Process Control Development for the Spray Forming Process

Abstract

Spray forming is an alternate alloy production technique to both conventional and powder metallurgy methods. In an effort to develop process control of this process, relationships must be established between process parameters and product quality parameters. Because mathematical modeling of the spray forming process has not yet been able to determine well-define relationships between process parameters and product quality, neural networks were employed to more clearly define this relationship. It was the goal of initial neural network development to prove the feasibility of neural network use in spray forming control. Because this initial work was successful, the focus of subsequent development was on determining and improving the accuracy of these neural predictions. Not only can neural networks successfully predict trends in quality data, but they are as accurate as an experienced operator in predicting quality outputs. Finally, process control development resulted in Windows compatible software program that puts neural network predictions within easy access for the spray forming plant operator

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1994
Accession Number
ADA280898

Entities

People

  • Rochelle D. Payne

Organizations

  • Naval Surface Warfare Center Carderock Division

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Basic Programming Language
  • Computers
  • Geometry
  • Manufacturing
  • Materials
  • Materials Processing
  • Measurement
  • Mechanical Properties
  • Metallurgy
  • Neural Networks
  • Powder Metallurgy
  • Simulators
  • Spray Forming
  • Surface Roughness
  • Surface Warfare
  • Three Dimensional
  • United States Naval Academy

Readers

  • Computational Modeling and Simulation
  • Surface Engineering/Surface Coating Technology.
  • Systems Analysis and Design

Technology Areas

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