseminated in near-real time, while information about every count will be telemetered to the ground for further analysis. In a 100-day mission we will localize ∼40 bursts with peak photon fluxes from 0.14 to ∼5 ph cm−2 s−1 using 1 s integrations; the typical localization resolution will be better than ∼2 arcminutes

A Design Methodology for Sensing-Ready Concentric Rings-Based Chipless RFID Tags With Effective Spectrum Use and High Coding Capacity

This paper introduces an innovative strategy for the development of sensing-ready concentric rings-based chipless radio frequency identification (CRFID) tags. Our approach is marked by the novel use of exponentially increasing spacing, a significant departure from the conventional uniform spacing method. This innovative design results in an impressive 88.2% improvement in tag data encoding capacity compared to traditional designs. Importantly, our design framework not only advances the current state of CRFID tag technology but also methodically lays the foundation for future integration of high-resolution sensing capabilities. This is achieved by strategically utilizing the innermost ring as a prospective sensing site, complemented by the implementation of nulls for data encoding achieved through the addition of an extra ring at the tag’s outermost edge. Notably, all these features represent advancements that have not been demonstrated in previously published concentric rings-based CRFID tags. To empirically validate our methodology, we have developed and tested 18-bit example tags optimized for operation within the ultrawideband (UWB) spectrum, covering a range from 3.1 to 10.6 GHz. The radar cross-section (RCS) response of these tags exhibits well-distributed resonances, culminating in a high encoding capacity of 17.65 bits/λ2/GHz. Preliminary results using capacitors connected to the innermost ring underscore the future sensing potential of our tags, setting the stage for more advanced sensing implementations in subsequent research

Ariel - Volume 6 Number 4

Editors Mark Dembert J.D. Kanofsky Frank Chervenak John Lammie Curt Cummings Entertainment Robert Breckenridge Joe Conti Gary Kaskey Photographer Larry Glazerman Overseas Editor Mike Sinason Humorist Jim McCann Staff Ken Jaffe Bob Sklaroff Halley Faust Jim Burk