Radosław Winiczenko

Estimation of tensile strength of ductile iron friction welded joints using hybrid intelligent methods

Abstract A hybrid intelligent method for evaluation of near optimal settings of friction welding process parameters of ductile iron was presented. The optimization of welding parameters was carried out in automatic cycle with the use of support vector regression (SVR), genetic algorithm (GA) and imperialist competitive algorithm (ICA). The method suggested was used to determine welding process parameters by which the desired tensile strength was obtained in the friction welding of ductile iron. The highest tensile strength (TS) of 256.93 MPa was obtained using SVR plus GA method for the following friction welding parameters: heating force 40 kN, heating time 300 s and upsetting force 10.12 kN. The samples were welded by friction and subjected to the tensile strength test. The optimized values obtained by means of these hybrid techniques were compared with the experimental results. The application of hybrid intelligent methods allowed to increase the tensile strength joints from 211 to 258 MPa for the friction welder ZT–14 type.

The microstructure and mass transport during friction welding of ductile cast iron

Purpose – The results of a study of friction welding of ductile cast iron using stainless steel interlayer are presented. Based on the microstructure evolution at the region close to the ductile cast iron‐stainless steel interface, the phenomena accompanying the process of joining were evaluated. Therefore, the purpose of this paper is to take a closer look into metallurgical phenomena accompanying the friction welding of ductile cast iron.Design/methodology/approach – In this paper, ductile cast iron and austenitic‐stainless steel are welded using the friction welding method. The tensile strength of the joints was determined using a conventional tensile test machine. Moreover, the hardness across the interface ductile cast iron‐stainless steel interface was measured on a metallographic specimen. The microstructure of the joints was examined using light metallography as well as electron microscopy. In this case, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were applied. En...

Some Remarks on Modelling of Mass Transfer Kinetics During Rehydration of Dried Fruits and Vegetables

Dehydration operations are important steps in the food processing industry. The basic objective in drying food products is the removal of water in the solids up to a certain level, at which microbial spoilage is minimized. The wide variety of dehydrated foods, which today are available to the consumer (dried fruits, dry mixes and soups, etc.) and the interesting concern for meeting quality specifications, emphasize the need for a thorough understanding of the operation [1].

Multi-objective optimization of convective drying of apple cubes

Abstract The effect of drying temperature and air velocity on apple quality parameters, such as color difference (CD), volume ratio (VR) and water absorption capacity (WAC) in convective drying was experimentally studied. Optimization of drying conditions was carried out in the range of air temperatures from 50 to 70 °C and air velocity from 0.01 to 6 m s−1. A novel algorithm of multi-objective optimization, based on artificial neural network (ANN), genetic algorithm (GA) and Pareto optimization was developed. Three optimization objectives included simultaneous minimization of CD, maximization of VR and maximization of WAC. Objective functions for CD, VR and WAC were developed by using ANN training on the experimental dataset of apple drying at 50, 60 and 70 °C. Pareto optimal set was developed with elitist non-dominated sorting genetic algorithm (NSGA II). Unique Pareto optimal solution within specified constraints was found at air temperature 65 °C and velocity 1 m s−1. This mode of apple drying resulted in CD = 5.24, VR = 49.66% and WAC = 0.488. Experimental verification showed that maximum error of modelling did not exceed 3.24%.

Thin-layer drying of sawdust mixture

Abstract Drying behaviour of sawdust mixture was investigated in a convective dryer at 0.01 m/s and 25, 60, and 150°C air temperature. Sawdust mixture (60% of spruce and 40% of the second ingredient: beech, willow, ash, alder) and sawdust of spruce, beech, willow, alder and ash was used in the drying experiments. The sawdust mixture drying was affected by the drying of its ingredients. The experimental drying data were fitted to the theoretical, semi–theoretical, and empirical thin-layer models. The accuracies of the models were measured using the correlation coefficient, root mean square error, and reduced chi–square. All semi-theoretical and empirical models described the drying characteristics of sawdust mixture satisfactorily. The theoretical model of a sphere predicts the drying of sawdust mixture better than the theoretical model of an infinite plane. The effect of the composition of the sawdust mixture on the drying models parameters were also taken into account.