Thermal Error Compensation Feasibility Study Using Artificial Intelligence

Abstract

One of the major unresolved sources of machine tool errors is thermal effects. Neural networks potentially offer a means of predicting errors in machine tool positioning due to thermal effects. These can then be used for compensation resulting in more accurate machining. This study demonstrates the feasibility of using artificial neural networks to predict thermally induced positioning errors on a turning center. The potential for application to other types of machine tools and implementation issues leading to commercialization are also discussed. Artificial neural networks, Machine tools, Thermal compensation.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1993
Accession Number
ADA266084

Entities

People

  • C. Gilmour
  • D. J. Canfield
  • J. R. Pfeiffer
  • M. J. Schmenk
  • M. Ruthemeyer
  • T. R. Sisson
  • W. J. Zdeblick

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Artificial Intelligence
  • Bench Tests
  • Computer Programs
  • Computers
  • Control Systems
  • Control Systems Engineering
  • Data Acquisition
  • Data Processing
  • Finite Element Analysis
  • Heat Transfer
  • Manufacturing
  • Neural Networks
  • Test And Evaluation
  • Test Beds
  • Test Methods

Readers

  • Manufacturing Engineering.
  • Neural Network Machine Learning.
  • Thermal Physics or Thermal Science.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks