Analysis Of Variance Approach Research Articles

Fabrication of Cu-Sn/SiC Metal Matrix Composites and Investigation of its Mechanical and Dry Sliding Wear Properties

Requisite for advanced materials with high hardness, high tensile strength and improved wear properties has been drastically increased in recent times. The main objective of this paper is to fabricate unreinforced copper alloy and Cu-Sn/SiC composite with varying wt% of SiC (5, 10, and 15) by liquid metallurgy route and to investigate its microstructure, mechanical properties and dry sliding wear behavior. The hardness and tensile strength of alloy and composites are measured on Vickers micro hardness tester and Universal Testing Machine respectively. Tensile test specimens are further subjected to fractography analysis and the results shows ductile mode of failure for alloy and mixed ductile and brittle mode failure for composites. Based on the test results, it is observed that Cu-Sn/10 wt% of SiC have uniform distribution of particles with optimum mechanical properties. Hence only these 10 wt% SiC composite is considered for dry sliding wear analysis and experiments are conducted using Pin-on-disc tribometer. Analysis of process parameters in three levels such as applied load (15,25,35 N), sliding distance (750,1250,1750 m) and sliding velocity (1,2 and 3 m/s) is done by Taguchi’s Design of Experiments technique based on L₂₇ orthogonal array. From Signal-to-Noise ratio and Analysis of Variance approach, the rank of process parameters as well as its influence on the response is found. The results reveal that the wear rate increases as applied load and sliding distance increases and the wear rate decreases as sliding velocity increases. The load is found to have the highest influence on wear rate (38.5%) followed by sliding distance (7%) and sliding velocity (7%). Interaction between the process parameters also contributed towards wear rate. The worn surfaces were examined using Scanning Electron Microscopy and observed the formation of mechanically mixed layer at high velocity condition. Thus, the fabricated composites can be used in applications like bearings and bushes to improve the wear resistance.

A Statistical Analysis of Wear Behaviour of Fly Ash Reinforced Al-Sic Hybrid Composites

The tribiological behaviour of aluminium hybrid composites reinforced with Silicon carbide (10 wt.%) and Fly ash (FA 0, 5, 10 and 15 wt.%) fabricated using powder metallurgy technique was investigated. Wear tests were performed on pin on disc machine to assess the tribiological behaviour of composites and to determine the optimum volume fraction of fly ash for its minimum wear. Wear loss reduces with increase in fly ash weight fraction and minimum wear rate was attained at 10 wt.% fly ash. The influences of sliding distance, sliding speed and applied load were studied with test based on full factorial design of experiments (DOE). Analysis of variance (ANOVA) was used to evaluate the percentage contribution of each sliding wear process parameters on wear loss. From the ANOVA approach, it was concluded that wear loss is mainly influenced by sliding distance followed by sliding speed and applied load.

Analysis of Repeatability and Reproducibility Standards of ATD Response for the Correlation Method.

Statistical methods, using the entire time-history, can be used to assess the impact response of an ATD (Anthropomorphic Test Device) in terms of its repeatability and reproducibility. In general, the methods generate a correlation relationship described as shape, magnitude and phase-difference between two time-histories' in a given set of similar tests: for repeatability the relationship it is for the same ATD, for reproducibility it is for different ATDs of the same design and for biofidelity it is a relationship between ATDs and biomechanical response data from a series of human surrogate impact tests. The method uses the phase relationship to minimize the difference between any two time-histories through an alignment procedure and the magnitude and shape correlations are used to generate a parametric evaluation of the differences between any two time-histories, or set of time-histories. This paper introduces a variance analysis using the entire time history to build additional foundation to the parametric evaluations using the magnitude and shape correlations and how they can be used to define repeatability and reproducibility ratings/criterion. The proposed methodology has been evaluated using two data sets based on HIII 50th dummy's chest acceleration time histories observed in USNCAP tests. The first set consists of five tests from a single Lab. The second set consists of seven tests from labs different from the first set. A time-history parameter, V, (the normalized summation of squared point to point difference between a pair of signals) was introduced and used to perform statistical analysis of Variance (ANOVA) of the reproducibility of the time histories under investigation. In particular, the V-parameter has been analyzed using both ANOVA and T-test approaches. The relationship between the parameter V and the parameters shape correlation and magnitude correlation is derived analytically. Using this relationship, criterions have been defined for reproducibility and/or repeatability with respect to the shape and magnitude correlations metrics. The criterions have been developed using a limited data set and may change as more data becomes available and is analyzed.

Mechanical Properties and Adhesive Scuffing Wear Behavior of Stir Cast Cu–Sn–Ni/Si3N4 Composites

The modern technology developments have seeded for the necessity of composite materials that are incorporated with high hardness, high tensile strength, and better wear properties. Cu–Sn–Ni alloy as well as the composites of varying weight percentage of Si3N4 (5, 10, and 15) are fabricated by liquid metallurgy technique. The alloy and composites are tested for their tensile strength and hardness on Universal Testing Machine and Vickers microhardness tester, respectively. Based on the tests, Cu–Sn–Ni/10 wt. % of Si3N4 is found to have optimum mechanical properties. The scuff type adhesive wear behavior is studied through pin-on-disk tribometer under dry sliding conditions for Cu–Sn–Ni/10 wt. % of Si3N4 composite. Taguchi's design of experiments technique based on L27 orthogonal array model is used for analyses of process parameters in three levels such as applied load (10, 20, and 30 N), sliding distance (500, 1000, and 1500 m), and sliding velocity (1, 2, and 3 m/s). The parameters are ranked based on the signal-to-noise ratio and the analysis of variance approach. Based on wear results, applied load is found to have highest stature on influencing wear rate followed by sliding distance and sliding velocity. A generalized wear rate equation is obtained based on the linear regression model and its feasibility is checked. Scanning electron microscope (SEM) analyses revealed severe delamination occurred on maximum load condition. The development of this copper composite can have the possibility of replacing aluminum bearings.

Impact of GCM structure uncertainty on hydrological processes in an arid area of China

Abstract Quantifying the uncertainty sources in assessment of climate change impacts on hydrological processes is helpful for local water management decision-making. This paper investigated the impact of the general circulation model (GCM) structural uncertainty on hydrological processes in the Kaidu River Basin. Outputs of 21 GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under two representative concentration pathway (RCP) scenarios (i.e., RCP4.5 and RCP8.5), representing future climate change under uncertainty, were first bias-corrected using four precipitation and three temperature methods and then used to force a well-calibrated hydrological model (the Soil and Water Assessment Tool, SWAT) in the study area. Results show that the precipitation will increase by 3.1%–18% and 7.0%–22.5%, the temperature will increase by 2.0 °C–3.3 °C and 4.2 °C–5.5 °C and the streamflow will change by −26% to 3.4% and −38% to −7% under RCP4.5 and RCP8.5, respectively. Timing of snowmelt will shift forward by approximately 1–2 months for both scenarios. Compared to RCPs and bias correction methods, GCM structural uncertainty contributes most to streamflow uncertainty based on the standard deviation method (55.3%) while it is dominant based on the analysis of variance approach (94.1%).

Dry sliding wear behavior of Al/Gr/SiC hybrid metal matrix composites by Taguchi techniques

In the present study, hybrid composites of Al6061 matrix with 3wt% graphite and varying composition of 3-9wt% SiCp in steps of 3wt% were prepared by liquid metallurgy route. The cast hybrid composites were solution heat treated at temperature of 530°C for 6 hours followed by ageing at a temperature of 175°C for 6 hours. The wear and frictional properties of hybrid metal matrix composites were studied by conducting dry sliding wear test using Pin on Disc machine as per ASTM G-99 standard. The experiments were conducted by adopting the Taguchi technique with an L9 orthogonal array and analysis of variance approach was employed to evaluate the effect of wear parameters such as load, percentage reinforcement and sliding distance on wear rate of hybrid composites. Thesignificant effectoftheseparameters onthewearrate ofhybridcompositeswas studiedin detail andexperimentalresults showed thattheappliedloadhad a most significant effect on thewearrateofthecompositesthan thatofslidingdistanceand reinforcement. The worn surfaces of the samples were examined by using Scanning Electron Microscope (SEM) micrographs to analyze the nature of wear mechanism and to study significant factors on wear rate.

Experimental Study of Glass Fibre Reinforced Polymer Surfaces Machined by Abrasive Water Jet Machining

Glass fibre reinforced polymer composites are being used increasingly for numerous applications. Conventional machining of glass fibre reinforced polymers is difficult because of excessive tool wear, delamination, improper kerf properties, etc. In this paper, an experimental study of influence of abrasive water jet machining process parameters on surface roughness of glass fibre reinforced polymer is presented. Process parameters namely stand-off distance, water pressure, traverse rate and abrasive mass flow rate are considered in the present work. The influence of process parameters on surface roughness is investigated using analysis of variance approach. It is observed that water pressure and abrasive mass flow rate are the most significant factors in influencing surface roughness. A set of optimum process parameters is determined on the basis of analysis of experimental data. The experimental results are successfully verified by comparing with the available literature. A regression model is developed using multi-linear regression approach on the basis of experimental analysis.

Multi Objective Optimization of Multi Wall Carbon Nanotube Based Nanogrinding Wheel Using Grey Relational and Regression Analysis

Carbon nanotube mixed grinding wheel is used in the grinding process to analyze the surface characteristics of AISI D2 tool steel material. Till now no work has been carried out using carbon nanotube based grinding wheel. Carbon nanotube based grinding wheel has excellent thermal conductivity and good mechanical properties which are used to improve the surface finish of the workpiece. In the present study, the multi response optimization of process parameters like surface roughness and metal removal rate of grinding process of single wall carbon nanotube (CNT) in mixed cutting fluids is undertaken using orthogonal array with grey relational analysis. Experiments are performed with designated grinding conditions obtained using the L9 orthogonal array. Based on the results of the grey relational analysis, a set of optimum grinding parameters is obtained. Using the analysis of variance approach the significant machining parameters are found. Empirical model for the prediction of output parameters has been developed using regression analysis and the results are compared empirically, for conditions of with and without CNT grinding wheel in grinding process.

High-order statistics in global sensitivity analysis: Decomposition and model reduction

ANalysis Of VAriance (ANOVA) is a common technique for computing a ranking of the input parameters in terms of their contribution to the output variance. Nevertheless, the variance is not a universal criterion for ranking variables, since non symmetric outputs could require higher order statistics for their description and analysis. In this work, we illustrate how third and fourth-order moments, i.e. skewness and kurtosis, respectively, can be decomposed mimicking the ANOVA approach. It is also shown how this decomposition is correlated to a Polynomial Chaos (PC) expansion leading to a simple strategy to compute each term. New sensitivity indices, based on the contribution to the skewness and kurtosis, are proposed. The outcome of the proposed analysis is depicted by considering several test functions. Moreover, the ranking of the sensitivity indices is shown to vary according to their statistics order. Furthermore, the problem of formulating a truncated polynomial representation of the original function is treated. Both the reduction of the number of dimensions and the reduction of the order of interaction between parameters are considered. In both cases, the impact on the reduction is assessed in terms of statistics, namely the probability density function. Feasibility of the proposed analysis in a real-case is then demonstrated by presenting the sensitivity analysis of the performances of a turbine cascade in an Organic Rankine Cycles (ORCs), in the presence of complex thermodynamic models and multiple sources of uncertainty.

Experimental Investigation and Parameter Optimization of Micro Holes Machining on Ti-6Al-4V Alloy

This paper presents an experimental study on the diametrical overcut and taper obtained during the machining of micro holes by electro-discharge machining (EDM). Many trials were taken by machining a 2.0 mm electrolytic copper rod to 0.50 mm under various conditions to analyse the effect of process parameters by wire electric discharge grinding process (WEDG). The optimum process parameters were determined by Taguchi’s method. Then a set of electrodes were produced with the determined optimum process parameters and were used to machine micro holes on Ti-6Al-4V alloy. The diameters of the holes were measured and the effects of various parameters were analysed for the variation in taper and diametrical overcut. The experimental results were analysed using analysis of variance approach.

A Hybrid One-Way ANOVA Approach for the Robust and Efficient Estimation of Differential Gene Expression with Multiple Patterns.

BackgroundIdentifying genes that are differentially expressed (DE) between two or more conditions with multiple patterns of expression is one of the primary objectives of gene expression data analysis. Several statistical approaches, including one-way analysis of variance (ANOVA), are used to identify DE genes. However, most of these methods provide misleading results for two or more conditions with multiple patterns of expression in the presence of outlying genes. In this paper, an attempt is made to develop a hybrid one-way ANOVA approach that unifies the robustness and efficiency of estimation using the minimum β-divergence method to overcome some problems that arise in the existing robust methods for both small- and large-sample cases with multiple patterns of expression.ResultsThe proposed method relies on a β-weight function, which produces values between 0 and 1. The β-weight function with β = 0.2 is used as a measure of outlier detection. It assigns smaller weights (≥ 0) to outlying expressions and larger weights (≤ 1) to typical expressions. The distribution of the β-weights is used to calculate the cut-off point, which is compared to the observed β-weight of an expression to determine whether that gene expression is an outlier. This weight function plays a key role in unifying the robustness and efficiency of estimation in one-way ANOVA.ConclusionAnalyses of simulated gene expression profiles revealed that all eight methods (ANOVA, SAM, LIMMA, EBarrays, eLNN, KW, robust BetaEB and proposed) perform almost identically for m = 2 conditions in the absence of outliers. However, the robust BetaEB method and the proposed method exhibited considerably better performance than the other six methods in the presence of outliers. In this case, the BetaEB method exhibited slightly better performance than the proposed method for the small-sample cases, but the the proposed method exhibited much better performance than the BetaEB method for both the small- and large-sample cases in the presence of more than 50% outlying genes. The proposed method also exhibited better performance than the other methods for m > 2 conditions with multiple patterns of expression, where the BetaEB was not extended for this condition. Therefore, the proposed approach would be more suitable and reliable on average for the identification of DE genes between two or more conditions with multiple patterns of expression.

A comparison of different synchronized permutation approaches to testing effects in two-level two-factor unbalanced ANOVA designs

Analysis of variance (ANOVA) is used to compare the means of various samples. Parametric ANOVA approaches assume normally distributed error terms within subsamples. Permutation tests like synchronized permutation tests are computationally intensive and distribution free procedures. Hence they overcome the limitations of parametric methods. Unbalanced designs with differing subsample sizes are quite frequent in various disciplines. There is a broad literature about unbalanced designs and parametric testing. For permutation tests this topic received some attention recently. This paper extends the synchronized permutation method to unbalanced two-level ANOVA designs. A simulation study investigates the behavior of different procedures for various types of unbalanced designs. It compares the results to other permutation approaches. The synchronized permutation method yields comparable results to the best performing competing permutation approaches. However the approach is limited to certain kinds of unbalanced designs.

Enhanced properties of magnetorheological fluids: Effect of pressure

Magnetorheological fluids are extensively used in the industrial world to produce dissipative systems in an easily adjustable or even self-adaptive way. Sometimes their intrinsic rheological properties fail to meet system requirements in terms of available forces or yield stress for a given design space. In technical literature, previous works show a dependency of the shear strength of magnetorheological fluids on the internal pressure of the fluid, called squeeze strengthen effect. This work aims at the experimental validation of the behaviour of the magnetorheological fluids in both flow and shear modes under a given compressive state. Two specific ad hoc experimental test rigs are used for the campaign. The systems are designed in order to apply the magnetic field and the pressure at the same time and the tests are carried out following a design of experiment method. The magnetic parts of the system are designed with the help of a magnetic finite element simulation software, then the experiments are performed and the results are collected. The output is analysed through an analysis of variance approach, a statistical procedure that shows the influence of multiple variables on the system outputs. The outcome of the experimental tests confirms the beneficial effect of the pressure in both flow and shear modes, with performances up to three times compared with the datasheet values, where no pressure is considered.

Minimizing Drilling Thrust Force for HFRP Composite by Optimizing Process Parameters using Combination of ANOVA Approach and S/N Ratios Analysis

Drilling hybrid fiber reinforced polymer (HFRP) composite is a novel approach in fiber reinforced polymer (FRP) composite machining studies as this material combining two different fibers in a single matrix that resulted in considerable improvement in mechanical properties and cost saving as compared to conventional fiber composite material. This study presents the development and optimized way of drilling HFRP composite at various drilling parameters such as drill point angle, feed rate and cutting speed by using the full factorial design experiment with the combination of analysis of variance (ANOVA) approach and signal to noise (S/N) ratio analysis. The results identified optimum drilling parameters for drilling the HFRP composite using small drill point angle at low feed rate and medium cutting speed that resulted in lower thrust force.

Evaluation of branding strategies for global versus local brand: the role of concept consistency

This paper aims to examine the moderating role of concept consistency in consumer evaluation of new product branding strategies for global versus local brands in the context of emerging economy India. A series of pretests were conducted prior to main experiment. The paper adopted multivariate analysis of variance approach to test the hypotheses. The results support the role of concept consistency in affecting the relationship between perceived brand globalness and branding strategy. It was evident from results that consumers favour global and local brand with extended brand name in high concept consistency cases and new brand name in low concept consistency situation. The findings imply that brand managers need to identify and measure the relevant types of concept consistency that will affect evaluation of branding strategies for global and local brands before introducing a new product to the market. Limitations and future research directions are discussed.

Model updating of a dynamic system in a high-temperature environment based on a hierarchical method

In the past, structural model updating methods have been widely researched as a means to reconcile mathematical models because the key parameters in dynamic systems often change during their life cycle due to repair and replacement of parts or the environment. However, there is very little literature that references model updating under the condition of complicated multi-physics fields such as a high temperature environment (HTE). In this paper, model updating in an HTE is proposed based on the hierarchical method, and an iterative procedure is presented. With this method, the temperature field updating of a structure is taken as the first stage, and the multi-objective optimization method and a surrogate model of radial-basis functions are introduced to improve the robustness and efficiency of this stage, respectively. Then, the temperature distribution achieved from the former is imposed on the structure as a thermal load, and the residuals of the modal characteristics are taken as objective functions to update the dynamic model of the structure in HTE. The proposed method is tested on an FE model of a wing model, and the analysis of variance approach is employed to evaluate the parameter variability significance and to select the updating variable. The results of the study show that the developed hierarchical method is capable of identifying the input parameters of the temperature field and structure with good accuracy.

Estimation and inference in sets of Weibull samples

Menon’s method of estimation of the Weibull parameters is extended to the analysis of multiple samples of a common size. A methodology is developed for testing the equality of shape parameters among the populations from which the samples are drawn. When the shape parameters are judged to be equal, point and interval estimates of the common shape parameter are constructed. An analysis of variance approach is shown to be valid for testing the equality of scale parameters when a common shape parameter is hypothesized. A post hoc multiple comparison test is developed for assessing which scale parameters differ. Confidence intervals on a percentile may be set for any of the sample populations using the estimated common shape parameter. Tables of the percentage points of the distributions needed for testing hypotheses and constructing interval estimates were obtained by Monte Carlo sampling and are given for a useful range of sample sizes and number of samples. The result is a comprehensive, easily computed, method of analyzing uncensored sets of Weibull data such as occurs when the flexural strength of brittle materials is observed under different conditions of formulation, finishing or test environment. An example analysis of data of this type is included.

Gist processing in free recall and recognition: Latent variable modelling of children's and adults' true and false memories

The present study was conducted to assess an important prediction of the fuzzy trace theory that both true and false memories should increase with gist processing, but that this positive correlation should be stronger when free recall is used as a memory measure than when recognition is used. Children from Grades 3 to 5 and college students learned two sets of six Deese-Roediger-McDermott (DRM) lists and after a set they received a free recall test or a yes/no recognition test. Instead of employing the traditional analysis of variance approach, a latent class analysis technique was used to investigate the relationship between gist processing and true and false free recall and recognition. The results showed that gist trace processing was positively related to true memories, and to false memories. Moreover, for true memories, this positive relationship was stronger for free recall than for recognition.

A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation

Stochastic model updating must be considered for quantifying uncertainties inherently existing in real-world engineering structures. By this means the statistical properties, instead of deterministic values, of structural parameters can be sought indicating the parameter variability. However, the implementation of stochastic model updating is much more complicated than that of deterministic methods particularly in the aspects of theoretical complexity and low computational efficiency. This study attempts to propose a simple and cost-efficient method by decomposing a stochastic updating process into a series of deterministic ones with the aid of response surface models and Monte Carlo simulation. The response surface models are used as surrogates for original FE models in the interest of programming simplification, fast response computation and easy inverse optimization. Monte Carlo simulation is adopted for generating samples from the assumed or measured probability distributions of responses. Each sample corresponds to an individual deterministic inverse process predicting the deterministic values of parameters. Then the parameter means and variances can be statistically estimated based on all the parameter predictions by running all the samples. Meanwhile, the analysis of variance approach is employed for the evaluation of parameter variability significance. The proposed method has been demonstrated firstly on a numerical beam and then a set of nominally identical steel plates tested in the laboratory. It is found that compared with the existing stochastic model updating methods, the proposed method presents similar accuracy while its primary merits consist in its simple implementation and cost efficiency in response computation and inverse optimization.

Searching for Alternative Splicing With a Joint Model on Probe Measurability and Expression Intensities

The exon tiling array offers a high throughput technology to search for aberrant splicing in biomedical research, but few methods of analysis for splicing detection have been tested both statistically and empirically. Noisy measurements on nonresponsive probe selection regions and outlying intensities at some of the samples tend to distort model-based assessments. We propose a robust analysis of variance approach that incorporates an informative model on probe measurability and uses median regression rank scores for better reliability in alternative splicing detection. We study the validity and effectiveness of our proposed approach in contrast with some of the existing methods through an empirical investigation of a brain cancer experiment, where a set of biologically validated genes for splicing and nonsplicing are available. Our study demonstrates favorable performance of the proposed ranking method, but shows that analysis of statistical significance cannot be trusted from any conventional use of p-values. We warn of any routine attempt to interpret p-values and their derivatives in model-based detection of alternative splicing.