Jan Górecki

Static Compression Tests of Concentrated Crystallized Carbon Dioxide

This paper presents the results of static compression tests of concentrated crystallized carbon dioxide. The test results obtained under this research describe the relationship between the compressive stress and strain allowing to estimate the proportionality ratio, limit stress and critical stress values. Special test methods were used due to low temperature of crystallized carbon dioxide (minus 78.5°C) and its sublimation under standard testing conditions. The results of the mechanical properties of agglomerated CO2 were compared with the mechanical properties of other known materials. This allowed us to derive assumptions, which can become the starting point of the process to build a mathematical model describing the dry ice compaction and granulation processes.

Dry Ice Compaction in Piston Extrusion Process

Abstract The article presents the results of research on the effect of extrusion tube geometry on the axial force being the key parameter of the dry ice piston extrusion process. The tests were carried out with the experimental set-up based on a cylindrical extrusion tube used alone and supplemented with reducer (orifice). The focus of the experiments was to determine the effect of compression tube reducer on the value of the force of resistance FOP in the dry ice compression process. Its value can subsequently be used as the basis for establishing guidelines for designing and building machines for compression and pelletizing of dry ice.

Recognition of Seat Elements during Production through Image Processing

The primary aim of the research, whose results are presented in this article, was to investigate the process of visual identification of objects. The objects recognised under this research were passenger seats for mass transit applications. The system presented in this paper is intended to be incorporated in the production process control system. The problem under analysis is based on the need to recognise (identify) an object located in the process area of the machine before proceeding with the process. Neural network was chosen as the adaptive element due to a high degree of similarity between the semi-products. The function of the network was to check if correct element had been fed. The input data fed to the network was the information from processing the photo images of these elements. The results of research as presented in this article are based on a limited set of elements and present the method used for solving the task of identification.

Geometric Indicators of Flexible Slice Lip

The paper is thematically connected with paper making machines. This type of the machine is equipped with a head which has a flexible slice lip for skimming the pulp applied to produce the paper. The definition of geometric features of the slice lip is a key issue. The slice lip should be made of material which is resistant to external factors, and designers want to obtain a high flexibility of this element. It is possible to get a high flexibility by forming a proper shape of the slice lip. The paper presents the proposal of dimensionless geometric indicators which combine basic geometric parameters of the slice lip. These indicators are the following: indicator of the cross-section, indicator of the length and indicator of the length of the transverse notch. FEM strength analyses were done – the aim of these analyses was the determination of the stress state and maximum deflection of the slice lip. The authors have analysed the slice lip with holes for mounting the actuators and the slice lip and with transverse notches for increasing its flexibility. The results were presented in the form of graphs.

The Effect of Magnetic Field on Magnetorheological Composites - Artificial Neural Network Based Modelling and Experiments

Looking for new applications of the available materials, such as magnetorheological elastomers (MERs) is an important element of machine design process. To this end it is necessary to determine their fundamental mechanical properties, including Young’s modulus and shear modulus. These properties are determined experimentally by testing the material in compression, tension and shear. In the case of the analysed group of materials the above-mentioned constants depend, inter alia, on the parameters of magnetic field acting on them. Therefore, it is necessary to determine the character and the extent of variation of the mechanical properties as a function of the physical constants characterising the active magnetic field, namely magnetic flux and magnetic intensity (field strength).This paper presents the results of static compression tests carried out on magnetorheological elastomers. The parameters measured during the static compression test were force and displacement at a pre-set magnetic flux. The maximum strength of the induced magnetic field was limited by the design parameters of the test set-up. In order to determine the behaviour of the material at greater values of magnetic strength and flux the properties of a real material were modelled with a neural network. The simulation was carried out using a simple, one-layer neural network. The chosen network training approach was error backpropagation. This approach enables approximation and predicting of changes of the properties of the tested material. The output results will enable deriving an analytical model of the tested MREs.