Suitable Experimental Design Research Articles

Optimization of Grinding Process Through Design of Experiment (DOE)—A Comparative Study

ABSTRACT Grinding is one of the most important processes for producing discrete components with high precision, and it accounts for about 20% to 30% of the total expenditure on machining operations in industrialized nations. So, it becomes imperative that the grinding process with its parameters must be understood properly to have good control over the quality, productivity, and cost aspects of the process. Many experimental investigations reveal that depth of cut, wheel speed, and work speed are the major influential parameters that affect the quality of the ground part. Determination of optimum parameters lies in the proper selection and introduction of suitable design of experiments (DOEs) at the earliest stage of the process and product development cycles. This paper compares and contrasts factorial design with Taguchi's design of experiments used in the determination of optimum grinding conditions. Optimum grinding conditions and grinding cycle time were estimated for each DOE and results were compared and analyzed. ANOVA analysis was carried out for the interpretation and for obtaining insight into the process as a whole.

Halton and Hammersley sequences in multivariate nonparametric regression

The present paper generalizes results by Rafajłowicz and Schwabe [2003. Equidistributed designs in nonparametric regression. Statist. Sinica 13, 129–142] for quasi least squares estimators in additive regression to a general multivariate regression setup. Equidistributed sequences of Halton or Hammersley type provide consistent regression estimators with a satisfactory rate of convergence. As those sequences are easy to construct they can serve as suitable experimental designs. Optimal generators for the Halton and Hammersley sequences are found by exhaustive search.

Quantifying Bioactivity on a Large Scale: Quality Assurance and Analysis of Multiparametric Ultra-HTS Data

There is a growing need to precisely quantify the selectivity of large compound sets in high throughput screening, directing investment in lead optimization towards compounds with a high chance of success. High-content, high-density screening technologies such as multiparametric ultra-HTS provide a basis for highly precise screening with unprecedented scope for delineating process artifacts from reliable signals. However, the full potential of these technologies can only be realized with suitable experimental design and sophisticated data analysis tools. We present two advanced analysis workflows demonstrating how multiparametric readouts from a high throughput primary screen can improve decision quality in the hit identification process. The first involves discrete thresholding and the application of multiple selection criteria. The second uses machine learning algorithms and allows an unbiased consideration of all measured parameters.

Experimental Design and Analysis of Antibody Microarrays: Applying Methods from cDNA Arrays

Protein expression microarrays, also called antibody arrays, represent a new technology that allows the expression level of proteins to be assessed directly. As is also the case with gene expression microarrays, it is hoped that protein expression microarrays will aid in biomarker discovery, predicting disease outcomes and response to treatments, and detecting molecular mechanisms and/or pathways associated with a particular disease state. However, accurately achieving these aims is dependent upon suitable experimental designs, normalization procedures that eliminate systematic bias, and appropriate statistical analyses to assess differential expression or expose expression patterns. In the last five years, a large amount of research has been devoted to two-color cDNA arrays to improve experimental design, normalization and statistical analyses to assess differential expression and classification. These methods are directly applicable to two-color antibody arrays. The objective of this article is to discuss statistical methods that have been developed for cDNA arrays and describe how the methods can be directly applied to antibody arrays.

Formulation of oil‐in‐water submicron emulsions in the dermatological field using experimental design

AbstractThe formation of oil‐in‐water submicron emulsions stabilized by non‐ionic surfactants and containing a variety of dermatologically acceptable oils, has been studied with the help of suitable experimental design. ‘Rechtschaffner experimental design’ was used to evaluate the joint influence of several formulation variables. This approach consists in reducing the number of experiments necessary and in using the most informative formulations. With this end in view only 16 experiments are necessary to obtain an estimation of the main effects of five factors and their first‐order interaction effects. Consequently, the strategy used in this study allowed us to determine the influence of several factors with a minimum of experiments and this design is extremely useful in preliminary studies where the aim is to identify variables that can be fixed or eliminated in further investigation.© 2003 Society of Chemical Industry

Artificial neural networks for modeling electrophoretic mobilities of inorganic cations and organic cationic oximes used as antidote contra nerve paralytic chemical weapons.

Electrophoretic mobility of various analytes can be modeled and thus also predicted using artificial neural networks (ANNs) evaluating experiments done according to a suitable experimental design. In contrast to response surfaces modeling which can be used to predict optimal separation conditions, ANNs combined with experimental design were shown to be efficient for modeling and prediction of optimal separation conditions, while no explicit model and any knowledge of the physicochemical constants is needed. Methodology has been developed and demonstrated on separation of inorganic cations and organic oximes while various additives (methanol, complexation agent), pH or buffer concentration were followed. In our approach proposed the number of experiments necessary to find optimal separation conditions can be reduced significantly.

Use of artificial neural networks for the evaluation of electrochemical signals of adenine and cytosine in mixtures interfered with hydrogen evolution

A new method for the simultaneous determination of adenine and cytosine is described. Multivariate calibration based on a suitable experimental design (ED) and soft modeling with artificial neural networks (ANNs) is used for quantitative analysis of overlapped linear scan voltammetric (LSV) and differential pulse polarographic (DPP) peaks of adenine and cytosine that occur in the region of hydrogen evolution. It is demonstrated that analysis of mixtures, even if some of the constituents undergo an irreversible reduction, can be quantified with reasonable accuracy. The average absolute error was estimated as 3.7% in LSV, 4.6% in DPP for adenine and 5.2% in LSV, 5.9% in DPP for cytosine. For the whole testing set the comparison of the added and found values of adenine and cytosine concentrations was characterized by an agreement factor (about 0.06). The method is quite general and can be used for analysis of other biologically important substances without their separation.

Determination of butyltin compounds by high-performance liquid chromatography-hydride generation-electrothermal atomization atomic absorption spectrometry

An ultrasensitive method for the determination of butyltin compounds has been developed coupling liquid chromatography with electrothermal atomization atomic absorption spectrometry by means of a hydride generation-in situ trapping system. The eluent coming out from the HPLC column was mixed in a tee piece assembly with hydrochloric acid and sodium tetrahydroborate; the reacting solution was carried through the mixing coil to the gas–liquid separator and the generated hydrides were stripped from the solution, introduced into the graphite atomizer through the quartz tube and collected on a Pd-treated platform. The quartz tube was then removed from the injection hole of the graphite tube and the atomic absorbance measured. For each considered tin species (inorganic tin, monobutyltin, dibutyltin, tributyltin) hydride generation conditions were studied in a multivariate way by a suitable experimental design. The resulting analytical performance of the HG-in situ trapping-ETAAS set-up was adequate for the determination of organotin species in environmental samples and therefore it was used as a metal-specific detector for HPLC analysis of this class of compounds. Detection limits of 0.84, 1.66 and 20.4 ng were obtained for mono-, di- and tributyltin, respectively. The system has been used for the determination of butyltin species in certified reference materials of marine samples such as mussel tissues and sediments.

OPT scheduling performances: A case study

A deep insight into Optimized Production Technology (OPT) is sought for by analysing a case study regarding a leading producer of high quality acetate yarns for the textile market. The case study highlights the importance of setting proper values for OPT management parameters in order to achieve desired performances in terms of both system efficiency as well as system effectiveness. A suitable experimental design and a statistical analysis framework allowed the recognition of the impact of single OPT management parameters. Furthermore, the optimal set-points of these parameters were determined in order to cope with resource constraints in a critical manufacturing department. Scheduling performances achievable as output of 'computeraided' scheduling with OPT were confronted with traditional 'man-made' scheduling on the basis of due date performances and setup-related parameters. The results achieved allowed to avoid problems due to a scheduling procedure that underutilized OPT scheduling characteristics.

Suitable experimental design for determination of auxin polar transport in space using a spacecraft.

It is necessary to establish a suitable experimental design for the determination of auxin (indole-3-acetic acid: IAA) polar transport in space using a spacecraft in concerning with the role of gravity. Problems in space experiments are as follows: I) Selection of suitable plant species; II) Preservation of integrity of plant segments for activities of auxin polar transport; III) Stop of auxin polar transport of the segments after the transport experiment in space. Segments of etiolated pea epicotyls and etiolated maize coleoptiles showed relatively high activities of auxin polar transport among dicotyledonous and monocotyledonous plants tested, respectively. The activities decreased dramatically when the segments were pre-stored at 25 degrees C only for 1 day. On the other hand, the storage at low temperature (5 degrees C) in the presence of antioxidants or chelating agents, especially EGTA, maintained relatively high activities of auxin polar transport in pea epicotyl segments. Low temperature (5 degrees C) substantially inhibited the activity of auxin polar transport. Based on the results in this study, a suitable experimental design for the space experiment of auxin polar transport using a spacecraft is also proposed.

Determination of manganese by graphite furnace atomic absorption spectrometry: matrix effect control by multiple linear regression model

The multivariate interference effects due to nitrates of sodium, potassium, magnesium and calcium on electrothermal atomization of manganese were modelled by using the multiple linear regression method in conjunction with a suitable experimental design. Since the model proved to be able to efficiently predict the simultaneous effects of the considered salts in a wide range of concentrations, it was applied to provide a computational correction of the matrix effects occurring in the ETAAS analysis of manganese in seawater, after preconcentration of the analyte on Chelex-100 resin and elution with nitric acid. Preliminary results showed that the matrix effects are significantly reduced, leading to an improvement in accuracy of the ETAAS analysis.

Computer simulation of polymers

A review with 42 references. Computer simulation techniques such as Monte Carlo methods, molecular mechanics and molecular dynamics are reviewed. These methods are usually used to model polymeric systems at atomic scales. Using such methods would thus provide detailed information that aids in the suitable design of materials or experiments or helps to rationalize macroscopically observed behaviour from a microscopic standpoint. Monte Carlo methods, for example, are used to simulate homopolymers, copolymers and stereoregular polymeric structures. Simulated chains could then be used to study, using various statistical models, sequence distribution in direct relation to the thermodynamic and mechanical properties. Dynamic behaviour of polymers is also studied using molecular mechanics and molecular dynamics techniques. Important information regarding force fields for the interatomic interactions could be inferred from the former, whereas transport phenomena, as an example, could be predicted using the latter. In general, detailed information about the arrangement of molecules and chain segments in relation to the macroscopic behaviour of the materials could be obtained using these simulation techniques.

Different definitions of Δ-correct selection for the indifference zone formulation

The goal of the indifference zone formulation of selection (Bechhofer, 1954) consists of selecting the t best variants out of k variants with a probability of at least 1 − β if the parameter difference between the t ‘good’ variants and the k − t ‘bad’ variants is not less than Δ. A review of generalized selection goals not using this difference condition is presented. Within some general classes of distributions, the suitable experimental designs for all these selection goals are identical. Similar results are described for the problem of selecting the best variant in comparison with a control, or standard.

Estimation of three-phase flow functions in porous media

Several important processes involve the flow of three immiscible fluids through porous media, such as the flow of oil, water and gas in petroleum reservoirs, or water, non-aqueous-phase liquid and air in underground aquifers. Multiphase flow functions (relative permeability and capillary pressure) are important to simulate the flow in such systems. Unfortunately, there are few, if any, reliable estimates of these flow properties from laboratory experiments. One of the main reasons for this is the use of unsupported simplifications in the procedures for determining property estimates from measured data. The authors present a method for simultaneous estimation of three-phase relative permeability and capillary pressure functions from laboratory experiments. The method is not limited by restrictions in the experimental design or assumptions regarding the saturation dependence or shape of the functions to be estimated. The method is demonstrated with simulated experiments. It is shown that with a suitable experimental design, regions of the functions represented by the measured data can be determined accurately.

The relationship between feed composition and the efficiency of protein utilisation in young pigs

The quantification of the relationship between feed composition and the efficiency of protein utilisation is a critical step in any model intended to predict the responses of an animal to its diet as rates of protein and lipid retention. Current descriptions of this relationship propose that the efficiency of protein utilisation (ep) is constant and independent of energy supply (Stranks et al, 1988), that ep is a function of protein intake only (ARC, 1981) or that it is a complicated function of the supply of energy at constant protein intake (Fuller and Crofts, 1977). The objective of the two experiments described here, was to define the form of the relationship between feed composition and ep, by using suitable experimental designs.

Determination of effective diffusion coefficients for gaseous and dissolved organic substances in soil material using a ‘stopped elution’ method with HPLC and GC

SUMMARYNon‐steady‐state diffusion of chemicals in soil can be described by Fick's second law using suitable effective diffusion coefficients. In this study a ‘stopped elution’ method was used to determine experimentally these effective diffusion coefficients for gaseous and dissolved substances in dry and water‐saturated soil. For this purpose a high‐performance liquid chromatography (HPLC) apparatus and a gas chromatograph (GS) were used in combination with soil‐filled columns. The method is based on the observed spreading of a substance moving through a soil column. This broadening is the result of several processes, one of which is diffusion. A suitable experimental design permits the separate measurement of that part of the widening caused by diffusion.The measured effective diffusion coefficients were compared with theoretical values calculated using predictive algorithms from the literature. For liquid‐phase diffusion the maximum deviation between the calculated and the measured values was 34%. The experimental results for gaseous diffusion showed systematic upward deviation from the calculated Coefficients. This might be caused by the diffusion of molecules in the adsorbed state, a phenomenon that had been neglected in the calculations.

The influence of presentation factors on the sensory assessment of beverages

Presentation order and preceding samples are known to influence the perception of sensory attributes. In conventional tasting procedures these effects are normally allowed for by the use of suitable experimental designs for assessment order and by averaging responses over assessors. However, it is also important to know how these effects influence perception. Experiments to evaluate and differentiate these order effects when assessing ciders indicated order effects to be most pronounced between first and subsequent samples, with strong characteristics building up during a session. The effect of a preceding sample appeared greater than later order effects and tended to amplify opposite characteristics.

Screening for possible human carcinogens and mutagens false positives, false negatives: statistical implications

A screening method aimed at identifying potential human carcinogens using either animal cancer bioassays or short-term genotoxic assays has 4 possible results: true positive, true negative, false positive and false negative. Such a categorisation is superficially similar to the results of hypothesis testing in a statistical analysis. In this latter case the false positive rate is determined by the significance level of the test and the false negative rate by the statistical power of the test. Although the two types of categorisation appear somewhat similar, different statistical issues are involved in their interpretation. Statistical methods appropriate for the analysis of the results of a series of assays include the use of Bayes' theorem and multivariate methods such as clustering techniques for the selection of batteries of short-term test capable of a better prediction of potential carcinogens. The conclusions drawn from such studies are dependent upon the estimates of values of sensitivity and specificity used, the choice of statistical method and the nature of the data set. The statistical issues resulting from the analysis of specific genotoxicity epxeriments involve the choice of suitable experimental designs and appropriate analyses together with the relationship of statistical significance to biological importance. The purpose of statistical analysis should increasingly be to estimate and explore effects rather than for formal hypothesis testing.

Analytical potentiometric data analyses of metal–ligand equilibria by microcomputer. A general procedure for minimizing potential sources of errors and a fast linear-scaled least-squares algorithm

Analytical potentiometry is a powerful technique to investigate multiple species in solution and their formation constants. However, there have been some difficulties in this method resulting primarily from the theoretical approach. In this paper we critically examined them and improvements have been implemented successfully. Suitable experimental designs are adapted and preliminary computation of potentiometric data are performed to minimize potential sources of errors. Criteria are proposed to choose the best set of titration curves. A correction is introduced to partially minimize the errors originating from the volume variation. A linear-scaled minimization algorithm has been implemented to extract the value of the complex stability constants from the experimental free ion concentrations. This has two advantages over the earlier non-linear minimization process. The execution time is faster and it facilitates the selection of the best fit of species. A series of 14 programs are introduced to run on a low memory microcomputer.

Statistical Evaluation of Nonisothermal Prediction of Drug Stability

Nonisothermal prediction of drug stability based on direct nonlinear estimation of the shelf-life was compared with the isothermal approach. The reliability of the statistics for the estimates of the shelf-life (the time period required for a drug to degrade to 90% remaining at 25 °C) and activation energy obtained by the two methods was evaluated by the Monte Carlo method of computer simulations. The accuracy and precision of the estimates obtained by the nonisothermal method depended largely on the experimental conditions, such as experimental periods, sampling time, and temperature rise programs. The uncertainty of the estimates was determined mainly by the extents of drug degradation and temperature change achieved during the experiment. The nonisothermal method needed suitable experimental designs and precise assay methods of drug contents to provide reliable parameter estimates.