Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology

Welding metal alloys with dissimilar melting points makes conventional welding processes not feasible to be used. Friction welding, on the other hand, has proven to be a promising technology. However, obtaining the welded joint’s mechanical properties with characteristics similar to the base materials remains a challenge. In the development of this work, several of the machine learning (ML) regressors (e.g., Gaussian process, decision tree, random forest, support vector machines, gradient boosting, and multi-layer perceptron) were evaluated for the prediction of the ultimate tensile strength (UTS) in joints of AISI 1045 steel and 2017-T4 aluminum alloy produced by rotary friction welding with laser assistance. A mixed design of experiments was employed to assess the effect of the rotation speed, friction pressure, and laser power over the UTS. Furthermore, the response surface methodology (RSM) was employed to determine an empirical equation for predicting the UTS, and contours maps determine the main interactions. A total of 48 specimens were employed to train the regressors; the 5-fold cross-validation methodology was used to find the algorithm with greater precision. The gradient boosting regressor (GBR), support vector regressor (SVR), and Gaussian processes regressors present the highest precision with a less than 3% percentage error for the laser-assisted rotary friction welding process. The GBR and SVR capability exceed the RSM’s accuracy with a coefficient of determination (R2) greater than 90.9 versus 83.2%, respectively.