用视觉+参数数据预测激光焊穿透深度与焊缝形态，精度超95%
A multi-task spatiotemporal deep neural network for predicting penetration depth and morphology in laser welding
- 融合焊池图像与焊接参数的时空深度学习模型
- 穿透状态识别准确率99.35%，深度误差仅1.79毫米
- 适合工业质检场景，提升激光焊接实时质量控制能力
在激光穿透焊中，评估熔深状态和焊缝形貌对判断焊缝质量至关重要。本文提出一种创新的多任务深度学习模型，可高精度预测熔深状态、熔深值及焊缝截面形态。监测平台通过互补金属氧化物半导体（CMOS）相机捕获激光焊接过程中的熔池图像。所提模型结合顶部熔池图像的时空特征与焊接参数，构建基于卷积神经网络与状态空间模型的深度学习框架，更高效地提取和处理时空信息。此外，提出一种可靠的建模数据集构建方法，提升了模型的鲁棒性与泛化能力。测试集验证结果表明，熔深状态预测准确率达99.35%，熔深预测误差为1.79毫米，焊缝截面重构准确率为95.65%。本研究为激光穿透焊系统的在线质量控制提供了新思路与方法。
原文摘要 · Abstract (English)
In laser penetration welding, the assessment of penetration state and weld seam morphology plays a crucial role in determining the weld quality. This paper presents a comprehensive introduction of the innovative muti-task deep learning model that has the capability to predict penetration state, depth, and weld seam morphology with high accuracy. The monitoring platform relies on weld pool images captured during the laser welding process using a complementary metal-oxide-semiconductor camera. The proposed model integrates spatiotemporal features extracted from top weld pool images along with welding parameters, establishing a deep learning framework based on convolutional neural networks and state space models for more efficient extraction and processing of spatial-temporal information. Furthermore, a reliable method for constructing the dataset is proposed to enhance both robustness and generalization capability of the developed model. Validation results on the test set demonstrate that prediction accuracy for penetration state can reach 99.35%, while prediction error for penetration depth is 1.79 millimeter, and accuracy of reconstructing the weld cross-section is 95.65%. This study provides new insights and methodologies for in-situ quality control strategies in laser penetration welding systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。