在使用图形神经网络的渐进性腔中,对于使用图形神经网络的尺寸过程的干扰建议.
Leandro Starke1, Aurélio Faustino Hoppe1, Andreza Sartori1,2
1Department of Information Systems and Computing, Regional University of Blumenau, Rua Antônio da Veiga 140, 89030-903, Blumenau, SC, Brazil.
Scientific reports
|October 6, 2023
概括
本研究介绍了一个图形神经网络原型,以优化渐进性腔大小. 人工智能模型准确地推了转子维度,减少了手工劳动和验证工程设计.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
背景情况:
- 渐进式空腔的尺寸是手动的,耗时的,需要广泛的专业知识.
- 目前的方法涉及历史数据分析和维度监控,导致大量的劳动力成本.
研究的目的:
- 使用图形神经网络 (GNN) 开发一个原型,以自动化和改进尺寸过程.
- 建议在渐进式空腔中为叶轮和定子匹配提供最佳干扰.
主要方法:
- 利用历史应用程序数据和控制电子表格来构建一个全面的数据库.
- 应用复杂的网络技术和中间中心性来确定旋转器的维度和订单确认的重要性.
- 开发了一个基于GNN的干扰建议原型.
主要成果:
- 在订单确认建议中达到0.28的平均平方误差.
- 人工智能产生的维度与经验丰富的工程师定义的维度之间具有很高的相似性.
- 原型成功验证了新的设计定义.
结论:
- 图形神经网络提供了一种可行的解决方案,用于自动化渐进式空腔大小.
- 开发的原型可以显著减少手工劳动和提高设计的准确性.
- 这些发现支持在验证和完善工程设计过程中使用人工智能.
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