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A numerical control machining tool path step error prediction method based on BP neural network.

Zi-Yu Zhang1, Wei Liu2, Peng-Fei Li1

  • 1College of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou, 215000, China.

Scientific Reports
|September 28, 2023
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Summary
This summary is machine-generated.

This study introduces a novel BP neural network for calculating numerical control (NC) machining tool path step errors. The method significantly reduces computation time and achieves high accuracy, with over 99% of predictions within 1 μm error.

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Area of Science:

  • Manufacturing Engineering
  • Computational Science
  • Artificial Intelligence

Background:

  • Accurate step error calculation is crucial for high-quality numerical control (NC) machining tool paths.
  • Current iterative methods for step error calculation are time-consuming and accuracy-limited.
  • Neural networks offer potential for faster and more accurate calculations via parallel processing and continuous learning.

Purpose of the Study:

  • To develop and validate a novel BP neural network model for predicting step errors in NC machining tool paths.
  • To improve the efficiency and accuracy of step error calculation compared to traditional geometric methods.
  • To demonstrate the effectiveness of the proposed neural network in real-world machining scenarios.

Main Methods:

  • A BP neural network model was constructed using core parameters for step error calculation.
  • Z-score normalization was applied to standardize data and mitigate the impact of singular parameters.
  • Dropout technique and Stochastic Gradient Descent with Momentum (SGDM) optimizer were employed during neural network training to enhance stability and prevent overfitting.

Main Results:

  • The neural network model successfully predicted step errors for samples from three different surface models.
  • Prediction error decreased with increased sample training, indicating model convergence.
  • After training on 15% of samples, over 99% of predicted step errors had an absolute error less than 1 μm.
  • The computation time was reduced to one-third compared to the traditional geometric method.

Conclusions:

  • The proposed BP neural network model provides an effective and efficient solution for calculating NC machining tool path step errors.
  • The method achieves high accuracy and significantly reduces computation time, making it suitable for practical applications.
  • This approach demonstrates the potential of neural networks in optimizing complex manufacturing processes.