AI 中文总结
研究弧焊过程监测问题，提出结合无监督深度潜在表示学习与贝叶斯滤波的通用框架，用DVAE学习潜在表示及演变，PF进行实时推理，验证表明该框架具通用性和稳健性，可跨应用监测弧焊过程。
AI 中文摘要
弧焊工艺对连续制造至关重要，但易受干扰影响焊接质量。由于复杂视觉模式和非线性时变动力学，实时监测既关键又困难。深度学习虽有前景，但因依赖大量标注数据集和特定应用调整而面临可扩展性限制。本文探索一种统一方法能否跨应用表征主要弧焊过程并通过一致状态监测提高可扩展性。介绍了一种稳健且通用的弧焊监测框架，它将从熔池图像中提取紧凑特征的无监督深度潜在表示学习与贝叶斯滤波相结合，以处理诸如电弧辐射和镜面反射等持续和波动的干扰。具体而言，动态变分自编码器（DVAE）由基于CNN的编码器-解码器和基于LSTM的过渡模型组成，联合学习潜在表示及其在控制输入下的演变。为了进行稳健的实时推理，专门的粒子滤波器（PF）传播潜在和LSTM隐藏状态，在抑制传感器噪声的同时保留过程历史。这种设计非常适合焊接的缓慢和惯性动力学。在GTAW和GMAW上进行的无需特定工艺调整的验证证明了该框架的通用性和稳健性。
英文摘要
Arc welding processes are essential for continuous fabrication but prone to disturbances that impair weld quality, making real-time monitoring critical yet difficult due to complex visual patterns and nonlinear, time-varying dynamics. Deep learning shows promise but faces scalability limits because of its dependence on large labeled datasets and application-specific tuning. We explore whether a unified approach can characterize major arc welding processes across applications and improve scalability through consistent state monitoring. This paper introduces a robust and generalizable monitoring framework for arc welding. It combines unsupervised deep latent representation learning, which extracts compact features from weld pool images, with Bayesian filtering to handle persistent and fluctuating disturbances such as arc radiation and specular reflections. Specifically, a Dynamic Variational Autoencoder (DVAE), consisting of a CNN-based encoder-decoder and an LSTM-based transition model, jointly learns latent representations and their evolution under control inputs. For robust real-time inference, a specialized Particle Filter (PF) propagates the latent and LSTM hidden states, preserving process history while suppressing sensor noise. This design is well suited to welding's slow and inertial dynamics. Validation on GTAW and GMAW without process-specific tuning demonstrates the framework's generalizability and robustness.
Journal refJournal of Manufacturing Processes 160 (2026) 15-27
DOI：10.1016/j.jmapro.2026.01.039
