Feasibility of Using Artificial Neural Networks with Electrochemical Impedance Spectroscopy Data From Coated Steel.

Abstract

Electrochemical impedance spectroscopy (E.I.S.) techniques can provide information about the condition of protective coatings on steel marine structures. Currently, an expert is required to interpret the data produced from an E.I.S. measurement, classifying the coating as 'good' or 'poor' or identifying the data as 'bad.' This limits the use of E.I.S. techniques to experienced operators. If the E.I.S. technique is to be used for production by inexperienced operators, measurements must be classified automatically. This investigation uses artificial neural networks (ANN) to develop an automated E.I.S. data classifier. ANNs were trained with a large database of measurements oil known good or poor coatings, including some bad data. The ANNs were tested with E.I.S. data not included in the training set. A variety of measurement signal processing schemes and network structures was evaluated. ANNs were developed which can accurately determine if the coating is good or poor and whether measurement problems produced bad data. (MM)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1995
Accession Number
ADA304240

Entities

People

  • HP Hack
  • M. A. Matteson

Organizations

  • Naval Surface Warfare Center Carderock Division

Tags

DTIC Thesaurus Topics

  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Coatings
  • Databases
  • Dimensionality Reduction
  • Impedance
  • Machine Learning
  • Measurement
  • Neural Networks
  • Production
  • Protective Coatings
  • Signal Processing
  • Spectroscopy
  • Training

Readers

  • Instructional Design and Training Evaluation.
  • Materials Science and Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks