Implementation of Helicoid Machining with BP Neural Network


On account of the interferential problem of milling the helicoid, the helical groove's profile are different from the milling cutter's cutting edge that result in difficulty in tool edge design; and the chip flutes of cutting tools have crucial effect in cutting capability and the machining quality, It need improve design precision of milling cutter so that meet the machining precision demand of spiral flute, especially under the different cutting parameters circumstances. Using BP neural network's nonlinear mapped characteristic to simulate discrete coordinates of cutting edge so that obtain the purpose of high precision designing cutting tools' profile. Therefore, a nonlinear model, which is established between the reamer's spiral finite and the milling cutter's profile by BP neural network. According to spiral flute profile, BP neural network simulate the cutting edge of under different helicoid parameter with the Levenberg-Marquardt back-propagation algorithm, the simulating experimental result has proved that using neural network to design milling cutter's profile can satisfy the actual need and can simulate the tool edge under different machining parameter.

  • Abstract
  • Key Words
  • 1 Introduction
  • 2. To Solve the Spiral Flute Cutting Equation
  • 3 Modeling with Neural Network
  • 4. Simulating the Milling Cutter's Profile
  • 5. Simulating Machining Example
  • 6. Summaries
  • References

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