Initial Phase in the Development of an Automatic, Optical Scatter Inspection Station.

Abstract

The goal of this program is to develop an automated, optical inspection station useful on a spectrum of munition related components. Laser scatter is composed of speckle whose exact distribution cannot be predicted. However, its envelope can be used to typify the surface profile. Thus, the ability to detect surface features is a function of the ability to resolve this envelope which, in turn is a function of the number of speckle per unit solid angle in the scatter plane. Since the dimensions of speckle are inversely proportional to the illuminating laser beam diameter, there is an implicit lower bound to features detectable by this method. Also, there are lower limits imposed on defect detectability, for actual production items, from background noise arising from surface variations due to the machining process employed. Both speckle and surface variances must be statistically computed on a per component basis in order to establish the usefulness of scatter inspection for that particular component. In order to be applicable to a wide spectrum of situations, a generalized scatter sampling system was constructed. The output is fed into a logic board, providing a degree of pattern recognition capability and yielding a system adaptable to the more difficult discrimination problems. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1977
Accession Number
ADA053945

Entities

People

  • Edward G. Kessler

Organizations

  • United States Army Armament Research, Development and Engineering Center

Tags

Communities of Interest

  • Air Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Automatic
  • Background Noise
  • Defect Detection
  • Detection
  • Diameters
  • Discrimination
  • Fiber Optics
  • Geometry
  • Inspection
  • Laser Beams
  • Laser Spots
  • Materials
  • Munitions
  • Pattern Recognition
  • Photodetectors
  • Radiation
  • Scattering

Fields of Study

  • Physics

Readers

  • Optical Physics and Photonics.
  • Radar Systems Engineering.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Directed Energy