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Related Experiment Videos

Predicting thermal displacements in modular tool systems.

Niels Wessel1, Jan Konvicka, Frank Weidermann

  • 1Institute of Physics, University of Potsdam, Am Neuen Palais 10, Potsdam 14415, Germany. niels@agnld.uni-potsdam.de

Chaos (Woodbury, N.Y.)
|March 9, 2004
PubMed
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This summary is machine-generated.

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This study introduces a nonlinear regression approach using the Alternating Conditional Expectation (ACE) algorithm to predict thermal displacements in modular tool systems. This method significantly improves accuracy compared to linear regression for manufacturing applications.

Area of Science:

  • Manufacturing Engineering
  • Mechanical Engineering
  • Data Science

Background:

  • Modular tool systems are crucial for manufacturing accuracy but suffer from thermally induced errors.
  • The thermal behavior of these systems is complex, exhibiting nonlinearities, delay, and hysteresis, making theoretical description difficult.
  • Traditional linear regression models are insufficient for accurately predicting thermal displacements.

Purpose of the Study:

  • To investigate the effectiveness of nonlinear temperature-displacement regression functions for predicting thermal displacements in modular tool systems.
  • To test the hypothesis that nonlinear regression can reliably compensate for thermally induced errors.
  • To compare the accuracy of nonlinear regression with traditional linear methods.

Main Methods:

Related Experiment Videos

  • Utilized the Alternating Conditional Expectation (ACE) algorithm to estimate nonlinear temperature-displacement regression functions.
  • Trained and tested the models on data from a finite element spindle model.
  • Validated the approach using experimental data from a thermally forced silent experimental setup and a working milling machine.

Main Results:

  • The ACE algorithm effectively described the relationship between temperature and displacement in simulated data.
  • Nonlinear regression achieved 10-fold lower errors compared to multiple linear regression in experimental setups.
  • The approach estimated thermally induced errors with 1-2 micrometer accuracy in a working milling machine.

Conclusions:

  • Nonlinear regression using the ACE algorithm is a powerful tool for predicting thermal displacements in modular tool systems.
  • This approach offers a significant improvement in accuracy for compensating thermally induced errors.
  • The findings are highly relevant for the development of next-generation modular tool systems with enhanced manufacturing precision.