Light-weight Self-Compacting Concrete (LWSCC) might be the answer to the increasing construction requirements of slenderer and more heavily reinforced structural elements. However there are limited studies to prove its ability in real construction projects. In conjunction with the traditional methods, artificial intelligent based modeling methods have been applied to simulate the non-linear and complex behavior of concrete in the recent years. Twenty one laboratory experimental investigations on the mechanical properties of LWSCC; published in recent 12 years have been analyzed in this study. The collected information is used to investigate the relationship between compressive strength, elasticity modulus and splitting tensile strength in LWSCC. Analytically proposed model in ANFIS is verified by multi factor linear regression analysis. Comparing the estimated results, ANFIS analysis gives more compatible results and is preferred to estimate the properties of LWSCC.
The purpose of this study is to identify relationships between the values of the fluidity obtained by computer simulation and by an experimental test in the horizontal three-channel mould designed in accordance with the Measurement Systems Analysis. Al-Si alloy was a model material. The factors affecting the fluidity varied in following ranges: Si content 5 wt.% – 12 wt.%, Fe content 0.15 wt.% – 0.3wt. %, the pouring temperature 605°C-830°C, and the pouring speed 100 g · s–1 – 400 g · s–1. The software NovaFlow&Solid was used for simulations. The statistically significant difference between the value of fluidity calculated by the equation and obtained by experiment was not found. This design simplifies the calculation of the capability of the measurement process of the fluidity with full replacement of experiments by calculation, using regression equation.
The research was concerned with the influence of chemical composition of austenitic steels on their mechanical properties. Resulting properties of castings from austenitic steels are significantly influenced by the solidification time that affects the size of the primary grain as well as the layout of elements within the dendrite and its parts with regard to the last solidification points in the interdendritic melt. During solidification an intensive segregation of all admixtures occurs in the melt, which causes a whole range of serious metallurgical defects and it has also a significant influence on subsequent precipitation of carbides and intermetallic phases. Chemical heterogeneity then affects the structure and mechanical properties of the casting. In a planned experiment, we cast melted steels containing 18 to 28 % Cr and 8 to 28 % Ni with variable carbon and nitrogen contents. Testing the tensile strength of the cast specimens we could determine the Rp0.2, Rm, and A5 values. The dependence of the mechanical properties on the chemical content was described by regression equations. The planned experiment results allow us to control the chemical content for the given austenitic steel quality to achieve the required values of the mechanical properties.
During the machining processes, heat gets generated as a result of plastic deformation of metal and friction along the tool–chip and tool–work piece interface. In materials having high thermal conductivity, like aluminium alloys, large amount of this heat is absorbed by the work piece. This results in the rise in the temperature of the work piece, which may lead to dimensional inaccuracies, surface damage and deformation. So, it is needed to control rise in the temperature of the work piece. This paper focuses on the measurement, analysis and prediction of work piece temperature rise during the dry end milling operation of Al 6063. The control factors used for experimentation were number of flutes, spindle speed, depth of cut and feed rate. The Taguchi method was employed for the planning of experimentation and L18 orthogonal array was selected. The temperature rise of the work piece was measured with the help of K-type thermocouple embedded in the work piece. Signal to noise (S/N) ratio analysis was carried out using the lower-the-better quality characteristics. Depth of cut was identified as the most significant factor affecting the work piece temperature rise, followed by spindle speed. Analysis of variance (ANOVA) was employed to find out the significant parameters affecting the work piece temperature rise. ANOVA results were found to be in line with the S/N ratio analysis. Regression analysis was used for developing empirical equation of temperature rise. The temperature rise of the work piece was calculated using the regression equation and was found to be in good agreement with the measured values. Finally, confirmation tests were carried out to verify the results obtained. From the confirmation test it was found that the Taguchi method is an effective method to determine optimised parameters for minimization of work piece temperature.
Electroencephalogram (EEG) is one of biomedical signals measured during all-night polysomnography to diagnose sleep disorders, including sleep apnoea. Usually two central EEG channels (C3-A2 and C4- A1) are recorded, but typically only one of them are used. The purpose of this work was to compare discriminative features characterizing normal breathing, as well as obstructive and central sleep apnoeas derived from these central EEG channels. The same methodology of feature extraction and selection was applied separately for the both synchronous signals. The features were extracted by combined discrete wavelet and Hilbert transforms. Afterwards, the statistical indexes were calculated and the features were selected using the analysis of variance and multivariate regression. According to the obtained results, there is a partial difference in information contained in the EEG signals carried by C3-A2 and C4-A1 EEG channels, so data from the both channels should be preferably used together for automatic sleep apnoea detection and differentiation.
In the dissertation the data modeling has been shown for the data that regards the damages, which value is above zero. With the use of Weibull distribution, with prior regression and correlation analysis chosen parameters that defines the life time and failure level of two populations of AlSi17Cu5 were defined. The calculation sheet of reliability allows to create so called survival diagram, and on the basis of durability data the average warrantee can be determined, on the pre-exploitation period.
Geometric deviations of free-form surfaces are attributed to many phenomena that occur during machining, both systematic (deterministic) and random in character. Measurements of free-form surfaces are performed with the use of numerically controlled CMMs on the basis of a CAD model, which results in obtaining coordinates of discrete measurement points. The spatial coordinates assigned at each measurement point include both a deterministic component and a random component at different proportions. The deterministic component of deviations is in fact the systematic component of processing errors, which is repetitive in nature. A CAD representation of deterministic geometric deviations might constitute the basis for completing a number of tasks connected with measurement and processing of free-form surfaces. The paper presents the results of testing a methodology of determining CAD models by estimating deterministic geometric deviations. The research was performed on simulated deviations superimposed on the CAD model of a nominal surface. Regression analysis, an iterative procedure, spatial statistics methods, and NURBS modelling were used for establishing the model.
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.
Electrical Discharge Machining (EDM) process with copper tool electrode is used to investigate the machining characteristics of AISI D2 tool steel material. The multi-wall carbon nanotube is mixed with dielectric fluids and its end characteristics like surface roughness, fractal dimension and metal removal rate (MRR) are analysed. In this EDM process, regression model is developed to predict surface roughness. The collection of experimental data is by using L9 Orthogonal Array. This study investigates the optimization of EDM machining parameters for AISI D2 Tool steel using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Analysis of variance (ANOVA) and F-test are used to check the validity of the regression model and to determine the significant parameter affecting the surface roughness. Atomic Force Microscope (AFM) is used to capture the machined image at micro size and using spectroscopy software the surface roughness and fractal dimensions are analysed. Later, the parameters are optimized using MINITAB 15 software, and regression equation is compared with the actual measurements of machining process parameters. The developed mathematical model is further coupled with Genetic Algorithm (GA) to determine the optimum conditions leading to the minimum surface roughness value of the workpiece.