‘Hard’ and ‘soft’ methods in analyses of territorial structures’. This article refers to two distinct approaches to investigations of territorial structures and their changes: the ‘intuitive’ of ‘soft’ approach and a more rigid, formalized or ‘hard’ one. The examples of analyzing the regional patterns in Poland over a almost 40 year span are called to illustrate these relations between two methodological standpoints. The conclusion states that both of them are valid and useful, however their strengths can be fully exposed when both are applied in an comprehensive way, supporting each other in a difficult process of investigation multidimensional and dynamic changes of the social territorial systems.
The dynamic development of wind power in recent years has generated the demand for production forecasting tools in wind farms. The data obtained from mathematical models is useful both for wind farm owners and distribution and transmission system operators. The predictions of production allow the wind farm operator to control the operation of the turbine in real time or plan future repairs and maintenance work in the long run. In turn, the results of the forecasting model allow the transmission system operator to plan the operation of the power system and to decide whether to reduce the load of conventional power plants or to start the reserve units. The presented article is a review of the currently applied methods of wind power generation forecasting. Due to the nature of the input data, physical and statistical methods are distinguished. The physical approach is based on the use of data related to atmospheric conditions, terrain, and wind farm characteristics. It is usually based on numerical weather prediction models (NWP). In turn, the statistical approach uses historical data sets to determine the dependence of output variables on input parameters. However, the most favorable, from the point of view of the quality of the results, are models that use hybrid approaches. Determining the best model turns out to be a complicated task, because its usefulness depends on many factors. The applied model may be highly accurate under given conditions, but it may be completely unsuitable for another wind farm.
The results of statistical analysis applied in order to evaluate the effect of the high melting point elements to pressure die cast silumin on its tensile strength Rm, unit elongation A and HB were discussed. The base alloy was silumin with the chemical composition similar to ENAC 46000. To this silumin, high melting point elements such as Cr, Mo, V and W were added. All possible combinations of the additives were used. The content of individual high melting point additives ranged from 0.05 to 0.50%. The tests were carried out on silumin with and without above mentioned elements. The values of Rm, A and HB were determined for all the examined chemical compositions of the silumin. The conducted statistical analysis showed that each of the examined high melting point additives added to the silumin in an appropriate amount could raise the values of Rm, A and HB. To obtain the high tensile strength of Rm = 291 MPa in the tested silumin, the best content of each of the additives should be in the range of 0.05-0.10%. To obtain the highest possible elongation A of about 6.0%, the best content of the additives should be as follows: chromium in the range of 0.05-0.15%, molybdenum 0.05% or 0.15%, vanadium 0.05% and tungsten 0.15%. To obtain the silumin with hardness of 117 HB, chromium, molybdenum and vanadium content should be equal to about 0.05%, and tungsten to about 0.5%.
Conducting reliable and credible evaluation and statistical interpretation of empirical results related to the operation of production systems in foundries is for most managers complicated and labour-intensive. Additionally, in many cases, statistical evaluation is either ignored and considered a necessary evil, or is completely useless because of improper selection of methods and subsequent misinterpretation of the results. In this article, after discussing the key elements necessary for the proper selection of statistical methods, a wide spectrum of these methods has been presented, including regression analysis, uni- and multivariate correlation, one-way analysis of variance for factorial designs, and selected forecasting methods. Each statistical method has been illustrated with numerous examples related to the foundry practice.
The study presents the results of the application of a statistical analysis for the evaluation of the effect of high-melting additions introduced into a pressure cast Al-Si alloy on the obtained level of its proof stress Rp0.2. The base Al-Si alloy used for the tests was a typical alloy used for pressure casting grade EN AC-46000. The base alloy was enriched with high-melting additions, such as: Cr, Mo, V and W. The additions were introduced into the base Al-Si alloy in all the possible combinations. The content of the particular high-melting addition in the Al-Si alloy was within the scope of 0.05 to 0.50%. The investigations were performed on both the base alloy and alloy with the high-melting element additions. Within the implementation of the studies, the values of Rp0.2 were determined for all the considered chemical compositions of the Al-Si alloy. A database was created for the statistical analysis, containing the independent variables (chemical composition data) and dependent variables (examined Rp0.2 values). The performed statistical analysis aimed at determining whether the examined high-melting additions had a significant effect on the level of Rp0.2 of the Al-Si alloy as well as optimizing their contents in order to obtain the highest values of the Al-Si alloy's proof stress Rp0.2. The analyses showed that each considered high-melting addition introduced into the Al-Si alloy in a proper amount can cause an increase of the proof stress Rp0.2 of the alloy, and the optimal content of each examined high-melting addition in respect of the highest obtained value of Rp0.2 equals 0.05%.
Forecasting and analysis SWOT are helping tools in the business activity, because under conditions of dynamic changes in both closer and more distant surroundings, reliable, forward-looking information and trends analysis are playing a decisive role. At present, the ability to use available data in forecasting and other analyzes according with changes in business environment are the key managerial skills required, since both forecasting and SWOT analysis are a integral part of the management process, and the appropriate level of forecasting knowledge is increasingly appreciated. Examples of practical use of some forecasting methods in optimization of the procurement, production and distribution processes in foundries are given. The possibilities of using conventional quantitative forecasting methods based on econometric and adaptive models applying the creep trend and harmonic weights are presented. The econometric models were additionally supplemented with the presentation of error estimation methodology, quality assessment and statistical verification of the forecast. The possibility of using qualitative forecasts based on SWOT analysis was also mentioned.
To achieve better precision of features generated using the micro-electrical discharge machining (micro-EDM), there is a necessity to minimize the wear of the tool electrode, because a change in the dimensions of the electrode is reflected directly or indirectly on the feature. This paper presents a novel modeling and analysis approach of the tool wear in micro-EDM using a systematic statistical method exemplifying the influences of capacitance, feed rate and voltage on the tool wear ratio. The association between tool wear ratio and the input factors is comprehended by using main effect plots, interaction effects and regression analysis. A maximum variation of four-fold in the tool wear ratio have been observed which indicated that the tool wear ratio varies significantly over the trials. As the capacitance increases from 1 to 10 nF, the increase in tool wear ratio is by 33%. An increase in voltage as well as capacitance would lead to an increase in the number of charged particles, the number of collisions among them, which further enhances the transfer of the proportion of heat energy to the tool surface. Furthermore, to model the tool wear phenomenon, a egression relationship between tool wear ratio and the process inputs has been developed.
The paper presents the results of the application of a statistical analysis to evaluate the effect of the chemical composition of the die casting Al-Si alloys on its basic mechanical properties. The examinations were performed on the hypoeutectic Al-Si alloy type EN AC-46000 and, created on its basis, a multi-component Al-Si alloy containing high-melting additions Cr, Mo, W and V. The additions were introduced into the base Al-Si alloy in different combinations and amounts (from 0,05% to 0,50%). The tensile strength Rm; the proof stress Rp0,2; the unit elongation A and the hardness HB of the examined Al-Si alloys were determined. The data analysis and the selection of Al-Si alloy samples without the Cr, Mo, W and V additions were presented; a database containing the independent variables (Al-Si alloy's chemical composition) and dependent variables (Rm; Rp0,2; A and HB) for all the considered variants of Al-Si alloy composition was constructed. Additionally, an analysis was made of the effect of the Al-Si alloy's component elements on the obtained mechanical properties, with a special consideration of the high-melting additions Cr, Mo, V and W. For the optimization of the content of these additions in the Al-Si alloy, the dependent variables were standardized and treated jointly. The statistical tools were mainly the multivariate backward stepwise regression and linear correlation analysis and the analysis of variance ANOVA. The statistical analysis showed that the most advantageous effect on the jointly treated mechanical properties is obtained with the amount of the Cr, Mo, V and W additions of 0,05 to 0,10%.