The purpose of this dissertation is to familiarize the reader with basic recognition of machine measuring probes for tools and workpieces. Namely then in area of measurement on CNC machining centers. Both recognition from aspects of principle of function of sensing elements and type of transmition to control system, and recognition from constructional aspects are described here. Further it puts mind on equipment of machine probes (touches etc.) and rules for best selection of this equipment. Next part of this work describes selection of appropriate probes, for universal machines from production of firm TOS Kuřim. Last part deals with testing of designed measuring system for concrete machine from production of firm TOS Kuřim. His typical composition, evaluation of tests results and achieved accuracy is also described here.

The aim of the thesis is focused on the proposal of a new technological procedure of the blade carrier for use in its own production. Currently, the carrier is manufactured by the German company B.Maier Zerkleinerungstechnik GmbH. The work includes several parts. The first part deals with the component itself, its function and importance, as well as its function in the assembly and the whole machine. In the next part the technological procedure of the blade carrier production is created. Fleet of machines including the necessary tools is also designed. In a separate chapter there is an economic evaluation of the proposed solution in two different variants, a subsequent evaluation and a proposal for implementation.

Esta tese teve por objetivo desenvolver um algoritmo preciso e robusto capaz de compensar os erros térmicos volumétricos de um centro de usinagem 5 eixos em diferentes condições operacionais. O comportamento térmico da máquina foi modelado usando técnicas do método dos elementos finitos (MEF) com base na teoria do calor de atrito e calor de convecção, e validadas através dos vários campos de temperatura obtidos experimentalmente usando termopares e imagens térmicas. Os principais subsistemas da máquina foram inicialmente modelados, como o conjunto de fusos de esferas, guias lineares e motofuso, o que permitiu posteriormente a validação do comportamento termoelástico da máquina completa para onze ciclos de trabalho em vazio, seis ciclos de usinagem, nove ciclos de posicionamento e dois ciclos com temperatura ambiente variando, obtendo erros máximos inferiores a 9%, ao comparar os resultados numéricos com os resultados experimentais. A validação do modelo em elementos finitos permitiu usar os resultados obtidos para treinar e validar uma rede neural artificial (RNA) para prever os erros térmicos do centro de usinagem. Os desvios entre os erros térmicos previstos pela RNA e os calculados pelo MEF foram inferiores a 5%. Baseado nos resultados obtidos pelas medições das peças de trabalho usinadas foi possível formular e implementar um modelo de compensação dos erros térmicos no CNC do centro de usinagem, que obteve uma redução dos erros entre 62% e 100% nas peças usinadas com compensação. Foi também proposto um algoritmo de previsão e compensação dos erros térmicos para o centro de usinagem, baseado em todos os ciclos e simulações realizadas, e que se comparando com os resultados experimentais mostrou-se capaz de reduzir os erros térmicos entre 50% e 95%. Após sua validação, foi possível concluir que o algoritmo desenvolvido é uma ferramenta precisa e robusta para compensar os erros térmicos da máquina para várias condições de trabalho, podendo compensá-los mesmo com esta movendo-se a diferentes velocidades, em usinagem ou mesmo operando em temperatura ambiente variável. / This thesis aims to develop an accurate and robust algorithm capable of compensating the volumetric thermal errors of a 5-axis machining center under different operating conditions. The thermal behavior of the machine was first modeled using finite element method (FEM) techniques based on theory of friction heat and convection heat, and validated with the various experimentally raised temperature fields using thermocouples and thermal imaging. The main machine subsystems were initially modeled, such as the ball screw system, linear guides and spindle, which allowed for validating of the thermoelastic behavior of the entire machine for eleven no load duty cycles, six cycles of machining, nine cycles of positioning and two cycles with varying ambient temperature, obtaining errors lower than 9%, when comparing the numerical results with the experimental results. The validation of the finite element model allowed for the use of the results obtained to train and validate an artificial neural network (ANN) for predicting the thermal errors of the machining center. The deviations between the thermal errors predicted by ANN and the FEM simulation results were less than 5%. Based on the results obtained by the measurements of the machined workpieces, it was possible to formulate and implement a model of compensation of the thermal errors in the CNC of the machining center, which obtained a reduction of errors of 62% and 100% of the machined parts with compensation. It was also proposed a thermal error prediction and compensation algorithm for the machining center, based on all cycles and simulations performed, and that, comparing with the experimental results, it was able to reduce the thermal errors between 50% and 95%. After its validation, it was possible to conclude that the developed algorithm is an accurate and robust tool to compensate the thermal errors of the machine for various duty conditions, being able to compensate the errors even when it is moving at different speeds, in machining process or even operating in variable ambient temperature.

Esta tese teve por objetivo desenvolver um algoritmo preciso e robusto capaz de compensar os erros térmicos volumétricos de um centro de usinagem 5 eixos em diferentes condições operacionais. O comportamento térmico da máquina foi modelado usando técnicas do método dos elementos finitos (MEF) com base na teoria do calor de atrito e calor de convecção, e validadas através dos vários campos de temperatura obtidos experimentalmente usando termopares e imagens térmicas. Os principais subsistemas da máquina foram inicialmente modelados, como o conjunto de fusos de esferas, guias lineares e motofuso, o que permitiu posteriormente a validação do comportamento termoelástico da máquina completa para onze ciclos de trabalho em vazio, seis ciclos de usinagem, nove ciclos de posicionamento e dois ciclos com temperatura ambiente variando, obtendo erros máximos inferiores a 9%, ao comparar os resultados numéricos com os resultados experimentais. A validação do modelo em elementos finitos permitiu usar os resultados obtidos para treinar e validar uma rede neural artificial (RNA) para prever os erros térmicos do centro de usinagem. Os desvios entre os erros térmicos previstos pela RNA e os calculados pelo MEF foram inferiores a 5%. Baseado nos resultados obtidos pelas medições das peças de trabalho usinadas foi possível formular e implementar um modelo de compensação dos erros térmicos no CNC do centro de usinagem, que obteve uma redução dos erros entre 62% e 100% nas peças usinadas com compensação. Foi também proposto um algoritmo de previsão e compensação dos erros térmicos para o centro de usinagem, baseado em todos os ciclos e simulações realizadas, e que se comparando com os resultados experimentais mostrou-se capaz de reduzir os erros térmicos entre 50% e 95%. Após sua validação, foi possível concluir que o algoritmo desenvolvido é uma ferramenta precisa e robusta para compensar os erros térmicos da máquina para várias condições de trabalho, podendo compensá-los mesmo com esta movendo-se a diferentes velocidades, em usinagem ou mesmo operando em temperatura ambiente variável. / This thesis aims to develop an accurate and robust algorithm capable of compensating the volumetric thermal errors of a 5-axis machining center under different operating conditions. The thermal behavior of the machine was first modeled using finite element method (FEM) techniques based on theory of friction heat and convection heat, and validated with the various experimentally raised temperature fields using thermocouples and thermal imaging. The main machine subsystems were initially modeled, such as the ball screw system, linear guides and spindle, which allowed for validating of the thermoelastic behavior of the entire machine for eleven no load duty cycles, six cycles of machining, nine cycles of positioning and two cycles with varying ambient temperature, obtaining errors lower than 9%, when comparing the numerical results with the experimental results. The validation of the finite element model allowed for the use of the results obtained to train and validate an artificial neural network (ANN) for predicting the thermal errors of the machining center. The deviations between the thermal errors predicted by ANN and the FEM simulation results were less than 5%. Based on the results obtained by the measurements of the machined workpieces, it was possible to formulate and implement a model of compensation of the thermal errors in the CNC of the machining center, which obtained a reduction of errors of 62% and 100% of the machined parts with compensation. It was also proposed a thermal error prediction and compensation algorithm for the machining center, based on all cycles and simulations performed, and that, comparing with the experimental results, it was able to reduce the thermal errors between 50% and 95%. After its validation, it was possible to conclude that the developed algorithm is an accurate and robust tool to compensate the thermal errors of the machine for various duty conditions, being able to compensate the errors even when it is moving at different speeds, in machining process or even operating in variable ambient temperature.

This master‘s thesis deals with the suggestion of suitable tools for milling aluminum alloys profiles for the construction of aperture fillings and their aplication to a CNC machining center for increasing the efficiency of the cutting process. The first part of the thesis is focused on the description of the company and the workplace of production of aluminum alloys constructions. The following pages contain description of standardized products, which are used to identify the test profile that is part of the experimental verification. Next part of this thesis is characteristic of aluminum alloys and tool materials suitable for their machining. Second part of this work contains an analysis of the current state, suggestion of new cutting tools and their application to the production proces. At the end of this thesis there is the technical-economic evaluation that deals with the time and economic savings of the applied design.

The Master's thesis deals with the safety of operator activities at the BASICTURN vertical turning center. The first part is focused on the requirements arising from the directives of the European Parliament and the Council in relevant areas and a system analysis of the issue. The second part is a risk analysis related to operator activities on the operated machine.