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一个可转移的数字双驱动过程设计框架,用于高性能多喷气抛光

Honglei Mo¹, Xie Chen¹, Lingxi Guo¹

¹Shanghai Aerospace Control Technology Institute, Shanghai 201109, China.

|February 27, 2026

概括

本研究引入了一个数字双胞胎 (DT) 驱动的框架,以提高多喷射抛光 (MJP) 的效率和质量. 转移学习模型增强了表面粗度预测,适应不断变化的条件,以获得更好的制造结果.

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
计算机科学计算机科学

背景情况:

多喷射抛光 (MJP) 为复杂的表面提供了高精度,但在效率,复杂性和稳定性方面面临挑战,特别是在不断变化的条件下.
在可变的工作条件下保持一致的抛光质量仍然是MJP应用中的一个重大障碍.

研究的目的:

提出一个以数字双胞胎 (DT) 为驱动,以人为中心的设计框架,以优化多喷气抛光 (MJP).
为了提高MJP在不同操作参数下的表面粗度预测准确性和稳定性.
在数据稀缺和不确定性下,为复杂的超精密制造提供预测建模解决方案.

主要方法:

开发一个数字双胞胎 (DT) 驱动的框架,集成关键的MJP因素:喷射动能,喷嘴结构,磨料类型和加工路径.
实现基于特征编码转移学习的模型,用于表面粗度预测.
在两个不同的MJP工作条件下使用3D打印工件进行实验验证.

主要成果:

与传统方法相比,拟议的模型显示出优异的预测性能.
该模型在表面粗性预测中表现出增强的强度和跨条件适应性.
实验结果证实了DT驱动框架和转移学习方法的有效性.

结论:

开发的预测建模组件支持MJP的DT驱动过程设计.
该框架提供了一种实用且可扩展的方法来优化超精密制造工艺.
这项工作解决了数据稀缺和不确定性占主导地位的制造业环境中的挑战.

关键词:

数字双胞胎数字双胞胎是什么意思流体喷射抛光抛光流体喷射抛光机械加工过程的加工过程.表面粗度预测的预测转移学习转移学习

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