Engineering topics

Cios, Krzysztof J.

Publications and source records attributed to Cios, Krzysztof J..

11 records

NASA NTRS2000

PSIDD3: Post-Scan Ultrasonic Data Display System for the Windows-Based PC Including Fuzzy Logic Analysis

Post-Scan Interactive Data Display (PSIDD) III is a user-oriented Windows-based system that facilitates the display and comparison of ultrasonic contact data. The system is optimized to compare ultrasonic measurements made at different locations within a material or at different stages of material degradation. PSIDD III provides complete analysis of the primary wave forms in the time and frequency domains along with the calculation of several frequency dependent properties including Phase Velocity and Attenuation Coefficient and several frequency independent properties, like the Cross Correlation Velocity. The system allows image generation on all of the frequency dependent properties at any available frequency (limited by the bandwidth used in the scans) and on any of the frequency independent properties. From ultrasonic contact scans, areas of interest on an image can be studied with regard to underlying raw waveforms and derived ultrasonic properties by simply selecting the point on the image. The system offers various modes of in-depth comparison between scan points. Up to five scan points can be selected for comparative analysis at once. The system was developed with Borland Delphi software (Visual Pascal) and is based on a SQL database. It is ideal for classification of material properties, or location of microstructure variations in materials.

Lovelace, Jeffrey J.↗

NASA NTRS1999

Knowledge Discovery Process for Characterization of Materials Failure Mechanism

It is the intent of this project to provide a platform to visualize the various data collected from stress-strain testing of composite ceramic matrix materials. The data collected from the stress-strain tests are acoustic emissions (AE). As a material is subjected to a stress-strain test, various failure mechanisms occur in the material. The recorded sounds emitted during the test may correspond to various failure mechanisms. This project, thus, will give a possible way to visualize the data and data derived from the recorded AE. The stress-strain testing was performed on several composite matrix material combinations. Each of these tests produced anywhere from 1000 to 10,000+ AE events. For each AE event recorded, several characteristics in both the time and frequency domains are created. This project has two goals. First, this project will provide a summation page for a selected waveform. This page will include all of the characteristics determined from the AE event waveform along with graphs of the AE event waveform and its corresponding Power Spectrum. The other function of this project is to retrieve and display selected AE event waveforms for comparison.

Cios, Krzysztof J.↗

NASA NTRS1996

A Comparison of Neural Networks and Fuzzy Logic Methods for Process Modeling

The goal of this work was to analyze the potential of neural networks and fuzzy logic methods to develop approximate response surfaces as process modeling, that is for mapping of input into output. Structural response was chosen as an example. Each of the many methods surveyed are explained and the results are presented. Future research directions are also discussed.

Cios, Krzysztof J.↗

NASA NTRS1996

Algorithm For A Self-Growing Neural Network

CID3 algorithm simulates self-growing neural network. Constructs decision trees equivalent to hidden layers of neural network. Based on ID3 algorithm, which dynamically generates decision tree while minimizing entropy of information. CID3 algorithm generates feedforward neural network by use of either crisp or fuzzy measure of entropy.

Cios, Krzysztof J.↗

NASA NTRS1993

Radial basis function network learns ceramic processing and predicts related strength and density

Radial basis function (RBF) neural networks were trained using the data from 273 Si3N4 modulus of rupture (MOR) bars which were tested at room temperature and 135 MOR bars which were tested at 1370 C. Milling time, sintering time, and sintering gas pressure were the processing parameters used as the input features. Flexural strength and density were the outputs by which the RBF networks were assessed. The 'nodes-at-data-points' method was used to set the hidden layer centers and output layer training used the gradient descent method. The RBF network predicted strength with an average error of less than 12 percent and density with an average error of less than 2 percent. Further, the RBF network demonstrated a potential for optimizing and accelerating the development and processing of ceramic materials.

Cios, Krzysztof J.↗

NASA NTRS1993

Fuzzy sets predict flexural strength and density of silicon nitride ceramics

In this work, we utilize fuzzy sets theory to evaluate and make predictions of flexural strength and density of NASA 6Y silicon nitride ceramic. Processing variables of milling time, sintering time, and sintering nitrogen pressure are used as an input to the fuzzy system. Flexural strength and density are the output parameters of the system. Data from 273 Si3N4 modulus of rupture bars tested at room temperature and 135 bars tested at 1370 C are used in this study. Generalized mean operator and Hamming distance are utilized to build the fuzzy predictive model. The maximum test error for density does not exceed 3.3 percent, and for flexural strength 7.1 percent, as compared with the errors of 1.72 percent and 11.34 percent obtained by using neural networks, respectively. These results demonstrate that fuzzy sets theory can be incorporated into the process of designing materials, such as ceramics, especially for assessing more complex relationships between the processing variables and parameters, like strength, which are governed by randomness of manufacturing processes.

Cios, Krzysztof J.↗

NASA NTRS1993

Soft computing in design and manufacturing of advanced materials

The potential of fuzzy sets and neural networks, often referred to as soft computing, for aiding in all aspects of manufacturing of advanced materials like ceramics is addressed. In design and manufacturing of advanced materials, it is desirable to find which of the many processing variables contribute most to the desired properties of the material. There is also interest in real time quality control of parameters that govern material properties during processing stages. The concepts of fuzzy sets and neural networks are briefly introduced and it is shown how they can be used in the design and manufacturing processes. These two computational methods are alternatives to other methods such as the Taguchi method. The two methods are demonstrated by using data collected at NASA Lewis Research Center. Future research directions are also discussed.

Cios, Krzysztof J.↗

NASA NTRS1992

Self-growing neural network architecture using crisp and fuzzy entropy

The paper briefly describes the self-growing neural network algorithm, CID2, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results of a real-life recognition problem of distinguishing defects in a glass ribbon and of a benchmark problem of differentiating two spirals are shown and discussed.

Cios, Krzysztof J.↗

NASA NTRS1992

Self-growing neural network architecture using crisp and fuzzy entropy

The paper briefly describes the self-growing neural network algorithm, CID3, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results for a real-life recognition problem of distinguishing defects in a glass ribbon, and for a benchmark problen of telling two spirals apart are shown and discussed.

Cios, Krzysztof J.↗

NASA NTRS1992

Application of neural networks in the acousto-ultrasonic evaluation of metal-matrix composite specimens

Acousto-ultrasonics (AU) is a nondestructive evaluation (NDE) technique that was devised for the testing of various types of composite materials. A study has been done to determine how effectively the AU technique may be applied to metal-matrix composites (MMCs). The authors use the results and data obtained from that study and apply neural networks to them, particularly in the assessment of mechanical property variations of a specimen from AU measurements. It is assumed that there is no information concerning the important features of the AU signal which relate to the mechanical properties of the specimen. Minimally processed AU measurements are used while relying on the network's ability to extract the significant features of the signal.

Cios, Krzysztof J.↗

NASA NTRS1992

A fatigue damage estimator using RBF, backpropagation, and CID4 neural algorithms

Fatigue damage estimation using neural networks is described in the paper. Attention is focused on the method of data generation for both the training and test data used by radial basis function (RBF), backpropagation, and CID4 algorithms used in this study. The performance results of the three neural algorithms are analyzed in terms of their strengths and weaknesses in training.

Cios, Krzysztof J.↗