Resistance spot welding is the dominant joining process for the body-in-white in the automotive industry, where the weld nugget diameter is the key quality metric. Its measurement requires destructive testing, limiting the potential for efficient quality control. Physics-informed neural networks were investigated as a promising tool to reconstruct internal process states from experimental data, enabling model-based and non-invasive quality assessment in aluminum spot welding. A major challenge is the integration of real-world data into the network due to competing optimization objectives. To address this, we introduce two novel training strategies. First, experimental losses for dynamic displacement and nugget diameter are progressively included using a fading-in function to prevent excessive optimization conflicts. We also implement a custom learning rate scheduler and early stopping based on a rolling window to counteract premature reduction due to increased loss magnitudes. Second, we introduce a conditional update of temperature-dependent material parameters via a look-up table, activated only after a loss threshold is reached to ensure physically meaningful temperatures. An axially symmetric two-dimensional model was selected to represent the welding process accurately while maintaining computational efficiency. To reduce computational burden, the training strategies and model components were first systematically evaluated in one dimension, enabling controlled analysis of loss design and contact models. The two-dimensional network predicts dynamic displacement and nugget growth within the experimental confidence interval, supports transferring welding stages from steel to aluminum, and demonstrates strong potential for fast, model-based quality control in industrial applications.

翻译：电阻点焊是汽车工业中白车身的主要连接工艺，其中焊核直径是关键质量指标。其测量需要破坏性测试，限制了高效质量控制的潜力。物理信息神经网络作为一种有前景的工具被研究，用于从实验数据重建内部过程状态，从而实现铝点焊中基于模型和非侵入式的质量评估。主要挑战在于将真实世界数据整合到网络中，因为存在相互竞争的优化目标。为此，我们引入了两种新颖的训练策略。首先，使用渐入函数逐步纳入动态位移和焊核直径的实验损失，以防止过度优化冲突。我们还实现了自定义学习率调度器以及基于滚动窗口的早停机制，以应对因损失幅度增加而导致的过早收敛。其次，我们引入了通过查找表对温度相关材料参数进行条件更新的方法，仅在达到损失阈值后激活，以确保物理上合理的温度预测。选择轴对称二维模型来准确表征焊接过程，同时保持计算效率。为降低计算负担，训练策略和模型组件首先在一维系统中进行系统评估，从而实现对损失设计和接触模型的可控分析。该二维网络能够在实验置信区间内预测动态位移和焊核生长，支持将焊接阶段从钢材转移到铝材，并展现出在工业应用中实现快速、基于模型的质量控制的强大潜力。