标题： 基于铝点焊真实实验数据的物理信息神经网络改进训练策略 显示英文标题

标题： Improved Training Strategies for Physics-Informed Neural Networks using Real Experimental Data in Aluminum Spot Welding

摘要： 电阻点焊是汽车工业中车身结构的主要连接工艺，其中焊核直径是关键的质量指标。 其测量需要破坏性测试，限制了高效质量控制的潜力。 研究了物理信息神经网络作为一种有前途的工具，可以从实验数据中重建内部过程状态，从而在铝合金点焊中实现基于模型的非侵入式质量评估。 一个主要挑战是由于竞争优化目标，将现实世界数据集成到网络中。 为了解决这个问题，我们引入了两种新颖的训练策略。 首先，使用渐进引入函数逐步包含动态位移和焊核直径的实验损失，以防止过度的优化冲突。 我们还实现了一个自定义的学习率调度器和基于滚动窗口的早停机制，以对抗因损失幅度增加而导致的过早减少。 其次，我们引入了通过查找表进行条件更新的温度相关材料参数，在达到损失阈值后激活，以确保物理上有意义的温度。 选择了一个轴对称二维模型来准确表示焊接过程，同时保持计算效率。 为了减少计算负担，训练策略和模型组件首先在一维中进行了系统评估，从而实现了对损失设计和接触模型的受控分析。 二维网络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转移到铝，并展示了在工业应用中快速、基于模型的质量控制的强大潜力。 显示英文摘要

摘要： 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.