Uncertainty analysis of milling parameters using Monte Carlo simulation, the Taguchi optimization method and data-driven modeling

Surface roughness plays an important role in the performance of finished structures. The surface quality obtained is enormously affected by cutting parameters. Therefore, the purpose of the present study is to examine the surface roughness value of aluminum 7075 workpiece material during milling operation by considering three steps: (1) the multi-nonlinear regression (MNLR) modeling basis of Taguchi design, (2) optimization based on signal to noise ratio (S/N), and (3) probabilistic uncertainty analysis depending on Monte Carlo technique as a result of depth of cut, cutting speed and feed rate. The depth of cut of 0.2 mm, cutting speed of 900 m x min(-1), and feed rate of 0.1 mm x tooth(-1) were determined as Taguchi-optimized conditions with a surface roughness of 0.964 mu m. In order to justify the surface roughness predicted under optimized conditions in relation to the predicted Taguchi method, three repetitive verification experiments were performed and surface roughness of 0.964 mu m +/- 0.3% was achieved. The best-fit MNLR method with an R-pred(2) (predicted regression coefficient) of 98.02 % is useful for calculating the success of estimating the outcome variable. Monte Carlo simulations were found to be quite effective for identifying the uncertainties in surface roughness that could not be captured by means of deterministic methods.