Engineering topics

Decker, Arthur J.

Publications and source records attributed to Decker, Arthur J..

At least 19 records

NASA NTRS2006

Optimization of Training Sets For Neural-Net Processing of Characteristic Patterns From Vibrating Solids

An artificial neural network is disclosed that processes holography generated characteristic pattern of vibrating structures along with finite-element models. The present invention provides for a folding operation for conditioning training sets for optimally training forward-neural networks to process characteristic fringe pattern. The folding pattern increases the sensitivity of the feed-forward network for detecting changes in the characteristic pattern The folding routine manipulates input pixels so as to be scaled according to the location in an intensity range rather than the position in the characteristic pattern.

Decker, Arthur J.↗

NASA NTRS2005

Optical Tweezers and Optical Trapping Improved for Future Automated Micromanipulation and Characterization

Optical trap arrays are being developed at the NASA Glenn Research Center for holding, manipulating, and optically interrogating arrays of nanotube sensors. The trap arrays, for example, might be used to arrange arrays of chemical sensors for insertion onto a chip in liquid, air, and vacuum environments. Neural-network-controlled spatial light modulators (SLMs) are to generate and control the trap positions and trap profiles in three dimensions.

Wrbanek, Susan Y.↗

NASA NTRS2004

Neural Network for Image-to-Image Control of Optical Tweezers

A method is discussed for using neural networks to control optical tweezers. Neural-net outputs are combined with scaling and tiling to generate 480 by 480-pixel control patterns for a spatial light modulator (SLM). The SLM can be combined in various ways with a microscope to create movable tweezers traps with controllable profiles. The neural nets are intended to respond to scattered light from carbon and silicon carbide nanotube sensors. The nanotube sensors are to be held by the traps for manipulation and calibration. Scaling and tiling allow the 100 by 100-pixel maximum resolution of the neural-net software to be applied in stages to exploit the full 480 by 480-pixel resolution of the SLM. One of these stages is intended to create sensitive null detectors for detecting variations in the scattered light from the nanotube sensors.

Decker, Arthur J.↗

NASA NTRS2004

Optical Calibration Process Developed for Neural-Network-Based Optical Nondestructive Evaluation Method

A completely optical calibration process has been developed at Glenn for calibrating a neural-network-based nondestructive evaluation (NDE) method. The NDE method itself detects very small changes in the characteristic patterns or vibration mode shapes of vibrating structures as discussed in many references. The mode shapes or characteristic patterns are recorded using television or electronic holography and change when a structure experiences, for example, cracking, debonds, or variations in fastener properties. An artificial neural network can be trained to be very sensitive to changes in the mode shapes, but quantifying or calibrating that sensitivity in a consistent, meaningful, and deliverable manner has been challenging. The standard calibration approach has been difficult to implement, where the response to damage of the trained neural network is compared with the responses of vibration-measurement sensors. In particular, the vibration-measurement sensors are intrusive, insufficiently sensitive, and not numerous enough. In response to these difficulties, a completely optical alternative to the standard calibration approach was proposed and tested successfully. Specifically, the vibration mode to be monitored for structural damage was intentionally contaminated with known amounts of another mode, and the response of the trained neural network was measured as a function of the peak-to-peak amplitude of the contaminating mode. The neural network calibration technique essentially uses the vibration mode shapes of the undamaged structure as standards against which the changed mode shapes are compared. The published response of the network can be made nearly independent of the contaminating mode, if enough vibration modes are used to train the net. The sensitivity of the neural network can be adjusted for the environment in which the test is to be conducted. The response of a neural network trained with measured vibration patterns for use on a vibration isolation table in the presence of various sources of laboratory noise is shown. The output of the neural network is called the degradable classification index. The curve was generated by a simultaneous comparison of means, and it shows a peak-to-peak sensitivity of about 100 nm. The following graph uses model generated data from a compressor blade to show that much higher sensitivities are possible when the environment can be controlled better. The peak-to-peak sensitivity here is about 20 nm. The training procedure was modified for the second graph, and the data were subjected to an intensity-dependent transformation called folding. All the measurements for this approach to calibration were optical. The peak-to-peak amplitudes of the vibration modes were measured using heterodyne interferometry, and the modes themselves were recorded using television (electronic) holography.

Decker, Arthur J.↗

NASA NTRS2004

Optics-Only Calibration of a Neural-Net Based Optical NDE Method for Structural Health Monitoring

A calibration process is presented that uses optical measurements alone to calibrate a neural-net based NDE method. The method itself detects small changes in the vibration mode shapes of structures. The optics-only calibration process confirms previous work that the sensitivity to vibration-amplitude changes can be as small as 10 nanometers. A more practical value in an NDE service laboratory is shown to be 50 nanometers. Both model-generated and experimental calibrations are demonstrated using two implementations of the calibration technique. The implementations are based on previously published demonstrations of the NDE method and an alternative calibration procedure that depends on comparing neural-net and point sensor measurements. The optics-only calibration method, unlike the alternative method, does not require modifications of the structure being tested or the creation of calibration objects. The calibration process can be used to test improvements in the NDE process and to develop a vibration-mode-independence of damagedetection sensitivity. The calibration effort was intended to support NASA s objective to promote safety in the operations of ground test facilities or aviation safety, in general, by allowing the detection of the gradual onset of structural changes and damage.

Decker, Arthur J.↗

NASA NTRS2004

Examples of Current and Future Uses of Neural-Net Image Processing for Aerospace Applications

Feed forward artificial neural networks are very convenient for performing correlated interpolation of pairs of complex noisy data sets as well as detecting small changes in image data. Image-to-image, image-to-variable and image-to-index applications have been tested at Glenn. Early demonstration applications are summarized including image-directed alignment of optics, tomography, flow-visualization control of wind-tunnel operations and structural-model-trained neural networks. A practical application is reviewed that employs neural-net detection of structural damage from interference fringe patterns. Both sensor-based and optics-only calibration procedures are available for this technique. These accomplishments have generated the knowledge necessary to suggest some other applications for NASA and Government programs. A tomography application is discussed to support Glenn's Icing Research tomography effort. The self-regularizing capability of a neural net is shown to predict the expected performance of the tomography geometry and to augment fast data processing. Other potential applications involve the quantum technologies. It may be possible to use a neural net as an image-to-image controller of an optical tweezers being used for diagnostics of isolated nano structures. The image-to-image transformation properties also offer the potential for simulating quantum computing. Computer resources are detailed for implementing the black box calibration features of the neural nets.

Decker, Arthur J.↗

NASA NTRS2003

Manipulation of Micro Scale Particles in Optical Traps Using Programmable Spatial Light Modulation

1064 nm light, from an Nd:YAG laser, was polarized and incident upon a programmable parallel aligned liquid crystal spatial light modulator (PAL-SLM), where it was phase modulated according to the program controlling the PAL-SLM. Light reflected from the PAL-SLM was injected into a microscope and focused. At the focus, multiple optical traps were formed in which 9.975 m spheres were captured. The traps and the spheres were moved by changing the program of the PAL-SLM. The motion of ordered groups of micro particles was clearly demonstrated.

Seibel, Robin E.↗

NASA NTRS2003

Neural-Net Based Optical NDE Method for Structural Health Monitoring

This paper answers some performance and calibration questions about a non-destructive-evaluation (NDE) procedure that uses artificial neural networks to detect structural damage or other changes from sub-sampled characteristic patterns. The method shows increasing sensitivity as the number of sub-samples increases from 108 to 6912. The sensitivity of this robust NDE method is not affected by noisy excitations of the first vibration mode. A calibration procedure is proposed and demonstrated where the output of a trained net can be correlated with the outputs of the point sensors used for vibration testing. The calibration procedure is based on controlled changes of fastener torques. A heterodyne interferometer is used as a displacement sensor for a demonstration of the challenges to be handled in using standard point sensors for calibration.

Decker, Arthur J.↗

NASA NTRS2003

Optical Tweezers Array and Nimble Tweezers Probe Generated by Spatial- Light Modulator

An optical tweezers is being developed at the NASA Glenn Research Center as a visiblelight interface between ubiquitous laser technologies and the interrogation, visualization, manufacture, control, and energization of nanostructures such as silicon carbide (SiC) nanotubes. The tweezers uses one or more focused laser beams to hold micrometer-sized particles called tools (sometimes called tips in atomic-force-microscope terminology). A strongly focused laser beam has an associated light-pressure gradient that is strong enough to pull small particles to the focus, in spite of the oppositely directed scattering force; "optical tweezers" is the common term for this effect. The objective is to use the tools to create carefully shaped secondary traps to hold and assemble nanostructures that may contain from tens to hundreds of atoms. The interaction between a tool and the nanostructures is to be monitored optically as is done with scanning probe microscopes. One of the initial efforts has been to create, shape, and control multiple tweezers beams. To this end, a programmable spatial-light modulator (SLM) has been used to modify the phase of a laser beam at up to 480 by 480 points. One program creates multiple, independently controllable tweezer beams whose shapes can be tailored by making the SLM an adaptive mirror in an interferometer (ref. 1). The beams leave the SLM at different angles, and an optical Fourier transform maps these beams to different positions in the focal plane of a microscope objective. The following figure shows two arrays of multiple beams created in this manner. The patterns displayed above the beam array control the intensity-to-phase transformation required in programming the SLM. Three of the seven beams displayed can be used as independently controllable beams.

Decker, Arthur J.↗

NASA NTRS2003

Neural-net Processed Electronic Holography for Rotating Machines

This report presents the results of an R&D effort to apply neural-net processed electronic holography to NDE of rotors. Electronic holography was used to generate characteristic patterns or mode shapes of vibrating rotors and rotor components. Artificial neural networks were trained to identify damage-induced changes in the characteristic patterns. The development and optimization of a neural-net training method were the most significant contributions of this work, and the training method and its optimization are discussed in detail. A second positive result was the assembly and testing of a fiber-optic holocamera. A major disappointment was the inadequacy of the high-speed-holography hardware selected for this effort, but the use of scaled holograms to match the low effective resolution of an image intensifier was one interesting attempt to compensate. This report also discusses in some detail the physics and environmental requirements for rotor electronic holography. The major conclusions were that neural-net and electronic-holography inspections of stationary components in the laboratory and the field are quite practical and worthy of continuing development, but that electronic holography of moving rotors is still an expensive high-risk endeavor.

Decker, Arthur J.↗

NASA NTRS2003

Damage Detection Using Holography and Interferometry

This paper reviews classical approaches to damage detection using laser holography and interferometry. The paper then details the modern uses of electronic holography and neural-net-processed characteristic patterns to detect structural damage. The design of the neural networks and the preparation of the training sets are discussed. The use of a technique to optimize the training sets, called folding, is explained. Then a training procedure is detailed that uses the holography-measured vibration modes of the undamaged structures to impart damage-detection sensitivity to the neural networks. The inspections of an optical strain gauge mounting plate and an International Space Station cold plate are presented as examples.

Decker, Arthur J.↗

NASA NTRS2003

Sensitivity and Calibration of Non-Destructive Evaluation Method That Uses Neural-Net Processing of Characteristic Fringe Patterns

Decker, Arthur J.↗

NASA NTRS2002

Interferometer Control of Optical Tweezers

This paper discusses progress in using spatial light modulators and interferometry to control the beam profile of an optical tweezers. The approach being developed is to use a spatial light modulator (SLM) to control the phase profile of the tweezers beam and to use a combination of the SLM and interferometry to control the intensity profile. The objective is to perform fine and calculable control of the moments and forces on a tip or tool to be used to manipulate and interrogate nanostructures. The performance of the SLM in generating multiple and independently controllable tweezers beams is also reported. Concurrent supporting research projects are mentioned and include tweezers beam scattering and neural-net processing of the interference patterns for control of the tweezers beams.

Decker, Arthur J.↗

NASA NTRS2002

Interferometer-Controlled Optical Tweezers Constructed for Nanotechnology and Biotechnology

A new method to control microparticles was developed in-house at the NASA Glenn Research Center in support of the nanotechnology project under NASA's Aerospace Propulsion and Power Base Research Program. A prototype interferometer-controlled optical tweezers was constructed to manipulate scanning probe microscope (SPM) tips. A laser beam passed through a Mach-Zehnder interferometer, and a microscope objective then produced an optical trap from the coaxial beams. The trap levitated and generated the coarse motion of a 10-mm polystyrene sphere used to simulate a SPM tip. The interference between the beams provided fine control of the forces and moments on the sphere. The interferometer included a piezoelectric-scanned mirror to modulate the interference pattern. The 10-mm sphere was observed to oscillate about 1 mm as the mirror and fringe pattern oscillated. The prototype tweezers proved the feasibility of constructing a more sophisticated interferometer tweezers to hold and manipulate SPM tips. The SPM tips are intended to interrogate and manipulate nanostructures. A more powerful laser will be used to generate multiple traps to hold nanostructures and SPM tips. The vibrating mirror in the interferometer will be replaced with a spatial light modulator. The modulator will allow the optical phase distribution in one leg of the interferometer to be programmed independently at 640 by 480 points for detailed control of the forces and moments. The interference patterns will be monitored to measure the motion of the SPM tips. Neuralnetwork technology will provide fast analysis of the interference patterns for diagnostic purposes and for local or remote feedback control of the tips. This effort also requires theoretical and modeling support in the form of scattering calculations for twin coherent beams from nonspherical particles.

Decker, Arthur J.↗

NASA NTRS2001

Optimization of Training Sets for Neural-Net Processing of Characteristic Patterns from Vibrating Solids

Artificial neural networks have been used for a number of years to process holography-generated characteristic patterns of vibrating structures. This technology depends critically on the selection and the conditioning of the training sets. A scaling operation called folding is discussed for conditioning training sets optimally for training feed-forward neural networks to process characteristic fringe patterns. Folding allows feed-forward nets to be trained easily to detect damage-induced vibration-displacement-distribution changes as small as 10 nm. A specific application to aerospace of neural-net processing of characteristic patterns is presented to motivate the conditioning and optimization effort.

Decker, Arthur J.↗

NASA NTRS2001

Training Data Optimized and Conditioned to Learn Characteristic Patterns of Vibrating Blisks and Fan Blades

At the NASA Glenn Research Center, we have been training artificial neural networks to interpret the characteristic patterns (see the leftmost image) generated from electronic holograms of vibrating structures. These patterns not only visualize the vibration properties of structures, but small changes in the patterns can indicate structural changes, cracking, or damage. Neural networks detect these small changes well. Our objective has been to adapt the neural-network, electronic-holography combination for inspecting components in Glenn's Spin Rig.

Decker, Arthur J.↗

NASA NTRS1999

Inspection of Space Station Cold Plate Using Visual and Automated Holographic Techniques

Real-time holography has been used to confirm the presence of non-uniformity in the construction of an International Space Station cold plate. Ultrasonic C-scans have previously shown suspected areas of cooling fin disbonds. But both neural-net processed and visual holography did not evidence any progressive permanent changes resulting from 3000 pressurization and relaxation cycles of a Dash 8 cold plate. Neural-net and visual inspections were performed of characteristic patterns generated from electronic time-average holograms of the vibrating cold plate. Normal modes of vibration were excited at very low amplitudes for this purpose, The neural nets were trained to flag very small changes in the mode shapes as encoded in the characteristic patterns. Both the whole cold plate and a zoomed region were inspected. The inspections were conducted before, after, and during pressurization and relaxation cycles of the cold plate. A water-filled cold plate was pressurized to 120 psig (827 kPa) and relaxed for each cycle. Each cycle required 5 seconds. Both the artificial neural networks and the inspectors were unable to detect changes in the mode shapes of the relaxed cold plate. The cold plate was also inspected visually using real-time holography and double-exposure holography. Regions of non-uniformity correlating with the C-scans were apparent, but the interference patterns did not change after 3000 pressurization and relaxation cycles. These tests constituted the first practical application of a neural-net inspection technique developed originally with support from the Director's Discretionary Fund at the Glenn Research Center at Lewis Field.

Decker, Arthur J.↗

NASA NTRS1999

Neural-Net Processing of Characteristic Patterns From Electronic Holograms of Vibrating Blades

Finite-element-model-trained artificial neural networks can be used to process efficiently the characteristic patterns or mode shapes from electronic holograms of vibrating blades. The models used for routine design may not yet be sufficiently accurate for this application. This document discusses the creation of characteristic patterns; compares model generated and experimental characteristic patterns; and discusses the neural networks that transform the characteristic patterns into strain or damage information. The current potential to adapt electronic holography to spin rigs, wind tunnels and engines provides an incentive to have accurate finite element models lor training neural networks.

Decker, Arthur J.↗