使用级联人工智能对生产流程进行网络物理优化:机器人引导的 MAG 焊接应用案例

Peter Burggräf , Fabian Steinberg , Philipp Nettesheim , Gerald Kolter
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引用次数: 0

摘要

在去年的文章中,我们提出了利用两个人工智能对机器人引导的金属气弧焊接过程进行网络物理优化。这两个人工智能以级联方式控制焊接参数。这意味着第一个人工智能对参数进行粗略调整,而微调则由第二个人工智能完成。在本文中,我们介绍了使用这种设置的结果。预测的焊接参数接近实验中发现的理想参数。在训练数据相对较少的情况下,这些结果已经显现出来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cyber-Physical Optimization of Production Processes Using Cascade AIs: A Robot-Guided MAG Welding Use-Case
In last year's article, we proposed a cyber-physical optimization of a robot-guided gas metal arc welding process using two artificial intelligences. These are set up in a cascade to control the welding parameter. This means the first AI performs a rough adjustment of the parameter, while the fine-tuning is done by the second AI. In this paper, we present the results when using this setup. The predicted welding parameter are close to the ideal parameter found experimentally. These findings are already seen with relatively low training data.
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