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Local corner smoothing based on deep learning for CNC machine tools.

Bai Jiang1, Rong Sun2, Ze-Long Li3

  • 1College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150006, China.

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Summary
This summary is machine-generated.

This study introduces a novel deep learning algorithm to smooth machining toolpaths by optimizing curvature. This enhances machining quality and allows for higher feedrates, improving efficiency.

Keywords:
Deep learningFeedrate planningIntelligent optimization algorithmLocal corner smoothing

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Area of Science:

  • Manufacturing Engineering
  • Computer-Aided Design (CAD)
  • Artificial Intelligence (AI)

Background:

  • Machining toolpaths often use linear segments, limiting speed and quality.
  • Optimizing toolpath curvature is crucial for smoother machining.
  • Existing intelligent optimization algorithms face challenges with computational resources.

Purpose of the Study:

  • To propose a new strategy for optimizing machining toolpaths at the curvature level.
  • To develop an efficient deep learning algorithm for toolpath smoothing.
  • To improve machining feedrates and quality by addressing local corner smoothness.

Main Methods:

  • Introduced three essential components for curvature-level toolpath optimization.
  • Developed the Double-ResNet Local Smoothing (DRLS) algorithm, incorporating First-Double-Local Smoothing (FDLS) and Second-Double-Local Smoothing (SDLS).
  • Integrated geometric, drive condition, and contour error constraints for feedrate planning.

Main Results:

  • The DRLS algorithm significantly improves optimization efficiency compared to traditional intelligent algorithms.
  • FDLS and SDLS effectively optimized NURBS control points and weights for smoother toolpaths.
  • Simulations verified the method's effectiveness in enabling higher feedrates and maintaining machining quality.

Conclusions:

  • The proposed DRLS algorithm offers an efficient solution for smoothing machining toolpaths.
  • Optimizing toolpath curvature at local corners leads to improved machining performance.
  • The method successfully balances higher feedrates with essential machining quality constraints.