工具磨损状态识别基于SVM优化通过改进的北方Goshawk优化优化.
Jiaqi Wang1, Zhong Xiang1, Xiao Cheng1,2
1School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究介绍了一种改进的北方Goshawk优化支持矢量机 (INGO-SVM) 模型,用于准确的工具磨损状态识别. 在磨削磨损实验中,INGO-SVM模型实现了97.9%的准确性,提高了加工精度并减少了停机时间.
科学领域:
- 制造业 工程 制造工程
- 机械工程 机械工程
- 信号处理 信号处理
背景情况:
- 工具磨损显著影响设备停机时间和加工精度.
- 准确的工具磨损状态识别对于优化制造流程至关重要.
- 现有的方法可能缺乏所需的准确性和效率.
研究的目的:
- 开发一种新且高度准确的工具磨损状态识别技术.
- 提高工具磨损监测系统的效率和稳定性.
- 提高加工精度,降低运营成本.
主要方法:
- 波段数据包值,用于多源信号处理和特征提取.
- 支持向量机器递归特征消除 (SVM-RFE) 用于选择相关特征.
- 一个改进的北方Goshawk优化 (INGO) 算法来优化支持向量机 (SVM) 参数,形成INGO-SVM模型.
主要成果:
- 在模拟测试中,INGO算法表现出卓越的收效率和稳定性.
- 拟议的INGO-SVM模型在磨磨损试验中实现了97.9%的显著识别准确率.
- 这种方法在识别准确性方面表现优于其他五种比较方法.
结论:
- INGO-SVM模型为工具磨损状态识别提供了一个高度准确和可靠的解决方案.
- 这种技术可以大大减少设备停机时间,提高加工精度.
- 该研究验证了将先进的信号处理与优化机器学习相结合的有效性,用于工业监控.
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