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Selection of the optimal paper-forming sieve obtained by the plasma welding

100%

Bojić N. , Nikolić R. , Hadzima B.

tom Vol. 2, Iss. 1

221--228

The paper-forming sieve is the most important part of the paper production machine. Reliability of the machine as a whole depends on the sieve's quality. An analysis of the two constructions of the paper forming sieves, which were obtained by the plasma welding of the sieve's wires, is presented in this paper. The mechanical characteristics of the two sieves, made of wires of diameters 0.15 mm and 0.20 mm, were tested, namely the tensile strength and elongation. The tests have shown that the sieve made of the wire of the smaller diameter has the better mechanical properties. This sieve is also better from the aspect of the water removal in the press section of the paper machine.

Thermal fracture characteristics of an interface crack subjected to temperature variations

100%

Kalinović S. M. , Djoković J. M. , Nikolić R. R. , Hadzima B.

Production Engineering Archives

2020

tom Vol. 26, Iss. 2

54--59

Thermal fracture characteristics - the thermal energy release rate and thermal stress intensity factor of a semi-infinite crack at an interface between the two elastic isotropic materials, subjected to the temperature variations, are considered in this paper. Those characteristics are determined based on application of the linear elastic fracture mechanics (LEFM) concept. Expressions for obtained theoretical solutions are compared to solutions from literature and they are found to be more concise. Influence of the materials change on these two thermal fracture properties were observed, as well as the influence of the thickness ratio of the two layers constituting the interface.

Prediction of the work-related injuries based on neural networks

88%

Ivaz J. , Nikolić R. R. , Petrović D. , Djoković J. M. , Hadzima B.

2021

tom Vol. 3, nr 1

19--37

Artificial neural networks (ANN) are a powerful tool in the decision-making process, especially in solving the complex problems with a large number of input data. The possibility to predict the work-related injuries in the underground coal mines, based on application of the neural networks, is analyzed in this work. the input data for the network were obtained based on a survey of 1300 respondents. After analyzing the input data influence on the network output, 14 most influential inputs were selected, with help of which the network correctly predicted whether the worker would suffer the work-related injury or not, with 80% precision. The two models were developed, based on the multilayer perceptron (MLP) and radial basis function (RBF) networks. The two models' results were compared to each other. The sensitivity analysis was used to select the most influential parameters, like mine, age of miners, as well as their work experience. The parameters were further analyzed by use of the descriptive statistics. The selected parameters are direct indicators of problems that can cause injuries. The obtained results point to the fact that the work-related injuries can be successfully predicted by application of the artificial neural networks. The proposed models' importance is reflected in the clear indicators for enforcing the stricter occupational safety and organizational measures in order to reduce the number of work-related injuries in underground mines.