In the automotive industry, aluminum alloys are increasingly favored over steel due to their superior strength-to-weight ratio, which benefits fuel efficiency and vehicle performance. However, research on aluminum resistance spot welding is less developed than on steel, leaving knowledge gaps in understanding the process. This study investigates the dynamic electrical resistance during resistance spot welding for two aluminum alloys, EN AW-5182 and EN AW-6014, addressing two gaps through machine learning, big data analysis, and leveraging knowledge drawn from steel resistance spot welding. The study focuses on two aspects. First, we use symbolic regression to identify a mathematical function describing the initial decay of dynamic resistance during the preheating. The resulting formula captures the behavior of the electrical resistance, describing an initial rapid increase proportional to time squared t^2 followed by an exponential decay following e^-t . Second, we develop a machine learning model to predict a critical point in the dynamic resistance at the end of the preheating. The model is trained with an aggregated data set of over 90,000 weld spots. The influence of the features is interpreted with Shapley additive explanations. It is found that material thickness and electrode force are the main factors affecting resistance at the end of preheating. Over a two-sheet material thickness range of 2 to 6 mms, the resistance increases by approximately 8 μΩ , while over an electrode force range of 5 to 8 kN, the resistance decreases by 3.5 μΩ .
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