A welding robotization has been used to improve the weld quality, minimize the process of trial and error, ensure process repeatability, and above all avoid the requirement for a highly qualified professional. The objective of this work is to verify the possibility of welding robot be programmed by the desired characteristics of the weld bead and in the case of use stainless steel also by the amount of ferrite in the weld bead. To that, experimental data were obtained under laboratory conditions, using an industrial robot that made welds with gas metal arc-welding process. Welds were made with different values of voltage, current, and different filler material and the following output parameters were measured from the weld bead: ferrite quantity, width, reinforcement, and penetration. Were used three different austenitic stainless steel welding wires and the same plate material (AISI 304), among other parameters that were kept constant. A fed forward artificial neural network, fully connected and supervised learning, was created from the experimental data. The mean absolute percentage error found to ferrite quantity was 4% and maximum was 17%. To width, penetration, and reinforcement of the weld beads, mean absolute percentage errors were, respectively, 5, 6, and 15% and the maximum 20, 23, and 47%. Artificial neural networks are able to predict the great complexity existing between the welding parameters in this case. This statement was made comparing the results with other methods of ferrite prediction and geometric parameter prediction.
Endotracheal aspiration is a routine procedure for managing secretions in mechanically ventilated patients and other cases of secretion accumulation. However, this procedure can lead to hypoxemia and tracheobronchial mucosal injury, and patients describe it as painful and uncomfortable. An adequate design of the suction catheter can help to avoid the injuries, decrease the risk of hypoxemia and patient's discomfort, and increase the efficiency of the procedure. The objective of this study is to evaluate the influence of the suction catheter design on suctioning efficiency of mucus with different properties, at different pressures, using computational fluid dynamics. Three concentrations of polyethylene glycol solution with molecular weight of 5,000,000 g/mol were prepared to simulate the airway mucus. Three parameters of catheter design related to the local pressure drop were defined for the analysis: mucus input area, distance between the lateral holes, and number of lateral holes. The influence of suction catheter length on the total pressure drop was also analyzed. The results of the study show that with the increase in the input area, the pressure drop decreases for all fluids and pressures. The suction catheter with aligned holes produced the largest pressure drop. As expected, catheter length had a critical influence on the pressure drop for all fluids and operational pressures, and therefore, it is advisable to use the shortest catheter possible.
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