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一种基于Resnet神经网络的协作机器人钻井的端到端倾斜状态监控方法

Lu Qian¹, Peifeng Liu², Hao Lu²

¹School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430062, China.

Sensors (Basel, Switzerland)

|February 24, 2024

概括

这项研究引入了一个有效的框架,用于使用振动信号监测机器人钻井质量. 该方法准确识别钻井倾斜状态,改善复杂环境中的表面质量和效率.

科学领域:

机器人技术机器人技术机器人技术
制造业工程制造工程
信号处理信号处理

背景情况:

协作机器人提供了灵活性,但受到了低刚度的影响,影响了钻井质量.
有效的在线监控对于高性能机器人钻井至关重要.

研究的目的:

开发一个端到端的框架,在线监测机器人钻井质量.
建立一种使用振动信号识别钻井倾斜状态的方法.

主要方法:

利用振动信号进行在线钻井质量监测.
使用Cannny边缘检测来量化钻井倾斜度.
开发了一个基于ResNet的模型来分类倾斜状态.

主要成果:

拟议的框架通过振动分析有效监测钻探质量.
该ResNet模型准确地对机器人钻井倾斜状态进行了分类.
该方法在各种工件材料中显示出高精度.

结论:

开发的框架为监测机器人钻井质量提供了有效和适用的解决方案.
这种方法在复杂的制造环境中提高了表面质量和钻井效率.

关键词:

深度残留网络深度残留网络倾斜状态检测倾斜状态检测机械加工信号处理过程监控过程监控机器人钻探机器人钻探

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