Miroslava Švecová

Robotic milling offers a flexible alternative to conventional CNC machining centers; however, its wider application is limited by the lower structural stiffness of industrial robots and their increased sensitivity to vibrations. The objective of this study was to experimentally investigate the influence of robotic milling process parameters on machined surface quality and vibration intensity during the machining of the Corian polymer composite and AlMgSi aluminum alloy. The experiments were conducted using an ABB IRB 1100 industrial robot equipped with a high-speed milling spindle. Prior to the machining experiments, an experimental modal analysis of the robotic system was performed under static conditions to identify its dominant natural frequencies and potentially critical frequency regions. These results were used as one of the criteria for selecting the initial spindle speed ranges, together with the spindle operating limitations and preliminary machining tests. A full factorial experimental design was subsequently applied separately to each material to investigate the effects of spindle rotational frequency, feed per tooth, and cutting depth. Vibrations were evaluated using RMS values obtained from triaxial accelerometers, while surface quality was assessed by measuring surface roughness using a Keyence optical microscope. Response Surface Methodology was used to model the relationships between the machining parameters and the investigated responses and to identify favorable machining conditions within the investigated parameter domains. The results showed material-dependent differences in process behavior and model performance. In particular, the AlMgSi surface roughness model exhibited limited predictive capability, and its predicted favorable condition should therefore not be interpreted as a reliably established or experimentally validated optimum. The results provide a basis for selecting material-specific machining conditions aimed at reducing vibration levels and improving surface quality in robotic milling.

The Volume 12, published for JFFHMT Journal.