Intelligent Automated Process Planning and Code Generation for Computer-Controlled Inspection

Abstract

Requirements for greater precision and reduced rejection rates demand improved inspection methods that can be provided by implementing increased automation into the process. This thesis discusses the implementation of an automated intelligent inspection planner and its integration into a feature- based concurrent engineering system. The approach utilizes features as the common language of the individual modules that promote ideas of geometry, functionality, and design intent throughout the system by feature translation among the modules. An artificial neural network optimizes the sequence of inspection points based on inspection rule criteria A collision avoidance algorithm ensures the safety of automated inspection in a computationally efficient manner. The goal of the inspection planner is to output instruction code that will be executed on a computer-controlled coordinate measurement machine (CMM) to properly, efficiently, and safely measure and evaluate the tolerances of the manufactured product. Automated inspection neural networks, Computer aided process, Planning.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1994
Accession Number
ADA275346

Entities

People

  • Steven M. Ruegsegger

Organizations

  • Case Western Reserve University

Tags

Communities of Interest

  • Air Platforms
  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Automation
  • Cognitive Science
  • Collision Avoidance
  • Computational Science
  • Computer Languages
  • Computer Programming
  • Computers
  • Control Systems
  • Motion Planning
  • Neural Networks
  • Operations Research
  • Systems Engineering
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Computer science
  • Engineering

Readers

  • Hydraulic Engineering.
  • Parallel and Distributed Computing.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks