Resistance spot welding is a widely used joining method in automotive manufacturing, known for its efficiency and reliability. With growing regulatory scrutiny on vehicles’ carbon footprint, the body-in-white faces higher weight requirements. Therefore, aluminum alloys, particularly 5xxx and 6xxx series aluminum, are increasingly favored over steel due to their strength-to-density ratio. Despite this shift, research on the resistance spot welding of aluminum in large-scale production remains limited compared to steel. Furthermore, with the advent of big data and industry 4.0, the development and optimization of aluminum resistance spot welding may be addressed as a machine learning problem. In this study, we establish a first baseline model through machine learning and big data to predict the nugget diameter. The model is trained on tens of thousands of weld spots for both mentioned alloys across a wide process parameter range. Moreover, we provide a workflow for identifying the optimal hyperparameters for a suitable training algorithm. We identify a reasonable algorithm, determine the most important process parameters, and visualize their effects with shapley additive explanations. The performance of the baseline model serves as a benchmark for future machine learning models and provides a cornerstone for the experimental design decisions within the field. To ensure the reproducibility of results, we make available our baseline models and code on GitHub (https://github.com/JanAlexanderZak/SCAP_2024).


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    Title :

    A Baseline Model for Nugget Diameter Prediction Based on Process Parameters for Aluminum Resistance Spot Welding


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    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource




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