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开发基于机器学习的优化整体预测模型,用于电线电流放电加工过程
Baneswar Sarker1, Shankar Chakraborty2, Robert Čep3
1Department of Industrial and Systems Engineering, Indian Institute of Technology, Kharagpur, India.
优化的组合模型增强了线电放电加工 (WEDM) 过程预测. 这些模型结合了多个基础算法,在复杂的制造应用中显示出比单个方法更好的准确性.
科学领域:
- 制造业 工程 制造工程
- 计算智能是一种计算智能.
- 材料科学 材料科学 材料科学
背景情况:
- 在难以加工的材料上制造复杂的配置文件时,电线电流加工 (WEDM) 是至关重要的.
- 准确预测WEDM过程反应对于优化生产和材料质量至关重要.
研究的目的:
- 为预测WEDM过程响应开发优化异质集团模型.
- 通过结合多个机器学习算法来提高预测准确性.
主要方法:
- 通过整合来自随机森林,支持矢量机器和回归的预测来开发集合模型.
- 制定了优化问题,以尽量减少预测错误 (RMSE,MAE) 进行加权合奏创建.
- 通过使用九个统计指标和多响应信号对噪声 (MRSN) 比率来评估模型性能.
主要成果:
- 优化集体模型与单个基准模型相比,显示出更高的预测准确性.
- 多响应信号对噪声 (MRSN) 比率证实了开发的合集的优越性能.
- 这项研究使用了来自WEDM过程的两个实验数据集.
结论:
- 优化异质组合模型为WEDM过程提供了卓越的预测准确性.
- 集成建模提供了一种强大的方法来提高制造过程预测的可靠性.
- 提出的方法是有效的优化复杂的加工操作.
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