The evaluation of the Resistance Spot Welding (RSW) that guarantees satisfactory performance of mechanical characteristics without altering physical properties can be reached by modeling the input parameters such as current, welding time, and applied force from which each unit has been built and correlating with digital images of the surface and infrared images that allows to identify variations on the parameters that modify the quality of the welding spot [1]. With this, mechanical and surface characteristics can be detected without the need for a mechanical test that modifies the structure of the unit. The database serves as a comprehensive record of the welding spot process, including the monitor of crucial input parameters such as current and force. The constructions and documentation of the testing platform through the instrumentation of a resistance welding will assess the variability of the input parameters and their impact on the output in surface and thermographic imaging, welding nugget diameter and it's mechanical strength. Additionally, it documents characteristics of the material used as thickness and material type and its output as the mechanical resistance and nugget diameter, along with its corresponding classification. Thus, the database not only captures the details of the welding process, but it also provides a valuable resource for analyzing and evaluating the performance of the welding operation.

Quality assessment of the resistance spot welding process (RSW) is vital during manufacturing. Evaluating the quality without altering the joint material’s physical and mechanical properties has gained interest. This study uses a trained computer vision model to propose a cheap, non-destructive quality-evaluation methodology. The methodology connects the welding input and during-process parameters with the output visual quality information. A manual resistance spot welding machine was used to monitor and record the process input and output parameters to generate the dataset for training. The welding current, welding time, and electrode pressure data were correlated with the welding spot nugget’s quality, mechanical characteristics, and thermal and visible images. Six machine learning models were trained on visible and thermographic images to classify the weld’s quality and connect the quality characteristics (pull force and welding diameter) and the manufacturing process parameters with the visible and thermographic images of the weld. Finally, a cross-validation method validated the robustness of these models. The results indicate that the welding time and the angle between electrodes are highly influential parameters on the mechanical strength of the joint. Additionally, models using visible images of the welding spot exhibited superior performance compared to thermal images.