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Title: Model-Based Ultrasonic SignalProcessing for the Nondestructive Evaluation of Additive Manufacturing Components

Abstract

Ultrasonic testing (UT) for nondestructive evaluation (NDE) is a critical entity necessary to resolve both the quality and precision questions of complex parts evolving from the innovative additive manufacturing (AM) process. This modality provides the essential quantitative information for acceptance and potential flaw detection of the part under investigation. A primary ingredient in UT besides the required precision robotic hardware for the acquisition of high quality measurement data is the underlying signal processing. It is here that much of the system performance capability resides. In this report, we discuss the basic steps in UT signal processing along with current and future capabilities that must be achieved in order to satisfy the critical demands created by the AM process. In its basic foundations, signal processing is essentially the “extraction of critical information (signals) from noisy, uncertain data.” Clearly, in a perfect world the best signal processing is none---just make a reliable, noise-free, uncertainty-free, measurement. Unfortunately, even the best of systems is still straddled within the confines and limitations of physical instrumentation. With this in mind, we discuss the development of signal processing techniques to extract the desired UT information from noisy measurement data. We start with a discussion of a simplemore » homogeneous representation of a “part” under investigation and its insonification by an ultrasonic excitation from a physics-based perspective. Once developed, we briefly discuss the various choices of excitation signals and their tradeoffs. Next we discuss a simulation approach employing simple models from the signal processing perspective and then move on to the development of the basic signal processing approach to ultrasonic signal processing all based on estimating the pulse arrival estimation. Starting with simple peak detection techniques, progressing to the processing workhorse so-called “matched-filter” and finally to the more sophisticated “model-based matched filter.” The underlying pre- and post-processing of noisy measurement data along with the application of these techniques are subsequently demonstrated on experimental ultrasonic data. Finally, we discuss some open problems in UT and suggest a much more sophisticated model-based approach expanding these revolutionary ideas and encompassing more realistic signal models that incorporate noise, jitter and other pertinent uncertainties into the processor.« less

Authors:
 [1];  [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1438615
Report Number(s):
LLNL-TR-748811
DOE Contract Number:  
AC52-07NA27344
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; 36 MATERIALS SCIENCE

Citation Formats

Candy, J. V., Fisher, K. E., and Fix, B.. Model-Based Ultrasonic SignalProcessing for the Nondestructive Evaluation of Additive Manufacturing Components. United States: N. p., 2018. Web. doi:10.2172/1438615.
Candy, J. V., Fisher, K. E., & Fix, B.. Model-Based Ultrasonic SignalProcessing for the Nondestructive Evaluation of Additive Manufacturing Components. United States. doi:10.2172/1438615.
Candy, J. V., Fisher, K. E., and Fix, B.. Tue . "Model-Based Ultrasonic SignalProcessing for the Nondestructive Evaluation of Additive Manufacturing Components". United States. doi:10.2172/1438615. https://www.osti.gov/servlets/purl/1438615.
@article{osti_1438615,
title = {Model-Based Ultrasonic SignalProcessing for the Nondestructive Evaluation of Additive Manufacturing Components},
author = {Candy, J. V. and Fisher, K. E. and Fix, B.},
abstractNote = {Ultrasonic testing (UT) for nondestructive evaluation (NDE) is a critical entity necessary to resolve both the quality and precision questions of complex parts evolving from the innovative additive manufacturing (AM) process. This modality provides the essential quantitative information for acceptance and potential flaw detection of the part under investigation. A primary ingredient in UT besides the required precision robotic hardware for the acquisition of high quality measurement data is the underlying signal processing. It is here that much of the system performance capability resides. In this report, we discuss the basic steps in UT signal processing along with current and future capabilities that must be achieved in order to satisfy the critical demands created by the AM process. In its basic foundations, signal processing is essentially the “extraction of critical information (signals) from noisy, uncertain data.” Clearly, in a perfect world the best signal processing is none---just make a reliable, noise-free, uncertainty-free, measurement. Unfortunately, even the best of systems is still straddled within the confines and limitations of physical instrumentation. With this in mind, we discuss the development of signal processing techniques to extract the desired UT information from noisy measurement data. We start with a discussion of a simple homogeneous representation of a “part” under investigation and its insonification by an ultrasonic excitation from a physics-based perspective. Once developed, we briefly discuss the various choices of excitation signals and their tradeoffs. Next we discuss a simulation approach employing simple models from the signal processing perspective and then move on to the development of the basic signal processing approach to ultrasonic signal processing all based on estimating the pulse arrival estimation. Starting with simple peak detection techniques, progressing to the processing workhorse so-called “matched-filter” and finally to the more sophisticated “model-based matched filter.” The underlying pre- and post-processing of noisy measurement data along with the application of these techniques are subsequently demonstrated on experimental ultrasonic data. Finally, we discuss some open problems in UT and suggest a much more sophisticated model-based approach expanding these revolutionary ideas and encompassing more realistic signal models that incorporate noise, jitter and other pertinent uncertainties into the processor.},
doi = {10.2172/1438615},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Mar 20 00:00:00 EDT 2018},
month = {Tue Mar 20 00:00:00 EDT 2018}
}

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