United States of America As Represented by the Secretary of the Air Force

Southwest Research Institute

General Electric Company

USA As Represented by Secretary of the Navy

Steven R LeClair

John Francis Maguire

Yoh-Han Pao

John David Busbee

Allen G Jackson

Ramgopal Darolia

David P Lubbers

Robert Edward Schafrik

Matthew Bernard Buczek

Yang Cao

Jeffrey S Zabinski

Andrey A Voevodin

Ronald A Servais

Samuel J Laube

Frances L Abrams

Timothy E Westhoven

C William Lee

Benny L Carreon

John B Park

Stephen C Gregory

Patrick H Garrett

Chun-Lung Philip Chen

Doug Moore

T Walter Haas

Oliver D Patterson

Jeffrey J Heyob

Kenneth Currie

Stephen J Adams

Hilmi N Al-Kamhawi

David Charles Liptak

Ron Cass

Mark D Benedict

Kam Komeyli

Victor Hunt

Adel C Chemaly

William C Fitzgerald

Rand Robert Biggers

C L Chen

A non-destructive method of detecting subsurface defects in thermal barrier coatings applied to gas turbine engine components is provided. In an exemplary embodiment, the method includes positioning a evanescent microwave microscope probe adjacent a turbine component surface coated with a thermal barrier coating, and scanning the thermal barrier coating by moving at least one of the evanescent microwave microscope probe and the component surface in relation to one another in an x-y plane while maintaining a predetermined distance between the probe and the thermal barrier coating constant.

Non-destructive evaluation of thermal barrier coatings in gas turbine engines

A cellular automata neural network method for process modeling of film-substrate interactions utilizes a cellular automaton system having variable rules for each cell. The variable rules describe a state change algorithm for atoms or other objects near a substrate. The state change algorithm is used to create a training set of solutions for training a neural network. The cellular automaton system is run to model the film-substrate interactions with the neural network providing the state change solutions in place of the more computationally complex state change algorithm to achieve real-time or near real-time simulations.

Cellular automata neural network method for process modeling of film-substrate interactions and other dynamic processes

An orthogonal functional basis method for function approximation is disclosed. Starting with the orthogonal least squares method, a new subset selection method for selecting a set of appropriate basis functions is explained where, instead of picking a subset from a given functional basis, the subset is selected from a combination of functional basis evolved from a set of heterogeneous basis functions. The method results in a more efficient neural network.

Orthogonal functional basis method for function approximation

A Raman-based spatial analysis method of detecting surface flaws. Special filters and optics are used to acquire filtered Raman response data from a portion of the surface. The filtered Raman response data represents the Raman response of the surface at a selected frequency. A camera records the response, thereby providing a two dimensional image of the portion of the surface. The image may be analyzed to determine whether that portion has desired thickness and chemical characteristics.

Surface flaw detection using spatial raman-based imaging

A method of controlling a pulsed laser deposition process. A spectroscope is used to acquire Raman response data from a substrate as it is being coated with a film. A processor compares the response data to reference data, which may represent an uncoated substrate or an ideal film. In the former case, the attenuation of the response signal is used to indicate film thickness. In the former case, the peak characteristics of the film can be used to indicate its chemical properties. The processor may be further programmed to compare deposition rates, reaction rates, and transport rates to model data. The processor makes decisions regarding equipment control based on these comparisons. It may access heuristic rules to aid in decision making where several factors are involved.

Process control for pulsed laser deposition using raman spectroscopy

Process control for generating graded multilayer films repetitively and consistently using both pulsed laser sputtering and magnetron sputtering deposition techniques. The invention includes an apparatus which allows for set up of an ultrahigh vacuum in a vacuum chamber automatically, and then execution of a computer algorithm or 'recipe' to generate desired films. Software operates and controls the apparatus and executes commands which control digital and analog signals which control instruments.

Gradient multilayer film generation process control

A method for producing a pattern for making a cast part is described which comprises the steps of defining the structure of the part in terms of computer aided design system data, selecting a parting surface for the part to be cast; defining core requirements for the part by sweeping each positive feature of the part to the parting surface, subtracting the part from the projection, adding any remaining volume to the core, sweeping negative features away from the parting surface to the top or bottom of the mold and subtracting the negative features from the projection and intersecting the remainder of the part and adding any remaining volume to the core; repetitively generating alternative parting surfaces for the part and defining the corresponding core requirements whereby an optimum parting surface is defined for which the quantity and complexity of the corresponding core requirements are minimized, constructing core prints for each core requirement; constructing a pattern by adding the core prints to the part; and defining draft for the pattern surfaces perpendicular to the optimum parting surface.

Optimized recursive foundry tooling fabrication method

In the design of the process to machine discrete mechanical parts, the sequence of removing material is arrived at to ensure that the resulting part is of the correct geometry and finish, and the process is safe, feasible and accomplished in minimum time. For complex parts, an experienced machinist makes use of inductive methods to relate similar part material, geometries together with interdependencies and their associated machining sequences which have produced quality parts in the past with minimum time expended, or deductive methods to generate a sequence by relating feature attributes (relative size and position) and relations (intersections and common tooling). Of interest is the interaction between the two methods because their coupling enables a self-improving design system to be realized. A feature-based solid modelling software environment provides the elements of a symbolic language for describing a discrete mechanical part in terms of its product and process design. The language is based on the use of features which the designer and/or machinist uses to associate and reason about the part and interaction between the machine, tool, fixture and starting raw stock for the various process design options involving the milling and drilling sequence for removing material. The machinist can either recall from experience, past designs contained in an associative memory, and/or activate generalized rules or constraints in a deductive memory to provide a feasible `good` design. As additional non-geometric features (tolerance, surface finish and material properties) are provided both feature and operation sequencing are refined.

Inductive-deductive process design for machined parts

A multi-featured control system which improves the manufacturing capability of the thin-film semiconductor growth process. This system improves repeatability and accuracy of the process, reduces the manpower requirements to operate MBE, and improves the MBE environment for scientific investigation. This system has three levels of feedback control. The first level improves the precision and tracking of the process variables, flux, and substrate temperature. The second level comprises an expert system that uses sensors to monitor the status of the product in order to tailor the process plan in real time so that the exact qualities desired are achieved. The third level features a continuously evolving neural network model of the process which is used to recommend the recipe and command inputs to achieve a desired goal. The third level is particularly useful during the development process for new materials. All three levels require models of the process which are updated during automatic process identification experiments.

Hierarchical control system for molecular beam epitaxy

An automated, close-loop system for controlling the autoclave cure of fiber reinforced composites uses multiple expert systems hosted on different microprocessors running concurrently to control the cure process. This qualitative process controller uses sensory feedback to determine matrix material state-of-cure and then makes control decisions and executes control commands to the autoclave to attain desired material properties. The sensory information is interpreted and translated into a qualitative cure-state description by a system known as a parser hosted on a 68000 processor. The cure state description is then analyzed by the expert system known as the thinke hosted on another 68000 processor. The control decisions are determined using symbolic logic programmed as a rule-based paradigm generated from a human expert on the cure process. Once made, these control decisions are then posted to a blackboard hosted on an 80286 processor for execution by the autoclave feedback controller.

Qualitative process automation for autoclave cure of composite parts

Growing community of inventors

Spring Valley, OH, United States of America

Steven R LeClair