In the present study, the groups of cutting conditions that minimize surface roughness and its variability are determined, in ball-end milling operations. Design of experiments is used to define experimental tests performed. Semi-cylindrical specimens are employed in order to study surfaces with different slopes. Roughness was measured at different slopes, corresponding to inclination angles of 15°, 45°, 75°, 90°, 105°, 135° and 165° for both climb and conventional milling. By means of regression analysis, second order models are obtained for average roughness Ra and total height of profile Rt for both climb and conventional milling. Considered variables were axial depth of cut ap, radial depth of cut ae, feed per tooth fz, cutting speed vc, and inclination angle Ang. The parameter ae was the most significant parameter for both Ra and Rt in regression models. Artificial neural networks (ANN) are used to obtain models for both Ra and Rt as a function of the same variables. ANN models provided high correlation values. Finally, the optimal machining strategy is selected from the experimental results of both average and standard deviation of roughness. As a general trend, climb milling is recommended in descendant trajectories and conventional milling is recommended in ascendant trajectories. This study will allow the selection of appropriate cutting conditions and machining strategies in the ball-end milling process.

At present, laser cutting is currently employed to cut metallic plates, due to their good finish and dimensional quality, as well as because of the flexibility of the process to obtain different shapes. In the present paper, surface roughness, dimensional accuracy, and burr thickness of thin plates of 0.8 mm are studied as functions of different process parameters: pulse frequency, pulse width, and speed. Eight different experiments were performed according to a full 23 factorial design, with two replicates each. Square specimens of 10 mm × 10 mm were cut. Arithmetical mean roughness Ra was measured with a contact roughness meter, and the dimensions and burr thickness with a micrometer. Ra values ranged between 1.89 and 3.86 µm, dimensional error values between 0.22 and 0.93%, and burr thickness between 2 and 34 µm. Regression analysis was performed, and linear models were obtained for each response. Results showed that roughness depends mainly on frequency, on the interaction of frequency and pulse width and on pulse width. The dimensional error depends on pulse width, frequency, and the interaction between pulse width and speed. Burr thickness is influenced by frequency, pulse width, and the interaction between frequency and speed. Multi-objective optimization showed that, in order to simultaneously minimize the three responses, it is recommended to use high frequency (80 Hz), high pulse width (0.6 ms), and high speed (140 mm/min). The present study will help to select appropriate laser cutting conditions in thin plates, in order to favor good surface finish and dimensional accuracy, as well as low burr thickness.