Abstract Advanced materials and automated processes in manufacturing pose a challenge in terms of adaptability. Introduction of 3rd-generation advanced high strength (3rd-gen AHSS) steels aimed for weight reduction in the automotive without compromising its strength and efficient fuel consumption. Nevertheless, welding 3rd-gen AHSS using resistance spot welding (RSW) is often affected by liquid metal embrittlement (LME) or other quality matters. Identifying the process window to control and produce defect-free welds, requires huge experimental work with an enormous time. Therefore, this paper aimed to use machine learning (ML) to identify the process window by computing the relationship between the input parameters and output weld defect categories like 'Splash', 'LME', 'Insufficient nugget size' and 'Good weld'. Classification-based algorithms, K-nearest-neighbour (KNN) and Naive Bayes algorithms were used. Among these, Naive Bayes exhibits better prediction efficiency of 71% and KNN has 63%. Using these models, predicting the incidence of weld spot defects with the fore-mentioned predictability is possible. Therefore, this work supports the industry experts and researchers to study and predict the process window for the welding process to produce defect-free welds and this idea could implement in different manufacturing processes.

Abstract Brazing is one of the key technologies in the field of joining of metal components. To improve the wetting of brazing material and work-piece surface, it is often required to fall back on the use of flux. The application of these substances requires accuracy and is often connected with considerable expenditure and it is, just as the removal of flux residues, often an additional working step which has to be carried out manually. Within the framework of a DFG research project it has been investigated to which degree gaseous substances as addition to the shielding gas may replace conventional flux in TIG arc brazing. To this end, investigations have been carried out using different combinations of base and filler materials. Mainly monosilane as a gaseous flux substitute has been added in low concentrations to the shielding gas volume flow. The resulting brazed joints have been quantified with regard to their geometry, their fusion conditions and their chemical compositions. These qualities were then correlated and evaluated with the provided quantity of monosilane in order to identify dependencies.