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A force-measuring-based approach for feed rate optimization considering the stochasticity of machining allowance

  • Zhongxi Zhang
  • Ming Luo
  • Dinghua Zhang
  • Baohai Wu
ORIGINAL ARTICLE
  • 75 Downloads

Abstract

Forging and casting are the commonly used manufacturing methods for the blank part of complex parts, such as the compressor blade and engine casing. Their geometric shapes are usually complex and difficult to measure or model. Besides, blank parts of different batches usually show slight difference in terms of shape and material consistency. Therefore, the axial cutting depth for the first sequence of rough machining is not fully decided. As a consequence, the rough milling process can only be planned with conservative feed rate, which leads to long machining time. Aiming at improving machining efficiency for roughing process, a feed rate optimization method by combining the monitored cutting force in the whole milling process with off-line optimization is developed in this paper. Firstly, the paper presents an analytical force model to calculate the instantaneous milling force and recognize the actual axial cutting depth, and a cutting force analysis method to recognize optimizable cutting segments of a trajectory. Secondly, a linear interpolation algorithm is applied to divide the optimizable cutting segments by interpolating a number of monitoring points. After that, the maximum feed rate of each monitoring point is calculated according to actual machining condition. The optimal feed rate is achieved after restraining with the constraints and smoothing with the least squares algorithm. To validate the effectiveness of the developed optimization method, a machining experiment with milling a designed blank part was conducted. Compared with the constant feed rate method, the developed method saved machining time by 19.83%.

Keywords

Milling Cutting force Feed rate Cutting depth 

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Notes

Funding information

This study was co-supported by the National Science and Technology Major Project (Grant No. 2017ZX04013001), National Natural Science Foundation of China (Grant No. 51675438), and the Fundamental Research Funds for the Central Universities (Grant No. 3102017gx06008).

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Zhongxi Zhang
    • 1
  • Ming Luo
    • 1
  • Dinghua Zhang
    • 1
  • Baohai Wu
    • 1
  1. 1.Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of EducationNorthwestern Polytechnical UniversityXi’anPeople’s Republic of China

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