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错误诊断和预测系统用于金属电线料增材制造

Meng Xie¹, Zhuoyong Shi², Xixi Yue¹

¹School of Electrical and Information Engineering, Xi'an Jiaotong University City College, Xi'an 710018, China.

Sensors (Basel, Switzerland)

|July 13, 2024

概括

本研究引入了金属添加剂制造的监测和预测系统,以提高生产效率和产品质量. 该系统使用LSTM神经网络进行故障诊断,增强制造过程.

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:

金属添加剂制造容易因环境和设备参数波动造成故障.
这些故障会对生产效率和产品质量产生负面影响.
有效的监测和预测对于缓解这些问题至关重要.

研究的目的:

为添加剂制造料工艺开发一个参数监测和预测系统.
为未来在金属电线增材制造中的故障诊断提供基础.
提高增材制造的可靠性和质量.

主要方法:

使用STM 32作为数据采集的核心 (温度,湿度,电流,电压).
在LabVIEW 2019平台上开发了一个上级计算机系统.
通过 MATLAB 2019 集成了一个长短期记忆 (LSTM) 神经网络模型,用于预测功能.

主要成果:

成功建立了一个能够捕获和传输关键制造参数的系统.
实施了一种LSTM模型,用于预测金属线供应过程中的潜在故障.
证明了系统能够为故障诊断提供基础的能力.

结论:

开发的系统为增材制造中的故障诊断提供了必要的支持.
实时监控和预测可以显著改善制造过程控制.
这项研究有助于提高金属增材制造的稳定性和质量.

关键词:

LSTM神经网络是一个神经网络.实验室查看实验室查看添加剂制造添加剂制造添加剂制造错误诊断错误诊断错误诊断是一个问题.

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