Peter Križan

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.

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.

The Volume 12, published for JFFHMT Journal.

Nowadays, automation is a dominant process in the engineering industry using several modern technologies, one of which is machine vision. Machine vision is a technology used to control processes performed in automated manufacturing, based on the recognition of geometric shapes of an object and measuring their distance using only a camera sensor. Object recognition is a common practice in all industrial cameras. Many industrial cameras can determine the shape of the imaged object, orientation, and position with maximum accuracy. In this paper, we will present the error correction of Cognex camera. The Cognex camera will be used in conjunction with an IRB 1100 robotic arm, where the resulting correction will be applied for accurate tool positioning in the milling process.

The presented article presents a summary of research findings based on a comparison of two statistical methods, both of which were applied to the same problem. These were data that were measured during the biomass compaction process, with softwood used as the input material. The aim of the analysis was to create models for both selected statistical methods that would sufficiently describe the given process and subsequently compare them. The second very important step was to compare the applicability of these methods from the point of view of limiting criteria and to define their advantages and disadvantages in practical use. For the purposes of the experiment itself, the biomass pressing process was used using a uniaxial press. In the case of this process, parameters were defined that significantly affect the quality of the resulting press, which is represented by the measured quantity - the press density. The parameters already mentioned, which by their nature significantly affect the quality of the press, are the pressing pressure, pressing temperature, humidity of the pressed material and the size of the pressed material fraction. The process itself and the subsequent experimental obtaining of results from the given research took place in laboratory conditions using an experimental uniaxial press. Subsequently, through the steps of statistical analysis, we carried out estimates of effects, individual tests of hypotheses about the significance of the model and effects and, last but not least, also the predictive ability of the models thus obtained. The obtained models, which were created using the two methods already mentioned, were subsequently compared based on their predictive ability, specifically through the so-called predictive ability of the model, which is represented by the coefficient of determination R2, which is defined as the ratio of the sum of squares SSM explained by the model to the total sum of squares SST. As a result, it expresses the degree of agreement of the observed values with the model. Based on the experimental knowledge thus obtained, the aim is to point out the significance and importance of statistical methods and methodologies that are useful in processing data of various nature and scope. It is precisely the modelling of a process, which is often complex in nature or demanding on the data obtained, that needs to be differentiated in the statistical methods used. The research results presented in the paper demonstrate their significance.

The paper presents a findings in simulation of the workspace and manipulability of a robotic arm with a spindle. Workspace and manipulability simulation is an important step in next implementation into production cell and it can easily improve CAD and layout design of the production line. Authors would like to present also the torques calculation of individual axes of robotic arms. Presented activities are essential condition for the upcoming experimental design which will be executed in our institute. Goals of research activities on our institute is to determine the functional dependencies between technological parameters of robotic milling, and rigidity of robotic arm, which can be easily evaluated by final machined surface quality and precision of machined components. The paper covers mechanical and simulation system of the existing stand which includes the ABB IRB 1100/0.58 robotic arm.

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.

Additive manufacturing (AM) technologies represent a progressive way of manufacturing components from different materials using only one production device. Additive manufacturing is gaining a distinct advantage in manufacturing industries because of its ability to manufacture parts with complex shapes without any tooling requirement or human interface. Its use covers a wide range of industries. The present work focuses on development of a new methodology for control of the AM process based on the integration of the outputs of the strength analysis into the printing software of the production equipment. The developed algorithm is based on several subsequent steps. The strength analysis is used for solving the CAD model of the printed part by the Finite Element Method (FEM). It is clear from the FEM results that the stresses are inhomogeneously distributed in the volume of the component. The methodology of the algorithm is based on dividing the part into separate volume areas with different stress levels. These separate volumes can be produced by AM with different printing orientations and different types and densities of infill. The results of stress analysis are integrated into the developed slicer software to design a layering of the model and the setting of technological and material parameters of printing. The goal is to generate data in the form of G-codes for the optimized printed structure of the component to maximize its strength and minimize production time and costs.

Topological optimization is a relatively new method of optimizing shapes and volumes of parts. Mathematics, mechanics, metaphysics, and computer science can all benefit from this field's rapid growth in research sectors. Currently, the robotic structures are successfully used in many industrial areas, and also for machining. Robotic structure with its large variability of movements can be easily used for machining operation. For this purposes also the CAM simulation should be used. The intention of this paper is to describe the methodology of topological optimization implementation in the CAM simulation of robotic machining. It is not usual to simulate the machining operation in robotic environment, in addition, if the 3D model is modified by topological optimization. This methodology can be the base of using robotic structure for hybrid additive manufacturing.

There is a large number of materials that can be used for FDM additive manufacturing technology. These materials have different strength properties, they are designed for different purposes. They can be highly strong or flexible, abrasion-resistant, or designed for example for environments with higher thermal loads. However recently new innovative and progressive materials have come to the practice, which include nano-composite particles, bringing new added value. One such material is the Conductive PLA material, which is capable of conducting electric current. The aim of this article is to present the material properties of this material. The article describes the design of the experiment, the process of measuring the resistance of samples printed by FDM device, measuring the maximum tensile strength of samples. The article includes a statistical evaluation of the measured data, with the determination of the significance of individual factors of the experiment as well as the evaluation of the overall result of the experiments.