Papers by Keyword: Regression Analysis

Abstract: The paper investigated the application of gravel as partial economic replacement of granite in concrete production. Concrete was produced using granite/gravel combination in varying percentages of 90/10, 80/20, 70/30, 60/40, 50/50, 40/60, 30/70, 20/80 and 10/90. Concrete made from 100 % granite and 100 % gravel served controls while other constituents of concrete were kept constant. Two different mix ratios of 1:2:4 and 1:3:6 were employed. Sieve analysis was carried out on the aggregates, while slump and compaction factor tests were carried out on fresh concrete. Compressive strength tests were performed on hardened concrete. Specimens were produced using 150 mm cubes for compressive tests. The reliable percentage of granite/gravel combination from compressive strength view point 60/40 with a value of 21.15 N/mm² for mix ratios 1:2:4 and 70/30 with 15.17 N/mm² for 1:3:6 mix ratio at 28 days. Satisfying respectively the 20 N/mm² and 15.17 N/mm² minimum requirement of BS 8110: 1997.There were costs saving of 4 % per unit volume of concrete production for both 1:2:4 and 1:3:6 respectively. Empirical evidence from the regression analysis revealed that higher composition of gravel significantly improves the concrete consistency properties while greater proportions of granite do significantly enhance comprehensive strength.

Abstract: In recent scenario, the automation in weld cladding process has a challenging task for most of the industries like automotive, petrochemical and food processing industries. Because to achieve the best quality of the weld clad bead geometry during automated process it is important to have complete control over the selected process parameters. The cladding process enhances the base material properties. Therefore, it is essential to predict the relationship between the process parameters and response the second order polynomial regression equation has to be developed. It makes more effectiveness of automated weld cladding processes. Palani and Murugan [1] developed a mathematical model for a flux cored arc welding process using an RSM method to study the direct effects and interaction effects of process parameters on clad bead geometry.

Abstract: This paper proposes the prediction of cutting temperature, tool wear and metal removal rate using fuzzy and regression modeling techniques for the hard milling process. The feed per tooth, radial depth of cut, axial depth of cut and cutting speed were used as process state variables.The experiements were conducted using RSM based central composite rotatable design methodology. Regression and fuzzy modeling were used to evaluate the input – output relationship in the process. It is interesting to observe that the R² and average error values for each response are very consistent with small variations were obtained.Also, the confirmation results show that very less relative error varitions. Thus, the developed fuzzy models directly integrated in manufacturing systems to reduce the more computational complexity in the process planning activities.

Abstract: The paper provides analysis of new car sales in Bosnia and Herzegovina for the period 2007-2014. For the examined period new car sales in B&H was reduced by 53.67%, from 12449 in 2007 to 6682 in 2014. The trend can be approximated using 3rd degree polynomial regression model with coefficient of determination R²=0,845. Most new cars sold were by Skoda and least was sold by Porsche. Total number of sold vehicles for this period was 73152. We also present annual growth, chain growth and cumulative growth index for the given period.

Abstract: Wire Electric Discharge Machining (WEDM) is a non-traditional process of material from conductive material to produce parts with intricate shape and profiles. In the present work, an attempt has been made to optimization the machining conditions for maximum material removal rate, minimise kerf width based on (L₉ Orthogonal Array) Taguchi method. Experiments, based on Taguchi’s parameters design, were carried out to effect of machining parameters, like pulse-on-time (T_ON), pulse-off-time (T_OFF), peak current (I_P), and wire feed (W_F) on the material removal rate and kerf width. The importance of the cutting parameters on the cutting performance outputs is determined by using the variance analysis (ANOVA). The variation of MRR and kerf width with cutting parameters is modeled by using a regression analysis method.

Abstract: This paper presents the concept of International Performance Measurement and Verification Protocol (IPMVP) for determining energy saving at whole facility level for an office building in Malaysia. Regression analysis is used to develop baseline model from a set of baseline data which correlates baseline energy with appropriate independents variables, i.e. Cooling Degree Days (CDD) and Number of Working Days (NWD) in this paper. In determining energy savings, the baseline energy is adjusted to the same set condition of reporting period using energy cost avoidance approach. Two types of energy saving analyses have been presented in the case study; 1) Single linear regression for each independent variable, 2) Multiple linear regression for each independent variable. Results show that NWD has coefficient of determination, R² higher than CDD which indicates that NWD has stronger correlation with the energy use than CDD in the building. Finding also shows that the R2 for multiple linear regression model are higher than single linear regression model. This shows the fact that more than one component are affecting the energy use in the building.

Abstract: This paper proposes Artificial Neural Network (ANN) to determine adjusted baseline energy for quantifying energy savings from an energy efficiency program implemented in an office building. The input data to the ANN includes number of working days and cooling degree days (CDD) each month for one year period before implementation of the retrofitting program. On the other hand, output data is baseline energy use (i.e. energy use before retrofit). Since the input data to the network encompasses of 36 months set of data only, Bootstrap method is used to generate more input data without changing the input and output trend of the original data set. This is performed to increase validity of the training process. Once the optimum training parameters have been obtained, adjusted baseline energy is determined by feeding the number of working days and CDDs in the post-retrofit period (i.e. 12 months set of data) to the network. Energy savings is then calculated by comparing the adjusted baseline energy with the energy use after implementing the retrofit program. The performances of the ANN model are then compared with Multi-regression technique in term of R², Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE) and Mean Absolute Deviation (MAD). Results show that the proposed ANN model has smaller errors and R² closer to one compare to Multi-regression technique.

Abstract: In this study, to predict the surface roughness of stainless steel-304 in Magneto rheological Abrasive flow finishing (MRAFF) process, an artificial neural network (ANN) and regression models have been developed. In this models, the parameters such as hydraulic pressure, current to the electromagnet and number of cycles were taken as variables of the model.Taguchi’s technique has been used for designing the experiments in order to observe the different values of surface roughness . A neural network with feed forward with the help of back propagation was made up of 27 input neurons, 7 hidden neurons and one output neuron. The 6 sets of experiments were randomly selected from orthogonal array for training and residuals were used to analyze the performance. To check the validity of regression model and to determine the significant parameter affecting the surface roughness, Analysis of variance (ANOVA) and F-test were made. The numerical analysis depict that the current to the electromagnet was an paramount parameter on surface roughness.Key words: MRAFF, ANN, Regression analysis

Abstract: A multi-factor linear regression model was developed to describe and estimatepilot-controller voice communication loads. The routinely-recorded air traffic control data andassociated voice communication data were collected to statistically analyze the correlation betweencomplexity factors and pilot-controller voice communication loads. Results show that each complexity factor is significantly correlated with voice communication loads. To eliminate multicollinearity among complexity indicators, principle component analysis is performed to extract two principle components from complexity indicators. These variables were used to construct the multivariate regression equations of the communication durations, the communication frequencies, and the integrated voice communication load index. These equations can quantitatively describe and estimate air traffic controller’s voice communication loads.

Abstract: This article introduces the working principle of the recovery fresh air heat pump units,and analyzes the applications system which units apply in air-conditioning heat recovery system.By using the principle of regression analysis of mathematical statistics,the article measured data for statistical analysis.The regression equation is established, the established regression equation has a high prediction accuracy.