Artificial neural networks for predicting diffusible hydrogen content and cracking susceptibility in rutile flux-cored arc welds
The aim of the current work was to build and validate a back propagation artificial neural network (BPANN) to predict the level of diffusible hydrogen (HD) based on a variety of flux-cored arc welding (FCAW) parameters such as welding current, contact-tip to work piece distance (CTWD) and shielding gas type for single run, horizontal bead-on-plate steel welds using seamed and seamless rutile consumable wires (Model 1). A probabilistic artificial neural network model (PANN) was then applied to determine the likelihood of hydrogen assisted cold cracking (HACC) in a gapped bead-on-plate (GBOP) test based on the level of HD and welding preheat temperature (Model 2). This approach could be applied to ‘real life’ situations with the availability of sufficient data.
Results are also presented for a sensitivity analysis for Model 1, which identifies the key input variables and the effect they have on the output, HD. It was determined that the consumable type and the CTWD were the key variables. A seamless consumable with a H5 classification and an increase in the CTWD, as well as increases in current led mostly to a decrease in the level of diffusible hydrogen. Additionally, Model 1 predicted that using a 25%CO2–75%Ar shielding gas, instead of 100%CO2, and an increase in absolute humidity (a function of relative humidity and temperature) in the surrounding atmosphere led to an increase in diffusible hydrogen deposited into the weld metal (WM). Finally, based on the predictions of Model 2 it was established that for the given welding conditions hydrogen cracking in a GBOP test could be avoided by maintaining hydrogen levels at less than ∼3 mL/100 g or using a preheat temperature slightly higher than 100 °C. Analysis of the data also led to the conclusion that further data were required for the GBOP tests when the hydrogen levels were between 4 and 8 mL/100 g to correct and improve confidence in this part of the model.