Interaction Effects Of Parameters Research Articles

An experimental investigation on Nd:YAG laser microchanneling on polymethyl methacrylate submerged in water

In this research work, an experimental investigation has been carried out on underwater laser microchanneling on polymethyl methacrylate, using a pulsed Nd:YAG laser system. The underwater laser processing has been used to minimize the heat-affected zone, microcracking and burr formation around the microchannel. The process parameters that have been taken into consideration are lamp current, pulse frequency, pulse width and cutting speed. The microchannel characteristics that have been taken into account as responses are microchannel width, microchannel depth, burr width and burr height. Response surface methodology has been used to develop the mathematical relationships between the process parameters and microchannel characteristics. The adequacies of the models have been tested using analysis of variance. The interaction effects of process parameters on the microchannel characteristics have been analyzed and discussed. In addition to that, single-objective and multiobjective optimization of the process parameters have been performed to obtain desired values of microchannel characteristics.

공정변수간의 교호작용을 고려한 모서리 접합두께 및 처짐량 예측 회귀식 도출

The thickness and deflection of melting parts of the glass edge reach the biggest effect on the intensity and thermal insulation performance. During the sealing process using a hydrogen mixed gas torch, the thickness and the deflection effect of the edge part are affected by process parameters. In order to analyze the correlative relationship of the thickness prediction and the deflection of the edge part according to the process parameters, data was obtained by conducting sealing experiments. The main effects and interaction effects of process parameters for the thickness and the shape of the glass edge parts were analyzed through the design of experiment. A mathematical experiment equation that can predict the thickness and deflection of the edge part according to the process parameters was developed by conducting multiple regression equations.

Application of response surface methodology for predicting springback in air bending of electro galvanised steel sheets

Springback in bending affects the geometry of the finished part and it must be predicted in advance to compensate, thus maintaining required tolerances in the finished part. Response surface methodology (RSM)-based models are more useful in industrial applications. Though the researches on application of RSM for other bending processes are available, it is limited in air bending and the attempts are only for uncoated steel sheets. This paper describes the method of developing a prediction model employing RSM and central composite design of experiments that establishes a relationship between springback and the parameters such as strain hardening exponent, coating thickness, die opening, die radius, punch radius, punch travel, and punch velocity in air bending of electro galvanised steel sheet. The results obtained from the model are in good accord with the experimental. Besides, the direct and interaction effect of parameters on springback have been studied based on the developed model.

Multivariate Data Analysis for Parameters Effect on CO2 Removal Efficiency

In this paper, both the main effects and interaction effects of parameters on CO2 removal efficiency were investigated. Flue gas stream data from a 500MW coal power plant has been used for the model development. The complete removal process is implemented in Aspen Plus with selected operating conditions and parameters using Monoethanolamine as solvent. The base case model is developed in Aspen Plus with specific parameter values to achieve 85% removal efficiency. The CO2 removal efficiency variation with different parameters; such as number of stages, inlet solvent flow rate, lean loading, temperature of the flue gas and solvent stream, absorber packing height and diameter and absorber pressure are considered as the most important parameters for sensitivity analyses. The data collected from simulations were analysed using Principal Component Analysis, Principal Component Regression and Partial Least Square-regression. The correlation between variables were studied, which indicate that inlet solvent flow rate, absorber packing height and diameter, absorber pressure and temperature of the solvent stream are positively correlated with CO2 removal efficiency whereas the lean loading and temperature of flue gas are negatively correlated with efficiency.

Compression creep behaviour of jute-polypropylene blended needle-punched nonwoven

This paper discusses the compression creep behaviour of jute-polypropylene blended needle-punched nonwoven. It is essential to know the compression creep behaviour of the needle-punched fabrics too, because of their various applications like geotextiles and carpets. Needle-punched fabric samples were prepared based on the three factors, three levels Box Behnken design of experiment to study the individual as well as interaction effects of process parameters of fabric weight, needling density and blend proportion of jute and polypropylene fibre blend on fabric thickness and compression creep. This study reveals that the initial thickness of fabric decreases prominently with the increase in needling density at low fabric weight and has hardly any effect at high fabric weight (450 g/m2). However, around 43% increase in fabric thickness occurs at high jute content level (60%). At low fabric weight, the effect of the needling density on compression creep is highly prominent when polypropylene content is more than 50% in the blend. However, its effect is negligible when jute content is higher than 50% in the blend. The creep initially decreases with the increase in fabric weight up to a certain limit and thereafter it increases. The minimum value of compression creep of around 14% is obtained at 375 g/m2 fabric weight with 150 punches/cm2 needling density at the level of 60% jute content.

Modeling and optimization of joint quality for laser transmission joint of thermoplastic using an artificial neural network and a genetic algorithm

A central composite rotatable experimental design(CCRD) is conducted to design experiments for laser transmission joining of thermoplastic-Polycarbonate (PC). The artificial neural network was used to establish the relationships between laser transmission joining process parameters (the laser power, velocity, clamp pressure, scanning number) and joint strength and joint seam width. The developed mathematical models are tested by analysis of variance (ANOVA) method to check their adequacy and the effects of process parameters on the responses and the interaction effects of key process parameters on the quality are analyzed and discussed. Finally, the desirability function coupled with genetic algorithm is used to carry out the optimization of the joint strength and joint width. The results show that the predicted results of the optimization are in good agreement with the experimental results, so this study provides an effective method to enhance the joint quality.

Parametric Evaluation of Design Input Parameters on the Mechanistic-Empirical Pavement Design Guide Predicted Performance

The objective of this study was to conduct a sensitivity analysis to identify the input parameters with the greatest effects on the predicted pavement performance from the Mechanistic-Empirical Pavement Design Guide (MEPDG). Three levels of analysis (low, medium, and high) with five input parameters (traffic level, hot-mix asphalt (HMA) thickness, E*, base course thickness, and subgrade type) were evaluated through a full factorial design. The main influence of each individual input parameter and the combinational interaction effects of the input parameters on the predicted distress response were quantified. It was determined that the traffic level input parameter was the main effect on all predicted pavement distresses in the MEPDG. The second main effect for international roughness index (IRI), fatigue cracking, and total pavement rutting was HMA thickness. For asphalt concrete (AC) rutting, the mixture dynamic modulus ranked second for the main effect followed by HMA thickness. For top-down cracking, it was observed that the base course thickness ranked second for the main effect followed by the HMA dynamic modulus. The influence of base thickness on top-down cracking was not expected but it may be due that the MEPDG adopted a traditional fatigue cracking model to describe this failure mechanism.

Compression behaviour of jute–polypropylene blended needle-punched nonwoven under wet conditions

The subject of this paper is the compression behaviour of jute–polypropylene blended needle-punched nonwoven under wet conditions. It is essential to know the compression behaviour of the needle-punched fabrics under wet conditions because of its various applications like geotextiles and carpets. Needle-punched fabric samples were prepared based on the three-factor three-level Box–Behnken design of experiment to study the individual as well as interaction effects of process parameters of fabric weight, needling density and blend proportion of jute and polypropylene fibre blend on compression properties under wet conditions. Initial thickness, percentage compression, percentage thickness loss and percentage compression resilience are the compression properties considered. The compression properties under wet conditions were compared with their respective dry conditions. This study reveals that most of the wet samples show a slightly higher value of initial thickness, percentage compression and thickness loss than dry samples, irrespective of processing variables, though for compression resilience value it is vice versa. The effect of blend does not have much influence on percentage compression in wet conditions. There is optimum level of needling density (around 250 punches/cm2) and blend proportions (around 75% polypropylene) for minimum percentage thickness loss of wet fabric under higher fabric weight. The compression resilience increases with increase in the content of polypropylene in the blend of polypropylene and jute. Optimum compression properties (highest percentage compression and compression resilience with minimum percentage thickness loss) can be achieved at fabric weight of 450 g/m2 with the combination of around 250 punches/cm2 needling density and around 75% polypropylene content.

Response surface optimization of a dynamic dye adsorption process: a case study of crystal violet adsorption onto NaOH-modified rice husk

The adsorption of crystal violet from aqueous solution by NaOH-modified rice husk was investigated in a laboratory-scale fixed-bed column. A two-level three factor (2(3)) full factorial central composite design with the help of Design Expert Version 7.1.6 (Stat Ease, USA) was used for optimisation of the dynamic dye adsorption process and evaluation of interaction effects of different operating parameters: initial dye concentration (100-200 mg L(-1)), flow rate (10-30 mL min(-1)) and bed height (5-25 cm). A correlation coefficient (R (2)) value of 0.999, model F value of 1,936.59 and its low p value (<0.0001) along with lower value of coefficient of variation (1.38 %) indicated the fitness of the response surface quadratic model developed during the present study. Numerical optimisation applying desirability function was used to identify the optimum conditions for a targeted breakthrough time of 12 h. The optimum conditions were found to be initial solution pH=8.00, initial dye concentration=100 mg L(-1), flow rate=22.88 mL min(-1) and bed height=18.75 cm. A confirmatory experiment was performed to evaluate the accuracy of the optimised procedure. Under the optimised conditions, breakthrough appeared after 12.2 h and the column efficiency was determined as 99 %. The Thomas model showed excellent fit to the dynamic dye adsorption data obtained from the confirmatory experiment. Thereby, it was concluded that the current investigation gives valuable insights for designing and establishing a continuous wastewater treatment plant.

Seasonal Variation of Cumulative CO2 Emission from a Vertisol Under Apricot Orchard in Semi-Arid Southeast Turkey

Understanding the factors affecting the CO2 emission from agricultural practices is crucial for global warming. A study was performed in an apricot orchard field in the experimental farm of the Harran University, Southeast Turkey, to i) quantify weekly and seasonal variations of the CO2 emissions from a Vertisol under apricot orchard; ii) evaluate the difference in CO2 emission between the area under trees and rows; and iii) assess the relationships between the amounts of CO2 emissions and environmental parameters for better use and management of the soils from the view point of carbon balance and flux in a semi-arid environment under drip irrigation. Soil CO2 emission measurements were performed during May 2008 and May 2010, from both under tree crowns (CO2-UC) and between tree rows (CO2-BR), on a weekly basis in southeast Turkey with a semi-arid climate. CO2 emissions were statistically correlated with weather and soil parameters such as air temperature, relative humidity, rainfall, soil water content, and soil temperature at various depths from 5 to 100 cm. The weekly emissions ranged from 82 to 1110 kg CO2 ha−1 week−1 and from 96 to 782 kg CO2 ha−1 week−1 in CO2-UC and CO2-BR, respectively. Increase in CO2 emission in the second year was due to increases in mean air and soil temperatures. The weekly and monthly cumulative CO2 emissions were positively correlated with the air and soil temperatures. Multiple linear regression analysis explained 35% and 83% variations in average weekly and monthly CO2 emissions, by using meteorological data. Including the interaction effects of meteorological parameters in regression equations nearly doubled the variance explained by the regression models. According to stepwise regression analysis, soil and air temperatures were found to have the most significant impact on the temporal variability of the soil CO2 emission.

Research on Surface Topography and Tool Wear in Micro-Turn-Milling of Micro-Screw

Turn-milling technology can replace turning with milling to high speed machining rotationally body parts. So, micro-turn-milling has superiority on the productivity and the quality of workpieces, and is more suitable to machine micro-shaft parts and desirable miniature parts based on the turn-milling technology. Micro-turn-milling can be used for the machining of micro-parts in its hardened state. In this paper, the influence of cutting parameters on surface topography and tool wear is presented in micro-turn-milling of micro-screw. The interaction effect of parameters to surface topography and tool wear is investigated. The results show that micro-turn-milling technology has its peculiar advantages in realizing high-speed cutting and ensuring machining quality.

Adsorption of crystal violet from aqueous solution onto sugarcane bagasse: central composite design for optimization of process variables

The adsorption of crystal violet (CV) from aqueous solution by sugarcane bagasse (SCB), an agro-industrial residue, was investigated in a batch experimental setup. A two level four factor (24) full factorial central composite design (CCD) with the help of Design Expert Version 7.1.6 (Stat-Ease, USA) was used for adsorption process optimization and evaluation of interaction effects of different operating parameters: agitation speed (80–180 rpm), initial solution pH (4.0–8.0), initial dye concentration (100–200 mg L–1), and adsorbent dose (2–5 g L–1). A multiple coefficient of determination (R2) value of 0.98, model F value of 266.36 and its low P-value (&amp;lt;0.0001) along with lower value of coefficient of variation (2.70%) indicated the fitness of the response surface quadratic model developed during the present study. Numerical optimization applying desirability function was used to identify the optimum conditions for maximum removal of CV. The optimum conditions were found to be agitation speed = 165 rpm, initial solution pH = 8.0, initial dye concentration = 200 mg L–1 and adsorbent dose = 2.0 g L–1. A confirmatory experiment was performed to evaluate the accuracy of the optimization procedure and maximum CV removal of 93.21% was achieved under the optimized conditions.

Extraction of natural dye from petals of Flame of forest (Butea monosperma) flower: Process optimization using response surface methodology (RSM)

The uncontrolled discharge of synthetic dyes into the aquatic ecosystem is a global environmental concern due to their negative ecotoxicological effects. Dyes obtained from different natural sources have emerged as an important alternative to synthetic dyes. In this study, natural colorant from the petals of the Flame of forest (Butea monosperma) flower was extracted under different operating conditions such as extraction time (45–120 min), temperature (60–90 °C) and mass of the petals (0.5–2 g) by conventional extraction technique. Response surface methodology (RSM) with the help of Design Expert Version 7.1.6 (STAT-EASE Inc., USA) was used for optimization of the extraction process and evaluation of interaction effects of different operating parameters. The optimum conditions for dye extraction were found to be 153.65 min, 73.53 °C and 1.47 g for extraction time, temperature, and mass of the flower respectively. The efficiency of extraction under these optimum conditions was found to be 8813.67 mg L−1. Further, Fourier Transform Infrared Spectroscopy was used to identify the major chemical groups in the extracted dye.

Optimization of fast disintegration tablets using pullulan as diluent by central composite experimental design

The objective of this work was to apply central composite experimental design to investigate main and interaction effect of formulation parameters in optimizing novel fast disintegration tablets formulation using pullulan as diluents. Face centered central composite experimental design was employed to optimize fast disintegration tablet formulation. The variables studied were concentration of diluents (pullulan, X1), superdisintigrant (sodium starch glycolate, X2), and direct compression aid (spray dried lactose, X3). Tablets were characterized for weight variation, thickness, disintegration time (Y1) and hardness (Y2). Good correlation between the predicted values and experimental data of the optimized formulation methodology in optimizing fast disintegrating tablets using pullulan as a diluent.

Modelling and Prediction of Stainless Steel Clad Bead Geometry Deposited by GMAW Using Regression and Artificial Neural Network Models

To improve the corrosion-resistant properties of carbon steel, usually cladding process is used. It is a process of depositing a thick layer of corrosion-resistant material over carbon steel plate. Most of the engineering applications require high strength and corrosion resistant materials for long-term reliability and performance. Cladding these properties can be achieved with minimum cost. The main problem faced in cladding is the selection of optimum combinations of process parameters for achieving quality clad and hence good clad bead geometry. This paper highlights an experimental study to predict various input process parameters (welding current, welding speed, gun angle, contact tip-to-work distance, andpinch) to getoptimum dilutionin stainless steel cladding of low carbon structural steel plates using Gas Metal Arc Welding (GMAW). Experiments were conducted based on central composite rotatable design with full replication technique, and mathematical models were developed using multiple regression method. The developed models have been checked for adequacy and significance. Using Artificial Neural Network (ANN) the parameters were predicted, and percentage of error was calculated between predicted and actual values. The direct and interaction effects of process parameters on clad bead geometry are presented in graphical form.

Optimization of Dry Sliding Wear Performance of Ceramic Whisker Filled Epoxy Composites Using Taguchi Approach

This study evaluates the influence of independent parameters such as sliding velocity (A), normal load (B), filler content (C), and sliding distance (D) on wear performance of potassium-titanate-whiskers (PTW) reinforced epoxy composites using a statistical approach. The PTW were reinforced in epoxy resin to prepare whisker reinforced composites of different compositions using vacuum-assisted casting technique. Dry sliding wear tests were conducted using a standard pin on disc test setup following a well planned experimental schedule based on Taguchi’s orthogonal arrays. With the signal-to-noise (S/N) ratio and analysis of variance (ANOVA) optimal combination of parameters to minimize the wear rate was determined. It was found that inclusion of PTW has greatly improved the wear resistance property of the composites. Normal load was found to be the most significant factor affecting the wear rate followed by (C), (D), and (A). Interaction effects of various control parameters were less significant on wear rate of composites.

Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters

Abstract. Sensitivity analysis of atmospheric models is necessary to identify the processes that lead to uncertainty in model predictions, to help understand model diversity through comparison of driving processes, and to prioritise research. Assessing the effect of parameter uncertainty in complex models is challenging and often limited by CPU constraints. Here we present a cost-effective application of variance-based sensitivity analysis to quantify the sensitivity of a 3-D global aerosol model to uncertain parameters. A Gaussian process emulator is used to estimate the model output across multi-dimensional parameter space, using information from a small number of model runs at points chosen using a Latin hypercube space-filling design. Gaussian process emulation is a Bayesian approach that uses information from the model runs along with some prior assumptions about the model behaviour to predict model output everywhere in the uncertainty space. We use the Gaussian process emulator to calculate the percentage of expected output variance explained by uncertainty in global aerosol model parameters and their interactions. To demonstrate the technique, we show examples of cloud condensation nuclei (CCN) sensitivity to 8 model parameters in polluted and remote marine environments as a function of altitude. In the polluted environment 95 % of the variance of CCN concentration is described by uncertainty in the 8 parameters (excluding their interaction effects) and is dominated by the uncertainty in the sulphur emissions, which explains 80 % of the variance. However, in the remote region parameter interaction effects become important, accounting for up to 40 % of the total variance. Some parameters are shown to have a negligible individual effect but a substantial interaction effect. Such sensitivities would not be detected in the commonly used single parameter perturbation experiments, which would therefore underpredict total uncertainty. Gaussian process emulation is shown to be an efficient and useful technique for quantifying parameter sensitivity in complex global atmospheric models.

Global optimization of the infrared matrix‐assisted laser desorption electrospray ionization (IR MALDESI) source for mass spectrometry using statistical design of experiments

Design of experiments (DOE) is a systematic and cost‐effective approach to system optimization by which the effects of multiple parameters and parameter interactions on a given response can be measured in few experiments. Herein, we describe the use of statistical DOE to improve a few of the analytical figures of merit of the infrared matrix‐assisted laser desorption electrospray ionization (IR‐MALDESI) source for mass spectrometry. In a typical experiment, bovine cytochrome c was ionized via electrospray, and equine cytochrome c was desorbed and ionized by IR‐MALDESI such that the ratio of equine:bovine was used as a measure of the ionization efficiency of IR‐MALDESI. This response was used to rank the importance of seven source parameters including flow rate, laser fluence, laser repetition rate, ESI emitter to mass spectrometer inlet distance, sample stage height, sample plate voltage, and the sample to mass spectrometer inlet distance. A screening fractional factorial DOE was conducted to designate which of the seven parameters induced the greatest amount of change in the response. These important parameters (flow rate, stage height, sample to mass spectrometer inlet distance, and laser fluence) were then studied at higher resolution using a full factorial DOE to obtain the globally optimized combination of parameter settings. The optimum combination of settings was then compared with our previously determined settings to quantify the degree of improvement in detection limit. The limit of detection for the optimized conditions was approximately 10 attomoles compared with 100 femtomoles for the previous settings, which corresponds to a four orders of magnitude improvement in the detection limit of equine cytochrome c. Copyright © 2011 John Wiley &amp; Sons, Ltd.

Modeling and prediction of transmission laser bonding process between titanium coated glass and PET based on response surface methodology

Laser bonding parameters play a very significant role in determining the quality of laser transmission bonding between PET films and titanium films coated glass sheets. In order to achieve good bond strength and minimal bond width, three key process parameters affecting the bond quality of transmission laser bonding, namely, laser power, bond speed and film thickness were optimized by response surface methodology in this paper. Response surface methodology (RSM) was used to develop mathematical models between the key process parameters and the desired responses and the central composite design (CCD) was utilized to conduct experimental planning. The samples were tested using an electromechanical universal micro-tester in order to determine bond strength. The morphology of the bonded area was observed with an optical microscope. The interaction effect of main process parameters on bond quality was researched. Design Expert analysis indicated that the best laser power, bond speed and film thickness on bond quality were 11.2 W, 4 mm/s and 163 nm, respectively. Finally, the experimental results are consistent with the predicted, which illustrates that the developed mathematical models can predict the responses adequately.

Sobol’ sensitivity analysis of a complex environmental model

Complex environmental models are controlled by a high number of parameters. Accurately estimating the values of all these parameters is almost impossible. Sensitivity analysis (SA) results enable the selection of the parameters to include in a calibration procedure, but can also assist in the identification of the model processes. Additionally, a sensitivity analysis can yield crucial information on the use and meaning of the model parameters. This paper presents a Sobol’ sensitivity analysis for flow simulations by a SWAT model of the river Kleine Nete, with the objective to assess the first order, second order and total sensitivity effects. Confidence intervals for the resulting sensitivity indices are inferred by applying bootstrapping. The results indicate that the curve number value (CN2) is the most important parameter of the model and that no more than 9 parameters (out of 26) are needed to have an adequate representation of the model variability. The convergence of the parameter ranking for total sensitivity effects is relatively fast, which is promising for factor fixing purposes. It is also shown that the Sobol’ sensitivity analysis enhances the understanding of the model, by e.g. pointing out 3 significant pairwise interactions. In general, it can be concluded that the Sobol’ sensitivity analysis can be successfully applied for factor fixing and factor prioritization with respect to the input parameters of a SWAT model, even with a limited number of model evaluations. The analysis also supports the identification of model processes, parameter values and parameter interaction effects.