用AI动态调节激光切割机排烟泵，节能20%至50%
Artificial intelligence approaches for energy-efficient laser cutting machines
- 通过深度学习实时识别材料与烟雾水平，实现排烟泵闭环控制
- 实验显示排烟泵能耗降低20%至50%，显著提升能效
- 适合关注智能制造与绿色制造的工程师和研究者
本研究针对激光切割中能耗高、环境影响大的问题，提出新型深度学习方法以降低能源消耗。针对现有CO2激光排烟泵缺乏自适应控制与开环运行的问题，引入闭环配置，根据被切材料类型和产生的烟雾水平动态调节泵的功率。为此，采用多种材料分类技术，包括基于无透镜散斑传感的定制卷积神经网络（CNN）以及利用预训练VGG16模型的USB摄像头迁移学习方法。同时，部署独立的深度学习模型用于烟雾水平检测，进一步优化泵的功率输出。该系统可在非工作时段自动关闭排烟泵，并在运行中动态调节功率，实验验证可实现排烟泵能耗下降20%至50%，对制造业可持续发展具有重要贡献。
原文摘要 · Abstract (English)
This research addresses the significant challenges of energy consumption and environmental impact in laser cutting by proposing novel deep learning (DL) methodologies to achieve energy reduction. Recognizing the current lack of adaptive control and the open-loop nature of CO2 laser suction pumps, this study utilizes closed-loop configurations that dynamically adjust pump power based on both the material being cut and the smoke level generated. To implement this adaptive system, diverse material classification methods are introduced, including techniques leveraging lens-less speckle sensing with a customized Convolutional Neural Network (CNN) and an approach using a USB camera with transfer learning via the pre-trained VGG16 CNN model. Furthermore, a separate DL model for smoke level detection is employed to simultaneously refine the pump's power output. This integration prompts the exhaust suction pump to automatically halt during inactive times and dynamically adjust power during operation, leading to experimentally proven and remarkable energy savings, with results showing a 20% to 50% reduction in the smoke suction pump's energy consumption, thereby contributing substantially to sustainable development in the manufacturing sector.
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