Abstract
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.
Highlights
In order to increase productivity and reduce costs, it is important to choose appropriate cutting conditions in high speed milling (HSM) processes because they will influence surface roughness and the dimensional precision obtained
Experiment 16 was chosen because it corresponds to high ap, ae, fz, and vc values, which lead to higher roughness values
Changes of surface topography can be observed as a function of machining strategy, position angle of the machined surface, and whether the tool displacement along fz trajectory is ascendant or descendant
Summary
In order to increase productivity and reduce costs, it is important to choose appropriate cutting conditions in high speed milling (HSM) processes because they will influence surface roughness and the dimensional precision obtained. When the tool is perpendicular to the workpiece’s surface, cutting speed is zero at the tool tip [1,2]. This implies that the tool tends to crush the material instead of cutting it. Neural networks provide a relationship between input and output variables by means of mathematical functions, to which different weights are applied. Neural networks have been used for modeling and predicting surface roughness in different machining operations. Feng et al modeled roughness parameters related to the Abbott–Firestone curve by means of ANN in honing operations [5]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
