Machinability study on abrasive waterjet drilling of YSZ-coated inconel 718 superalloy: optimization and modelling by machine learning

The purpose of this research is to ascertain the optimal abrasive waterjet drilling parameters for making holes on Inconel 718 superalloy coated with yttrium-stabilized zirconia, namely waterjet pressure (J P ), standoff distance (S D ), abrasive flow rate (F R ), and angle of impact (A I ). The study explores the predictive modeling of the entry diameter (D N ) and exit diameter (D X ) of the drilled holes using an amalgamation of experimental analysis, response optimization and machine learning approaches. Eight different ML techniques are used to predict D N and D X . Better values of D N = 1.31352 mm and D X = 1.00515 mm are obtained through Random Forest for the setting of J P = 175 MPa, F R = 250 g min −1 , S D = 1.45455 mm, and A I = 0.909091 deg. Further, the tuning of hyperparameters of Random Forest algorithm is performed to study the improvement in measuring D N and D X . The least value of total absolute error = 0.468 is observed while finding the D N and D X using Random Forest algorithm than the traditional response optimization method with reference to the confirmation test results. The work provides important insights for enhancing the machinability of YSZ-coated Inconel 718 superalloy utilizing the AWJ drilling process by bridging the gap between manufacturing research, machine learning, and real-world applications.