Thermal conductivity of nickel superalloy MAR-M247

The paper presents the narrow connection between γ’ phase dissolving and values of thermal conductivity. In annealing process the free space among γ’ particles (blocks) changes in certain cycle from fine to rough and back to fine. This is accompanied by decrease and subsequent increase of thermal conductivity as well as the sample density. The results of thermal conductivity coarse are supported by image analysis.Web of Science55342242

Artificial neural networks application in modal analysis of tires

The paper deals with the application of artificial neural networks (ANN) to tires’ own frequency (OF) prediction depending on a tire construction. Experimental data of OF were obtained by electronic speckle pattern interferometry (ESPI). A very good conformity of both experimental and predicted data sets is presented here. The presented ANN method applied to ESPI experimental data can effectively help designers to optimize dimensions of tires from the point of view of their noise.Web of Science13527827

Sensitivity analysis: A tool for tailoring environmentally friendly materials

In this article, we examine the use of sensitivity analysis for the optimization of selected physical properties in rubber compounds and determine objective criteria which allow for the reduction of environmental load during rubber compound production. The sensitivity analysis shows how significantly each input value affects the output value, and the response graphs express the effect of the selected parameter on the output value. The solutions described in the article are applicable to other production technologies. We present a sensitivity analysis based on the prediction of selected mechanical properties of rubber mixtures composed of Standard Malaysian Rubber (SMR). Two blends were pre- pared by mixing SMR and oleic acid and different concentrations of surfactant (2, 4, 6, 8, 10, 20, 30 wt%). Tensile strength Rm and moduli M100, M200, M300 were measured and evaluated. The sensitivity analysis showed the significance of certain ingredients which affect the measured mechanical properties.Web of Science208art. no. 11803

Thermal aging of Menzolit BMC 3100

This paper deals with the influence of thermal aging on physical properties of a composite material, Menzolit BMC 3100. First, we present a number of analysis, FTIR (infrared spectroscopy), DSC (differential scanning calorimetry), TMA (thermomechanical analysis), TGA (thermogravimetric analysis), and HDT (heat deflection temperature), to understand the material performance under heat, and then, we carry out a test of toughness and strength using Charpy impact strength and Brinell hardness. Finally, we present optical surface analysis of the material under investigation by carrying out aging analysis at increments from room temperature up to 300 degrees C. It was observed that above 200 degrees C, the material begins to degrade at the surface, especially its organic component, polyester resin. This type of degradation has a negative impact on a variety of its physical properties. Exposure to temperatures above 200 degrees C reduces the material's hardness, toughness, and shape stability, likewise, material degradation was found to increase with higher thermal loads almost linearly for all the observed properties.Web of Science2020art. no. 857518

Proceedings of the 12th International Conference on Kinanthropology

Proceedings of the 12th Conference of Sport and Quality of Life 2019 gatheres submissions of participants of the conference. Every submission is the result of positive evaluation by reviewers from the corresponding field. Conference is divided into sections – Analysis of human movement; Sport training, nutrition and regeneration; Sport and social sciences; Active ageing and sarcopenia; Strength and conditioning training; section for PhD students

Prediction of metal corrosion by neural networks

The contribution deals with the use of artifi cial neural networks for prediction of steel atmospheric corrosion. Atmospheric corrosion of metal materials exposed under atmospheric conditions depends on various factors such as local temperature, relative humidity, amount of precipitation, pH of rainfall, concentration of main pollutants and exposition time. As these factors are very complex, exact relation for mathematical description of atmospheric corrosion of various metals are not known so far. Classical analytical and mathematical functions are of limited use to describe this type of strongly non-linear system depending on various meteorological-chemical factors and interaction between them and on material parameters. Nowadays there is certain chance to predict a corrosion loss of materials by artifi cial neural networks. Neural networks are used primarily in real systems, which are characterized by high nonlinearity, considerable complexity and great diffi culty of their formal mathematical description.Web of Science52338137