Search results

Of the factors known to influence metabolic rate it is those with the most marked effect, such as exercise, food intake and temperature extremes, which have attracted the most interest and therefore been the most thoroughly investigated. While researchers have long agreed that emotional disturbance of a subject during a measurement of metabolic rate is likely to lead to errors in the measurement, the evidence for such an effect has been largely anecdotal and there has been very little systematic research attempted. The most widely available method of measuring metabolic rate is that of indirect calorimetry, that is, by estimating oxygen consumption, and the errors inherent in the method, coupled with the difficulty in achieving a consistent baseline, make the study of small increases in metabolic rate, such as would be expected to result from anxiety, very difficult.

This study aims to present a novel methodology for the evaluation of tribological properties of new nanocomposites with the A356 alloy matrix reinforced with aluminium oxide (Al₂O₃) nanoparticles.

Metal matrix nanocomposites (MMnCs) with varying amounts and sizes of Al₂O₃ particles were produced using a compocasting process. The influence of four factors, with different levels, on the wear rate, was analysed with the help of the design of experiments (DoE). A regression model was developed by using the response surface methodology (RSM) to establish a relationship between the observed factors and the wear rate. An artificial neural network was also applied to predict the value of wear rate. Adequacy of models was compared with experimental values. The extreme values of wear rate were determined with a genetic algorithm and particle swarm optimization using the RSM model.

The combination of optimization methods determined the values of the factors which provide the highest wear resistance, namely, reinforcement content of 0.44 wt.% Al₂O₃, sliding speed of 1 m/s, normal load of 100 N and particle size of 100 nm. Used methods proved as effective tools for modelling and predicting of the behaviour of aluminium matrix nanocomposites.

The specific combinations of the optimization methods has not been applied up to now in the investigation of MMnCs. In addition, using of small content of ceramic nanoparticles as reinforcement has been poorly investigated. It can be stated that the presented approach for testing and prediction of the wear rate of nanocomposites is a very good base for their future research.

This research aims to describe the influence of weight per cent of graphite (Gr), applied load and sliding speed on the wear behavior of aluminum (Al) alloy A356 reinforced with silicon carbide (SiC) (10 Wt.%) and Gr (1 Wt.% and 5 Wt.%) particles. The objective is to analyze the effect of the aforementioned parameters on a specific wear rate.

These hybrid composites are obtained by means of the compo-casting process. Tribological analyses were conducted on block-on-disc tribometer at three different loads (10, 20 and 30 N) and three different sliding speeds (0.25, 0.5 and 1 m/s), at the sliding distance of 900 m, in dry sliding wear conditions. Optimization of the tribological behavior was conducted via the Taguchi method, and ANOVA was used for the analysis of the specific wear rate. Confirmation tests are used to foresee and check the experimental results. Examined samples were analyzed via a scanning electron microscope (SEM). Regression models for predicting specific wear rate were developed with Taguchi and ANN (artificial neural network) methods.

The biggest impact on value of specific wear rate has the load (43.006%), while the impact of Wt.% Gr (31.514%) was less. After comparison of the results, i.e. regression models, for predicting the specific wear rate, it was observed that ANN was more efficient than the Taguchi method. The specific wear rate of Al alloy A356 with SiC (10 Wt.%) and Gr (1 Wt.% and 5 Wt.%) decreases with a decrease in the load and weight per cent of Gr-reinforcing material, as well as with a decrease in sliding speed.

The results obtained in this paper using the Taguchi method and the ANN method are useful for improving and further investigating the wear behavior of the SiC- and Gr-reinforced Al alloy A356.

Snacking is commonly regarded by the general public as unhealthy, believing that it is more beneficial to stick to an eating pattern of three meals a day. Similarly anyone on a weight reduction programme will avoid snacks, reducing the frequency of eating occasions to two to three times a day. However there is evidence to suggest that snacking is not the evil once thought and individuals that snack throughout the day have positive advantages over individuals who conform to a rigid pattern of three meals a day. Increasingly western populations appear to be moving away from the “gorging” to the “nibbling” pattern of eating, probably as a direct result of the increased availability of fast foods and snacks. Reviews the literature in the area of snacking and frequency of eating with respect to energy and nutrient intakes, body weight, body composition and energy balance and indicates the direction for further research.

Friction stir processing (FSP) is overviewed with the process variables, along with the thermal aspect of different metals.

With its inbuilt advantages, FSP is used to reduce the failure in the structural integrity of the body panels of automobiles, airplanes and lashing rails. FSP has excellent process ability and surface treatability with good corrosion resistance and high strength at elevated temperatures. Process parameters such as rotation speed of the tool, traverse speed, tool tilt angle, groove design, volume fraction and increase in number of tool passes should be considered for generating a processed and defect-free surface of the workpiece.

FSP process is used for modifying the surface by reinforcement of composites to improve the mechanical properties and results in the ultrafine grain refinement of microstructure. FSP uses the frictional heat and mechanical deformation for achieving the maximum performance using the low-cost tool; the production time is also very less.

100