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August 1, 2024Journal of Physics Conference SeriesOpen Access

Optimization of milling parameters based on GA-BP neural network

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Authors

LMLiqin MiaoCLChaoneng LiaoDZDashun Zhang

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Overview

Experimental study demonstrates reduced cutting force and surface roughness in milling processes, indicating improved machining quality over empirical methods.

Key Points

  • Optimized milling parameters decrease cutting force and surface roughness compared to conventional empirical selection, enhancing overall machining quality.
  • Cutting force decreases by 3.6% and surface roughness drops by 10.0% under the multi-objective optimization model relative to traditional empirical benchmarks.
  • Multi-objective optimization model using the NSGA-II algorithm integrates a predictive GA-BP neural network, providing robust parameter control for industrial milling.

Cite This Study

Miao et al. (2024) studied this question.

synapsesocial.com/papers/68e5dfd6b6db6435875741cbhttps://doi.org/10.1088/1742-6596/2815/1/012052
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