Independent Input Parameters Research Articles

Compressive strength of tungsten mine waste- and metakaolin-based geopolymers

Neuro-fuzzy approach has been successfully applied to a wide range of civil engineering problems so far. However, this is limited for geopolymeric specimens. In the present study, compressive strength of different types of geopolymers has been modeled by adaptive neuro-fuzzy interfacial systems (ANFIS). The model was constructed by 395 experimental data collected from the literature and divided into 80% and 20% for training and testing phases, respectively. Curing time, Ca(OH)2 content, NaOH concentration, mold type, aluminosilicate source and H2O/Na2O molar ratio were independent input parameters in the proposed model. Absolute fraction of variance, absolute percentage error and root mean square error of 0.94, 11.52 and 14.48, respectively in training phase and 0.92, 15.89 and 23.69, respectively in testing phase of the model were achieved showing the relatively high accuracy of the proposed ANFIS model. By the obtained results, a comparative study was performed to show the interaction of some selected factors on the compressive strength of the considered geopolymers. The discussions findings were in accordance to the experimental studies and those results presented in the literature.

A new predictive model for calculating the hardness of metal matrix nanocomposites produced by mechanical alloying

Abstract

Predicting Charpy impact energy of Al6061/SiC p laminated nanocomposites in crack divider and crack arrester forms

Charpy impact energy of the produced Al6061–SiCp laminated nanocomposites by mechanical alloying was modeled by adaptive neuro-fuzzy interfacial systems (ANFIS) in both crack divider and crack arrester configurations. The model was constructed by training, validating and testing of 171 gathered input–target data. The thickness of layers, the number of layers, the adhesive type, the crack tip configuration and the content of SiC nanoparticles were five independent input parameters utilized for modeling. The output parameter was Charpy impact energy of the nanocomposites. The performance of the proposed models was evaluated by absolute fraction of variance, the absolute percentage error and the root mean square error and the best values of 0.9945, 3.521 and 8.224, respectively acquired for them. The results introduced ANFIS as an influential tool for predicting the Charpy impact energy of the considered Al6061–SiCp laminate nanocomposites. & 2013 Elsevier Ltd and Techna Group S.r.l. All rights reserved.

Statistical Modeling and Optimization of Process Parameters in Electro-Discharge Machining of Cobalt-Bonded Tungsten Carbide Composite (WC/6%Co)

In this paper, attempts have been made to model and optimize process parameters in Electro-Discharge Machining (EDM) of tungsten carbide-cobalt composite (Iso grade: K10) using cylindrical copper tool electrodes in planing machining mode based on statistical techniques. Four independent input parameters, viz., discharge current (A: Amp), pulse-on time (B: μs), duty cycle (C: %), and gap voltage (D: Volt) were selected to assess the EDM process performance in terms of material removal rate (MRR: mm3/min), tool wear rate (TWR: mm3/min), and average surface roughness (Ra: μm). Response surface methodology (RSM), employing a rotatable central composite design scheme, has been used to plan and analyze the experiments. For each process response, a suitable second order regression equation was obtained applying analysis of variance (ANOVA) and student t-test procedure to check modeling goodness of fit and select proper forms of influentially significant process variables (main, two-way interaction, and pure quadratic terms) within 90% of confidence interval (p-value≤0.1) It has been mainly revealed that all the responses are affected by the rate and extent of discharge energy but in a controversial manner. The MRR increases by selecting both higher discharge current and duty cycle which means providing greater amounts of discharge energy inside gap region. The TWR can be diminished applying longer pulse on-times with lower current intensities while smoother work surfaces are attainable with small pulse durations while allotting relatively higher levels to discharge currents to assure more effective discharges as well as better plasma flushing efficiency. Having established the process response models, a multi-objective optimization technique based on the use of desirability function (DF) concept has been applied to the response regression equations to simultaneously find a set of optimal input parameters yielding the highest accessible MRR along with the lowest possible TWR and Ra within the process inputs domain. The obtained predicted optimal results were also verified experimentally and the values of confirmation errors were computed, all found to be satisfactory, being less than 10%. The outcomes of present research prove the feasibility and effectiveness of adopted approach as it can provide a useful platform to model and multi-criteria optimize MRR, Ra, and TWR during EDMing WC/6%Co material.

EFFECT OF ADDITIONAL FACTORS ON DIMENSIONAL ACCURACY AND SURFACE FINISH OF TURNED PARTS

This article reports the experimental and analytical results of an investigation of additional factors that affect the dimensional accuracy and surface finish of turned parts besides the three major cutting parameters—cutting speed, feed rate, and depth of cut. The selected additional factors were cooling method, blank size, and work material. A three-level, three-parameter experiment was planned using design-of-experiment methodology. The three levels of independent input parameters were: for cooling method—dry turning, flood turning, and minimum quantity lubrication turning; for blank size—φ20, 40, and 60 mm; and for work material—aluminium 6061, mild steel 1030, and alloy steel 4340. The measured output parameters were the two most widely used dimensional accuracy characteristics of turned parts—diameter error and circularity—and the surface finish characteristic arithmetic average. The results were analyzed applying three methods: traditional analysis, Pareto ANOVA, and Taguchi method. The results reveal that, while work material has the greatest effect on diameter error and surface roughness, the major contributor to circularity is blank size.

Application of the Classification and Regression Trees for Modeling the Laser Output Power of a Copper Bromide Vapor Laser

This study examines the available experiment data for a copper bromide vapor laser (CuBr laser), emitting in the visible spectrum at 2 wavelengths—510.6 and 578.2 nm. Laser output power is estimated based on 10 independent input parameters. The CART method is used to build a binary regression tree of solutions with respect to output power. In the case of a linear model, an approximation of 98% has been achieved and 99% for the model of interactions between predictors up to the the second order with an relative error under 5%. The resulting CART tree takes into account which input quantities influence the formation of classification groups and in what manner. This makes it possible to estimate which ones are significant from an engineering point of view for the development and operation of the considered type of lasers, thus assisting in the design and improvement of laser technology.

Predicting compressive strength of different geopolymers by artificial neural networks

In the present study, six different models based on artificial neural networks have been developed to predict the compressive strength of different types of geopolymers. The differences between the models were in the number of neurons in hidden layers and in the method of finalizing the models. Seven independent input parameters that cover the curing time, Ca(OH)2 content, the amount of superplasticizer, NaOH concentration, mold type, geopolymer type and H2O/Na2O molar ratio were considered. For each set of these input variables, the compressive strength of geopolymers was obtained. A total number of 399 input-target pairs were collected from the literature, randomly divided into 279, 60 and 60 data and were trained, validated and tested, respectively. The best performance model was obtained through a network with two hidden layers and absolute fraction of variance of 0.9916, the absolute percentage error of 2.2102 and the root mean square error of 1.4867 in training phase. Additionally, the entire trained, validated and tested network showed a strong potential for predicting the compressive strength of geopolymers with a reasonable performance in the considered range.

Modeling Charpy impact behavior of Al6061/SiCp laminated nanocomposites by genetic programming

In this work, 4 different gene expression programming models were conducted to predict Charpy impact energy of Al6061-SiCP laminated nanocomposites produced by mechanical alloying. The differences between the models were in their number of genes, head size and chromosomes as well as their linking function. To build the models, 171 pair input-target data were gathered from the literature, randomly divided into 133 and 38 data sets and then were respectively trained and tested by the proposed models. The thickness of layers, the number of layers, the adhesive type, the crack tip configuration, the content of SiC nanoparticles and the test trial number were 6 independent input parameters. The output parameter was Charpy impact energy of the laminated nanocomposites. Although the entire models proposed high performance outcomes, the best performance model had the absolute fraction of variance, the mean absolute percentage error and the root mean square error of 0.9826, 10.217 and 12.432, respectively. All of the training and testing results in the models showed an appropriate performance for predicting Charpy impact energy of Al6061/SiCp laminated nanocomposites in the considered range.

Effect of Cutting Fluid Supply Strategies on Surface Finish of Turned Parts

This paper presents the experimental and analytical results of different cutting fluid supply strategies—dry, minimum quantity lubrication (MQL) and flood turning in terms of the surface finish of turned parts. Subsequently, the influence of independent input parameters on surface finish is investigated in order to optimize their effects. Three techniques—traditional analysis, Pareto ANOVA analysis, and the Taguchi method—are employed. Initially mild steel AISI 1030 has been selected as the work material. The results indicate that the cutting fluid supply strategy has insignificant influence on the surface finish of turned parts. However, the amount of cutting fluid in MQL showed some influence. Further research on two additional materials, aluminum 6061 and alloy steel AISI 4340, reveals that the surface roughness for different work materials is influenced differently by the cutting fluid supply strategies and there is a scope for optimizing the cutting fluid supply strategy in terms of both method and the amount of cutting fluid. This will reduce the amount of cutting fluids used and consequently, their negative impact on the environment, by avoiding unnecessary applications.

Instant Center Based Kinematic Formulation for Planar Wheeled Platforms

For a general J wheeled mobile platform capable of up to three-degrees-of-freedom planar motion, there are up to two J independent input parameters yet the output of the platform is completely represented by three independent variables. This leads to an input parameter resolution problem based on operational criteria, which are in development just as they have been developed for n input manipulator systems. To resolve these inputs into a meaningful decision structure means that all motions at the wheel attachment points must have clear physical meaning. To this effect, we propose a methodology for kinematic modeling of multiwheeled mobile platforms using instant centers to efficiently describe the motion of all system points up to the nth order using a generalized algebraic formulation. This is achieved by using a series of instant centers (velocity, acceleration, jerk, and jerk derivative), where each point in the system has a motion property with its magnitude proportional to the radial distance of the point from the associated instant center and at a constant angle relative to that radius. The method of instant center provides a straightforward and physically intuitive way to synthesize a general order planar motion of mobile platforms. It is shown that a general order motion property of any point on a rigid body follows two properties, namely, directionality and proportionality, with respect to the corresponding instant center. The formulation presents a concise expression for a general order motion property of a general point on the rigid body with the magnitude and direction separated and identified. The results are summarized for up to the fifth order motion in the summary table. Based on the initial formulation, we propose the development of operational criteria using higher order properties to efficiently synthesize the motion of a J wheeled mobile platform.

Formulating the effects of applied temperature and pressure of hot pressing process on the mechanical properties of polypropylene–hydroxyapatite bio-composites by response surface methodology

Mechanical properties of polypropylene–hydroxyapatite (PP–HA) bio-composites produced by hot press molding depend on different parameters, particularly the pressure and temperature of the hot pressing process. In this study, a mathematical models for the effects of the pressure and temperature of the hot pressing process on the mechanical properties of the polypropylene–hydroxyapatite composites is developed using a response surface methodology. Ultimate tensile strength, Young’s modulus and impact absorbed energy were target parameters of the formulas and the temperature and pressure of the hot pressing process were independent input parameters. Formulas were derived by a nonlinear regression analysis and then were refined at the end. The validity of the formulas was also verified by experimental data. It was found that the obtained results by the formulas are matched closely to the experimental results; and the formulas have adequate precision in the ranges of the experimental data. The maximum error that occurs for the calculated results by the formulas is about 7%. The effectiveness of the pressure and temperature of hot pressing process on the mechanical properties of the composites was investigated using sensitivity analyses of the formulas. It was found that the sensitivity of the mechanical properties with respect to pressure of hot pressing process is more than temperature. Furthermore, the ultimate tensile strength of the composites has most sensitivity respect to the pressure and temperature of the hot pressing process than other mechanical properties.

SemileptonicΛb,cto nucleon transitions in full QCD at light cone

The tree level semileptonic $\Lambda_{b}\to pl\nu$ and $\Lambda_{c}\to nl\nu$ transitions are investigated using the light cone QCD sum rules approach in full theory. The spin--1/2, $\Lambda_{Q}$ baryon with $Q=b$ or $c$, is considered by the most general form of its interpolating current. The time ordering product of the initial and transition currents is expanded in terms of the nucleon distribution amplitudes with different twists. Considering two sets of independent input parameters entering to the nucleon wave functions, namely, QCD sum rules and Lattice QCD parameters, the related form factors and their heavy quark effective theory limits are calculated and compared with the existing predictions of other approaches. It is shown that our results satisfy the heavy quark symmetry relations for lattice input parameters and b case exactly and the maximum violation is for charm case and QCD sum rules input parameters. The obtained form factors are used to compute the transition rates both in full theory and heavy quark effective theory. A comparison of the results on decay rate of $\Lambda_{b}\to pl\nu$ with those predicted by other phenomenological methods or the same method in heavy quark effective theory with different interpolating current and distribution amplitudes of the $\Lambda_{b}$ is also presented.

A method for approximating acoustic-field-amplitude uncertainty caused by environmental uncertainties

In underwater acoustics, the accuracy of computational field predictions is commonly limited by uncertainty in environmental parameters. An approximate technique for determining the probability density function (PDF) of computed field amplitude, A, from known environmental uncertainties is presented here. The technique can be applied to several, N, uncertain parameters simultaneously, requires N+1 field calculations, and can be used with any acoustic field model. The technique implicitly assumes independent input parameters and is based on finding the optimum spatial shift between field calculations completed at two different values of each uncertain parameter. This shift information is used to convert uncertain-environmental-parameter distributions into PDF(A). The technique's accuracy is good when the shifted fields match well. Its accuracy is evaluated in range-independent underwater sound channels via an L(1) error-norm defined between approximate and numerically converged results for PDF(A). In 50-m- and 100-m-deep sound channels with 0.5% uncertainty in depth (N=1) at frequencies between 100 and 800 Hz, and for ranges from 1 to 8 km, 95% of the approximate field-amplitude distributions generated L(1) values less than 0.52 using only two field calculations. Obtaining comparable accuracy from traditional methods requires of order 10 field calculations and up to 10(N) when N>1.

Development of empirical model for different process parameters during rotary electrical discharge machining of copper–steel (EN-8) system

The present work investigates the machining characteristics of EN-8 steel with copper as a tool electrode during rotary electrical discharge machining process. The empirical models for prediction of output parameters have been developed using linear regression analysis by applying logarithmic data transformation of non-linear equation. Three independent input parameters of the model viz. peak current, pulse on time and rotational speed of tool electrode are chosen as variables for evaluating the output parameters such as metal removal rate (MRR), electrode wear ratio (EWR) and surface roughness (SR). Analysis of the results, by using Taguchi's recommended signal–noise ratio formulae and ANOVA, has been conducted to identify the significant parameters and their degree of contribution in the process output. Analyzed results shows that peak current and pulse on time are the most significant and significant parameters for MRR and EWR, respectively. But peak current and electrode rotation become the most significant and significant parameters for SR, respectively. Experimental results further revealed that maximizing the MRR while minimizing EWR and improving the surface roughness, cannot be achieved simultaneously at a particular combination of control parameters setting. In addition, the predictions based on the above developed models have been verified with another set of experiments and are found to be in good agreement with the experimental results.

Prediction of Surface Roughness Using Regression and ANN Models in High-Speed Finish Milling Operation

In this paper, multi-linear regression and artificial neural network (ANN) models have been developed to predict surface roughness in high-speed milling of 7050-T7451 aluminum alloy. Surface roughness is taken as the response variable, while cutting speed, feed per tooth, radial depth of cut and slenderness ratio are taken as independent input parameters. An orthogonal experiment design is developed to conduct experiments. The measured values of surface roughness are used to find the regression coefficients and train the neural network for prediction of surface roughness. Predicted values of surface roughness by both models are compared with the measured values.

Probabilistic analysis of peri‐implant strain predictions as influenced by uncertainties in bone properties and occlusal forces

The purpose of this study was to examine the influence of variability in bone properties and loading on peri-implant crestal and cancellous bone strains using a probabilistic approach, in combination with finite element (FE) analysis. Oblique occlusal loading was applied to a single endosseous implant embedded in a two-dimensional (2-D) FE model of a premolar section of a mandible. Perfect bonding was assumed at all interfaces. Five independent parameters (cortical bone thickness (T), cortical (ECORT) and cancellous (ECANC) bone Young's moduli, and vertical (FVERT) and horizontal (FHOR) occlusal forces) were assigned statistical distributions based on data in the literature. Two cancellous bone distribution models were examined, one with a lower mean and range (LM) and the second with a higher mean and range (HM) of cancellous bone Young's modulus values. Sets of randomly chosen values for the five parameters were selected from the distributions and FE analyses were performed for all randomly selected sets. In the LM model, 50% of the cases experienced hyper-physiologic peri-implant crestal strains in the region of commonly reported saucerization, compared with about 25% of the cases in the HM model. Relative probabilistic sensitivities (%) of bone strains to the independent input parameters (T, ECORT, ECANC, FVERT and FHOR) were as follows: 29, 11, 30, 13, and 17 for the LM model, and 17, 11, 35, 21, and 15 for the HM model, respectively. Probabilistic analyses of FE models suggest that up to twice as many cases in the LM distribution may be at risk of saucerization as compared with the HM distribution model. Although based on hypothetical distribution values and the limitations inherent to a 2-D analysis, this probabilistic study demonstrated that FE models are very sensitive to the often arbitrarily assigned values for cancellous bone Young's modulus, and also to values used for cortical bone thickness, when the cancellous bone modulus is low.

Measurement of Mueller matrix for an optical fiber system with birefringence and polarization-dependent loss or gain

In an optical fiber system with both birefringence and polarization-dependent loss or gain (PDL/G), a set of input polarization states and their corresponding outputs are deduced to fulfill some general relationships in Stokes space, by considering the fact that the Mueller matrix of such an optical fiber system meets Lorentz transformation. Then, it can be proven that the minimum number of input polarization states is three for an explicit determination of such a Mueller matrix and no independent input parameter is redundant. Based on the theoretical findings, a general and simple approach is proposed to measure the Mueller matrix. The requirements regarding the choices of three inputs are presented for achieving optimum measurement stability and accuracy. Experimental results on an optical fiber system, formed by two 10-km long single-mode fibers with an optical isolator in between, show excellent agreement with the theory.

A synthetic time-delay circuit in mammalian cells and mice

Time-delay circuitries in which a transcription factor processes independent input parameters can modulate NF-kappaB activation, manage quorum-sensing cross-talk, and control the circadian clock. We have constructed a synthetic mammalian gene network that processes four different input signals to control either immediate or time-delayed transcription of specific target genes. BirA-mediated ligation of biotin to a biotinylation signal-containing VP16 transactivation domain triggers heterodimerization of chimeric VP16 to a streptavidin-linked tetracycline repressor (TetR). At increasing biotin concentrations up to 20 nM, TetR-specific promoters are gradually activated (off to on, input signal 1), are maximally induced at concentrations between 20 nM and 10 microM, and are adjustably shut off at biotin levels exceeding 10 microM (on to off, input signal 2). These specific expression characteristics with a discrete biotin concentration window emulate a biotin-triggered bandpass filter. Removal of biotin from the culture environment (input signal 3) results in time-delayed transgene expression until the intracellular biotinylated VP16 pool is degraded. Because the TetR component of the chimeric transactivator retains its tetracycline responsiveness, addition of this antibiotic (input signal 4) overrides biotin control and immediately shuts off target gene expression. Biotin-responsive immediate, bandpass filter, and time-delay transcription characteristics were predicted by a computational model and have been validated in standard cultivation settings or biopharmaceutical manufacturing scenarios using trangenic CHO-K1 cell derivatives and have been confirmed in mice. Synthetic gene circuitries provide insight into structure-function correlations of native signaling networks and foster advances in gene therapy and biopharmaceutical manufacturing.

TORSIONAL STRENGTH OF STEEL FIBER REINFORCED CONCRETE

The addition of steel fibers in reinforced concrete improves the toughness and the tensile strength of the obtained steel fiber reinforced concrete (SFRC). The latter property enhances the shear strength and the torque moment capacity of SFRC which mainly depend on the tensile strength. The current study is targeted towards developing an expression that is based on the thin walled tube (space truss) analogy adopted by American concrete Institute (ACI). In the proposed expression, the effective tensile strength of concrete in pure torsion in addition to parameters that are in accordance with thin walled tube (space truss) analogy are introduced as independent input parameters. Literature survey is carried out to collect experimental data pertinent to the torsional strength of SFRC beams. Based on regression analysis a new mathematical relationship that predicts the torsional moment for fiber reinforced concrete is derived.

Volumetric characteristics of lava flows from interferometric radar and multispectral satellite data: the 1995 Fernandina and 1998 Cerro Azul eruptions in the western Gal�pagos

We have used a suite of remotely sensed data, numerical lava flow modeling, and field observations to determine quantitative characteristics of the 1995 Fernandina and 1998 Cerro Azul eruptions in the western Galapagos Islands. Flank lava flow areas, volumes, instantaneous effusion rates, and average effusion rates were all determined for these two eruptions, for which only limited syn-eruptive field observations are available. Using data from SPOT, TOPSAR, ERS-1, and ERS-2, we determined that the 1995 Fernandina flow covers a subaerial area of 6.5×106 m2 and has a subaerial dense rock equivalent (DRE) volume of 42×106 m3. Field observations, ATSR satellite data, and the FLOWGO numerical model allow us to determine that the effusion rate declined exponentially from a high of ~60–200 m3 s-1 during the first few hours to <5 m3 s-1 prior to ceasing after 73 days, with a mean effusion rate of 4–16 m3 s-1. Integrating the ATSR-derived, exponentially declining effusion rate over the eruption duration produces a total (subaerial + submarine) DRE volume of between 27 and 100×106 m3, the range in values being due to differing assumptions about heat loss characteristics; only values in the higher part of this range are consistent with the independently derived subaerial volume. Using SPOT, TOPSAR, ERS-1, and ERS-2 data, we determine that the 1998 Cerro Azul flow is 16 km long, covers 16 km2, and has a DRE volume of 54×106 m3. FLOWGO produces at-vent velocity and effusion rate values of 11 m s-1 and ~600 m3 s-1, respectively. The velocity value agrees well with the 12 m s-1 estimated in the field. The mean effusion rate (total DRE volume/duration) was 7–47 m3 s-1. Dike dimensions, fissure lengths, and pressure gradients along the conduit based on magma chamber depth estimates of 3–5 km produce mean effusion rates for the two eruptions that range over nearly four orders of magnitude, the range being due to uncertainty in the magma viscosity, dike dimensions, and pressure gradient between magma chamber and vent. Although somewhat consistent with mean effusion rates from other techniques, their wide range makes them less useful. The exponentially declining effusion rates during both eruptions are consistent with release of elastic strain being the driving mechanism of the eruptions. Our results provide independent input parameters for previously published theoretical relationships between magma chamber pressurization and eruption rates that constrain chamber volumes and increases in volume prior to eruption, as well as time constants of exponential decay during the eruption. The results and theoretical relationships combine to indicate that at both volcanoes probably 25–30% of the volumetric increase in the magma chamber erupted as lava onto the surface. In both eruptions the lava flow volumes are less than 1% of the magma chamber volume.