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VSG-FC:一种联合的虚拟样本生成和特征构造模型,用于有效预测抛光过程中的表面粗度

Dapeng Yang¹, Shenggao Ding¹, Lifang Pan²

¹School of Computer Engineering, Jimei University, Xiamen 361021, China.

|June 27, 2025

概括

本研究介绍了VSG-FC,这是一种用于预测表面粗度的新型模型,通过虚拟样本生成和特征构建来克服数据稀缺性. 该方法通过提高预测准确性和识别关键加工因素来提高生产效率.

科学领域:

制造业工程制造工程
材料科学材料科学材料科学
人工智能的人工智能

背景情况:

表面粗度对于工件的质量和性能至关重要.
准确的预测提高了生产效率.
用于预测表面粗度的数据驱动模型通常需要广泛的训练数据,由于成本和时间限制,在抛光过程中通常很难获得这些训练数据.

研究的目的:

提出一种新的数据驱动模型,VSG-FC,用于预测表面粗度.
为了应对抛光过程中数据稀缺的挑战.
通过优化功能空间,提高预测准确度和识别关键加工因素.

主要方法:

该研究整合了基因算法驱动的虚拟样本生成 (GA-VSG) 进行样本增强.
它还结合了基因编程驱动的特征构造 (GP-FC) 来进行特征重建.
结合VSG-FC方法优化了功能空间,以提高模型性能.

主要成果:

VSG-FC方法在生成高质量的虚拟样本方面表现出显著的优势.
该模型实现了对表面粗度的增强预测精度.
这种方法被证明是可以解释的,成功地确定了影响表面粗度的关键加工因素.

结论:

拟议的VSG-FC模型有效地克服了在表面粗度预测中的数据稀缺性.
集成GA-VSG和GP-FC优化了功能空间,以提高模型性能.
该方法提供了一个可行的,可解释的解决方案,用于提高生产效率和抛光过程中的产品质量.

关键词:

可以解释性的解释性.建筑特征建筑特征建筑特征抛光抛光抛光抛光表面粗度预测的预测虚拟样本的生成是虚拟样本的生成.

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