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Tool Cutting Force Prediction Model Based on ALO-ELM Algorithm.

Hongna Zhang1

  • 1College of Engineering, Inner Mongolia University for Nationalities, Tongliao 028000, Inner Mongolia, China.

Computational Intelligence and Neuroscience
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Summary

This study introduces an Ant Lion Optimizer (ALO) enhanced Extreme Learning Machine (ELM) for tool cutting force prediction. The ALO-ELM model significantly improves prediction accuracy and convergence speed compared to traditional methods.

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Area of Science:

  • Manufacturing Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional data-driven models for tool cutting force prediction suffer from low efficiency, slow convergence, and poor accuracy.
  • Accurate prediction of cutting forces is crucial for optimizing machining processes and tool life.

Purpose of the Study:

  • To propose a novel prediction method for tool cutting force using an optimized Extreme Learning Machine (ELM).
  • To enhance the prediction accuracy and convergence speed of ELM by integrating the Ant Lion Optimizer (ALO).

Main Methods:

  • The Ant Lion Optimizer (ALO) algorithm was employed to optimize the weights of the input and hidden layers of the Extreme Learning Machine (ELM).
  • Tool cutting force prediction models were developed using the proposed ALO-ELM, standard ELM, Backpropagation (BP) neural network, and Support Vector Machine (SVM).

Main Results:

  • The ALO-ELM model demonstrated superior performance with a mean square error of 0.9911%, mean absolute percentage error of 0.0011%, and mean absolute error of 1.0863%.
  • These error metrics were significantly lower than those achieved by ELM, BP, and SVM models.
  • The ALO-ELM model exhibited enhanced prediction accuracy and generalization capabilities.

Conclusions:

  • The proposed Ant Lion Optimizer-Extreme Learning Machine (ALO-ELM) model effectively predicts tool cutting forces with high accuracy and improved efficiency.
  • The ALO-ELM approach offers a robust and reliable solution for real-time cutting force prediction in manufacturing applications.