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<p>This paper proposes a multi-stage adaptive genetic algorithm for multi-objective optimization of machining parameters in complex structural part manufacturing. The model jointly considers machining accuracy, processing time, and energy consumption under parameter coupling and nonlinear constraints. An adaptive crossover-mutation strategy and a multi-stage evolutionary mechanism are introduced to improve convergence stability and balance exploration with exploitation. Experiments are conducted on a self-developed Python-based simulation platform using 12,400 samples covering 30 part structures, 12 machining processes, and 36 parameter variables. Compared with the standard GA and NSGA-II, the proposed method reduces the average machining cycle to 13.1&#xa0;h and the order delay rate to 3.8%, while maintaining a resource utilization rate of 87.4%, demonstrating stable performance in complex machining scenarios.</p>

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A multi-stage adaptive genetic algorithm for multi-objective optimization of machining parameters in complex part manufacturing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月5日

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  • Teng Li,
  • Qin Xing

摘要

This paper proposes a multi-stage adaptive genetic algorithm for multi-objective optimization of machining parameters in complex structural part manufacturing. The model jointly considers machining accuracy, processing time, and energy consumption under parameter coupling and nonlinear constraints. An adaptive crossover-mutation strategy and a multi-stage evolutionary mechanism are introduced to improve convergence stability and balance exploration with exploitation. Experiments are conducted on a self-developed Python-based simulation platform using 12,400 samples covering 30 part structures, 12 machining processes, and 36 parameter variables. Compared with the standard GA and NSGA-II, the proposed method reduces the average machining cycle to 13.1 h and the order delay rate to 3.8%, while maintaining a resource utilization rate of 87.4%, demonstrating stable performance in complex machining scenarios.