The aim of this paper is to evaluate the usability of the self-organizing migrating algorithm (SOMA) in a nonlinear system predictive control area. The model predictive control is based on an objective function minimization. Two approaches to model predictive control applied on a nonlinear system are studied here. Firstly, the SOMA was used to minimize the objective function, secondly, the fmicon function included in the MATLAB optimization toolbox was used for the same. The nonlinear system simulated here is an exothermic semi-batch reactor mathematical model based on a real chemical exothermic process. Also the input data used here to simulate the process were obtained from the same real process. Results obtained by the simulation means were than evaluated using suitable criterion which was defined for that purpose and discussed.

- In this paper the usability of the self-organizing migrating algorithm (SOMA) in a nonlinear system predictive control area is studied. Two approaches to model predictive control applied on a nonlinear system are compared here. Firstly, the SOMA was used to minimize the objective function, secondly, the fmicon function included in the MATLAB optimization toolbox was used for the same. The comparison itself was made from four points of view. Firstly, the value of the in-reactor temperature overshoot and the related quality of the in-reactor temperature course were observed. Secondly, the time of processing which is important for effectiveness of a real plant and also the course of the actuating signal that is important from the practical point of view were monitored. The input data used here to simulate the process were obtained from the real chemical exothermic process.

The article focuses on the design, construction and manufacture of an inspection vehicle intended to access difficult-to-reach places. The vehicle is able to monitor piping system failures at the view angle of 180° in deep depths and adverse environments. The individual components of the inspection vehicle, more detailed their programming and production on CNC machines are discussed. The vehicle is supposed to easily run into the piping systems and can be safely pulled out. A control system was created for motion and video signal transmission to the operator from above the ground. Aluminum alloy (EN AW 2024) is the predominant material of the manufactured components and at the same time has ideal processing and operating properties. Proposed inspection vehicle is a robust and functional solution with minimal maintenance.

Robotic systems are used wherever human limits are achieved. The inspection robots are used for securing the access to the manholes, pipes and sewage systems, for monitoring faults and for the eventual minor repairs. The paper presents the design, construction and assembly of a four-wheeled inspection robotic rover used in sewage pipes with diameters larger than 200 mm. We managed to design the robust, waterproof and dirt-resistant washable rover solution, which has been proved to be very functional and easy to produce.

The current availability of powerful computing technologies enables using of complex computational methods. One of such complex method is also the self-organizing migrating algorithm (SOMA). This algorithm can be used for solving of various optimization problems. It may be used even for such complex task, as the non-linear process control is. In this paper, the capability of using SOMA algorithm for the model predictive control (MPC) of semi-batch chemical reactor is studied. The MPC controller including self-organizing migrating algorithm (SOMA) is used for the optimization of the control sequence. The reactor itself is used in chromium recycling process in leather industry.

High speed grinding methods are continuously being developed and have been highly accepted due to high productivity and resulting surface quality when machining materials dissimilar in physical and mechanical properties. Grinding wheels are the most important part of the entire technological system, in particular their stress state. Hence, the article focuses on the determination of the mathematical model of the grinding wheel with uniform strength. Based on it are calculated critical wheel speeds for various bonding material. Then, the optimal models of grinding wheel under high wheel speed are solved by finite element analysis.

Computer numerical control (CNC) allows achieving a high degree of automation of machine tools by pre-programmed numerical commands. CNC milling process is widely used in industry for machining of complex parts. The need of a description of the CNC milling process is necessary for production of precise parts. This paper introduces artificial neural network based modeling, while the CNC milling of moderate slope shapes is studied. The developed neural models consist of two inputs and two outputs. The created neural models were experimentally tested on the real data. Then, the evaluation and comparison of all models were performed.

bar. Abstract—The goal of this work is to create numerical model, which will be used for design and optimization of a rubber bushing for stabilizer bar. Thanks this model we are able to predict the mechanical behavior of the bushing. To get material constants for the model, the material of bushing (rubber) was tested in special deformations modes. A hyperelastic material model was set and it was implemented into the numerical model of the bushing. Critical points in the construction of bushing were reveled by the analysis of the numerical model.

Increasing urge to raise production rate and production quality in the industry brings new requests and challenges. One of them is demand for accuracy and precision of produced parts. Especially in the CNC machining, where the expectations are high, the companies face new issues. Therefore, it is very important to recognize, understand and cope with the technological factors influencing the production accuracy and surface quality of CNC machined parts.

The paper presents application of Siemens RobotExpert software of industrial robot offline programming. The deburring process of aluminium wheel is described and developed. The robotic work-cell contains robot ABB IRB 1600id and two axes positioner ABB IRBP A 750 D 1000 H 700. The final robot tool path is checked using the collision viewer, the joint status monitor, the tool centre point speed viewer and tracker.

A rubber boot as an important sealing part is often exposed of a large deformations. It is not easy to find optimal shape of this part. The analysis of the mechanical behavior of the rubber boot and its shape optimization is the object of this paper. Small strength and often failures of the rubber were the motivation of this work. We had to localize critical points in the boot profile and then the shape of the part was optimized. A new boot profile was designed and its mechanical behavior was verified. Advanced FEM system for nonlinear analyses was used to simulate mechanics of the boot. To simulate hyperelastic behavior of the rubber, we tested material in uniaxial tension and equibiaxial tension. The James-Green-Simpson hyperelastic model was chosen for the material of the boot and the hyperelastic material constants were determined from the tests results. The result of the work is a design of new profile of the boot with increased strength. Key-Words: rubber, boot, hyperelasticity, numerical model, FEM analysis, optimization, buckling

CNC machining is known as an advanced machining process increasingly used for modern materials. This paper outlines modeling methodology applied to optimize cutting parameters during CNC milling with ball end mill tool. The parameters taken into account were radial depth of cut and feed per tooth. A predictive model was based on artificial neural network approach. Key-Words: Modeling, artificial neural networks, CNC machining, surface roughness, feed-forward networks

The paper presents a control mechanism design for a semi-batch chemical reactor. The data obtained by chemical engineering analysis of real experiments are used to simulate the semi‑batch process. A mathematical model based on the real reactor geometry and size is used to simulate the whole process. The process simulations are implemented in MATLAB / Simulink environment and suitable PID and Model Predictive Control are also proposed. Because of that the chemical reactor is a complex and nonlinear system, the PID controller has to use an online identification to be able to deal with nonlinearities. Results obtained by simulations are compared and discussed.

Residual stresses lower the utility value of plastic parts.Determination of the induced stresses can help deal with them.Measurements are time-consuming and expensive.A new approach to measuring residual stresses, such as indentation measurement, can lead to the simple determination of residual stresses.The paper shows the relationship between the condition of injection moulding, the subsequent residual stress, and hardness through thickness.The computer model displays the field and magnitude of residual stress in the samples.The model results are then compared to measured parameters after indentation and the magnitude of residual stress determined by the standard hole drilling method.

The article deals with the interconnection of the Arduino microcontroller with sensors and with an interface which is connected to the Internet. Selected sensors are intended for home usage. We used sensors for measuring of temperature, humidity, light intensity and the status of doors or windows (opened / closed). Microcontroller can be programmed to respond on some triggered events and to run an appropriate action. Key-Words: Arduino, microcontroller, sensor, Internet, household control

M. Ovsik(a), D. Manas(a), M. Manas(a), M. Stanek(a), M. Hribova(a), K. Kocman(a), D. Samek(a), and Manas M.(b) ((a) Tomas Bata University in Zlin, Faculty of Technology, Department of Production Engineering, Zlin, (b) MITAS a. s., Prague, Czech Republic): Irradiated Polypropylene Studied by Mircohardness and Waxs Hard surface layers of polymer materials, especially polypropylene, can be formed by chemical or physical process. One of the physical methods modifying the surface layer is radiation cross-linking. Radiation doses used were 0, 30, 45, 60 and 90 kGy for unfilled polypropylene with the 5 % cross-linking agent (triallyl isocyanurate). Individual radiation doses caused structural and micromechanical changes which have a significant effect on the final properties of the polypropylene tested. Small radiation doses cause changes in the surface layer which make the values of some material parameters rise. The improvement of micromechanical properties was measured by an instrumented microhardness test. X-ray diffraction was used to study the influence of the structure.

Artificial neural networks provide powerful tools for linear and nonlinear system modeling and prediction. They are commonly used in various fields, such as economics, medicine, industry, aerospace, chemistry etc. Artificial neural networks are capable comprehend single input – single output as well as multiple input – multiple output functions. This paper is focused on prediction of non-artificial time series that are typically considered as single input – single output systems from the point of view of the predictor. The paper presents study of the influence of the input vector length to prediction quality. All simulations were done in MATLAB. Key-Words: artificial neural network, time series, prediction, benchmark,Santa Fe competition, Matlab

This experimental study describes the influence of radiation cross-linking to the structure and properties of polypropylene. The structure of polypropylene was assessed using microhardness measurement. It was verified that the structure influences the resulting material parameters. Polypropylene modified by radiation cross-linking at doses of 30, 45, 60 and 90kGy shows substantial changes of the structure and hence changes to resulting mechanical and micromechanical properties. The change of micromechanical properties is greatly manifested mainly in the surface layer of the modified polypropylene where a significant growth of microhardness values can be observed. The changes were examined and confirmed by X-ray diffraction and measurement on Transmission electron microscopy.

The paper studies time series prediction using artificial neural networks. The special attention is paid to the influence of size of the input vector length. Furthermore, the prediction of standard single-dimensional data signal and the prediction of multi-dimensional data signal are compared. The tested artificial networks are as follows: multilayer feed-forward neural network, recurrent Elman neural network, adaptive linear network and radial basis function neural network. Keywords—Artificial neural network, benchmark, prediction, time series, multi-dimensional data.