对于数控加工的振动监测质量控制的新型框架
Georgia Apostolou1, Myrsini Ntemi1, Spyridon Paraschos1
1Information Technologies Institute (ΙΤΙ), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
本研究介绍了一种人工智能框架,该框架将振动监测与质量预测相结合,以减少加工中的缺陷和成本. 这种新的方法提高了各种CNC机器的效率和适应性.
科学领域:
- 制造业 工程 制造工程
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 在加工过程中,螺丝轴的振动显著降低了工件表面质量,导致了昂贵的手工加工和高废弃率.
- 传统的解决方案往往将抑制振动和质量控制视为单独的过程,从而限制了整体效率.
- 现有的数据驱动方法在各种CNC机器配置中难以适应.
研究的目的:
- 开发一个新的框架,将先进的振动监测与人工智能驱动的加工过程质量预测相结合.
- 通过同时解决振动和质量控制,提高工件质量,生产率和操作效率.
- 创建适用于各种CNC机器的状态监测和质量控制的通用方法.
主要方法:
- 实施先进的振动监控技术,以捕获实时机器动态.
- 基于振动数据的人工智能驱动模型的开发,用于预测工件质量指标.
- 将监控和预测集成到一个主动过程控制的统一框架中.
主要成果:
- 显著减少了废弃部件,重新加工时间和手工完成成本.
- 在整体工艺质量,生产力和效率方面取得了明显的改善.
- 在不同CNC机器设置中成功应用通用方法.
结论:
- 集成的AI框架有效地减轻了轴心振动对加工质量的负面影响.
- 与传统的单独方法相比,这种方法提供了一个更全面和更适应的解决方案.
- 这种通用方法提高了先进的监控和控制在各种制造环境中的适用性.
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