This study aims at evaluating the efficiency of sensor fusion, based on neural networks, to estimate the microstructural characteristics of both the weld bead and base material in GMAW processes. The weld beads of AWS ER70S-6 wire were deposited on SAE 1020 steel plates varying welding voltage, welding speed, and wire-feed speed. The thermal behavior of the material during the process execution was analyzed using thermographic information gathered by an infrared camera. The microstructure was characterized by optical (confocal) microscopy, scanning electron microscopy, and X-ray Diffraction tests. Finally, models for estimating the weld bead microstructure were developed by fusing all the information through a neural network modeling approach. A R value of 0.99472 was observed for modelling all zones of microstructure in the same ANN using Bayesian Regularization with 17 and 15 neurons in the first and second hidden layers, respectively, with 4 training runs (which was the lowest R value among all tested configurations). The results obtained prove that RNAs can be used to assist the project of welded joints as they make it possible to estimate the extension of HAZ.

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Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) quickly reached a global pandemic status in 2020 creating a huge demand for personal protective and medical equipment. Additive manufacturing, in this context, became a solution to alleviate the demands of the healthcare sector. This article presents how lean manufacturing approaches and Design for Additive Manufacturing (DfAM) helped to implement an additive manufacturing line for face shields production. The methodological approach was action research, in which undergraduate and graduate students, professionals, and researchers joined forces to produce and deliver 27,000 face shields. The article contributes towards the implementation of additive manufacturing for small batches production of product final parts and offers content for future discussions regarding production digitization.