Additive manufacturing (AM) technologies are becoming a global phenomenon in the manufacturing industry. The progressiveness of additive manufacturing lies in its universality. AM makes it possible to produce parts with complex shapes from different materials without any tools, using only one device. Complex and time-consuming production preparation is eliminated by using AM. It is used in a wide range of industries. Although additive manufacturing is a progressive technology, the currently applied conservative approach has significant limits. The presented work focuses on the development of a new methodology for controlling the AM process. This methodology is based on the outputs of the strength simulation of a specific component through the finite element method (FEM) and their implementation in the printing software of the production equipment. The developed algorithm for controlling the AM process consists of a sequence of successive steps. The designed CAD model of the component is subjected to FEM simulation in order to analyze the von Mises stress in the entire volume of the loaded component. Stresses are distributed asymmetrically in the volume of the component due to the shape and nature of the load. The results of the FEM analysis allow the definition of the volumes in the component with different levels of infill geometry and infill density based on different levels of stress. The FEM simulation also serves to define the effective fiber orientation. The goal of implementing FEM simulation into the building structure of the component is to achieve a symmetrical distribution of stresses in the entire volume. Through the symmetry of internal stresses, it is possible to obtain more efficient production with high productivity and component strength. The work also deals with experimental research on the effect of the building structure on flexural strength. The results of FEM simulation and experimental research are integrated into the developed slicer software to design a layering of the model and the setting of technological and material parameters of printing. This progressive approach makes it possible to generate data for 3D printing based on FEM analysis of components to obtain an optimized printed structure of components and optimized technological and material parameters with regard to maximizing the strength of components and minimizing production times and costs.

Industrial robots are a widely used technology in various industries, capable of performing multiple tasks, particularly machining operations. However, compared to CNC machines, industrial robots have certain drawbacks, such as lower rigidity, precision, and repeatability. These limitations are significant obstacles to fully integrating industrial robots into manufacturing processes. The deficiencies in robots directly impact the quality of machined workpieces, leading to reduced surface quality and geometric accuracy. Researchers have been exploring ways to improve production accuracy and quality using robotic arms for decades. Despite technological advancements, the full implementation of robotic arms in machining processes remains in its early stages. This article focuses on robotic milling and the effect of technological parameters on the final quality of the machined material, aiming to determine their importance for designing the main experimental plan. In our experiment, we used an ABB IRB 1100 robotic arm with a payload of 4 kg and a maximum reach of 0.58 m. The milling process was programmed in RobotStudio, which also supports milling complex shapes. Our statistical analysis showed that feed rate had the greatest impact on quality, while depth of field had the least influence. This information will guide further research in developing a mathematical model for robotic milling.

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.