工具磨损预测基于XGBoost功能选择与PSO-BP网络组合的预测
Zhangwen Lin1, Yankun Fan2, Jinling Tan3
1College of Mechanical Engineering, Anhui Institute of Information Technology, Wuhu, 241199, Anhui, China. 1339777864@qq.com.
Scientific reports
|January 24, 2025
概括
本研究介绍了使用XGBoost和PSO-BP网络进行CNC加工的先进工具磨损预测方法. 这种方法显著提高了预测准确性,并减少了模型构建时间,即使数据有限.
科学领域:
- 制造业 工程 制造工程
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 准确的工具磨损预测对于优化CNC加工流程和降低运营成本至关重要.
- 现有的方法经常在小样本大小和选择预测模型的最佳输入特征和参数方面扎.
研究的目的:
- 为数控加工开发一种强大的工具磨损预测方法,在数据有限的情况下特别有效.
- 通过整合先进的特征选择和参数优化技术,提高工具磨损状态识别的准确性和效率.
主要方法:
- 使用XGBoost进行特征选择和粒子群集优化 (PSO) 来优化双层编程模型中的反向传播神经网络 (BPNN).
- 预处理的CNC加工信号 (振动,切削力) 使用时间域细分,Hampel过和波形消噪.
- 提取时间域,频域和时间频域特征,然后使用皮尔森相关性和XGBoost特征重要性进行选.
主要成果:
- 拟议的XGBoost特征选择将模型构建时间减少57.4%,预测准确度增加63.57%.
- 与其他工具磨损预测算法相比,PSO在优化BPNN参数方面表现出卓越的性能.
- 该方法在预测工具磨损状态方面取得了很高的准确性,超过了决策树,随机森林,Adaboost和额外树等传统方法.
结论:
- 结合的XGBoost和PSO-BP网络为CNC加工中工具磨损预测提供了有效的解决方案,特别是在小型样本场景中.
- 这种方法有助于提高生产效率,减少工具更换频率,并在工业环境中降低维护成本.
- 这些发现为增强自动化制造环境中的预测性维护策略提供了宝贵的见解.
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