Different Designs Of Experiments Research Articles

Experimental design for the development of a multiplex antigen lateral flow immunoassay detecting the Southern African Territory (SAT) serotypes of foot-and-mouth disease virus.

Antigenic lateral flow immunoassays (LFIAs) rely on the non-competitive sandwich format, including a detection (labelled) antibody and a capture antibody immobilised onto the analytical membrane. When the same antibody is used for the capture and the detection (single epitope immunoassay), the saturation of analyte epitopes by the probe compromises the capture and lowers the sensitivity. Hence, several factors, including the amount of the probe, the antibody-to-label ratio, and the contact time between the probe and the analyte before reaching the capture antibody, must be adjusted. We explored different designs of experiments (full-factorial, optimal, sub-optimal models) to optimise a multiplex sandwich-type LFIA for the diagnosis and serotyping of two Southern African Territory (SAT) serotypes of the foot-and-mouth disease virus, and to evaluate the reduction of the number of experiments in the development. Both assays employed single epitope sandwich, so most influencing variables on the sensitivity were studied and individuated. We upgraded a previous device increasing the sensitivity by a factor of two and reached the visual limit of detection of 103.7 and 104.0 (TCID/mL) for SAT 1 and SAT 2, respectively. The positioning of the capture region along the LFIA strip was the most influent variable to increase the detectability. Furthermore, we confirmed that the 13-optimal DoE was the most convenient approach for designing the device.

Optimization of biomass saccharification processes with experimental design tools for2Gethanol production: a review

AbstractThe current energy scenario has encouraged the replacement of carbon sources with renewable sources. Second‐generation (2G) ethanol production from lignocellulosic material or agroindustrial biomass involves a three‐step process consisting of pretreatment, saccharification and fermentation. Enzymatic saccharification is the most critical step for recovering fermentable sugars from biomass. Many factors such as pretreatment, enzyme load, pH, temperature, substrate and surfactant concentrations, and reaction time, among others, can affect biomass saccharification. In this sense, different design of experiment (DoE) tools together with response surface methodology (RSM) have been used as a viable strategy to determine how factors influence saccharification and to optimize this process. The strategy involves fewer experiments, less time and lower cost. This review summarizes how DoE and RSM tools have been used to optimize biomass saccharification. Moreover, an overview of the main experimental design tools used in this field is provided.

Comprehensive Method for Obtaining Multi-Fidelity Surrogate Models for Design Space Approximation: Application to Multi-Dimensional Simulations of Condensation Due to Mixing Streams

In engineering problems, design space approximation using accurate computational models may require conducting a simulation for each explored working point, which is often not feasible in computational terms. For problems with numerous parameters and computationally demanding simulations, the possibility of resorting to multi-fidelity surrogates arises as a means to alleviate the effort by employing a reduced number of high-fidelity and expensive simulations and predicting a much cheaper low-fidelity model. A multi-fidelity approach for design space approximation is therefore proposed, requiring two different designs of experiments to assess the best combination of surrogate models and an intermediate meta-modeled variable. The strategy is applied to the prediction of condensation that occurs when two humid air streams are mixed in a three-way junction, which occurs when using low-pressure exhaust gas recirculation to reduce piston engine emissions. In this particular case, most of the assessed combinations of surrogate and intermediate variables provide a good agreement between observed and predicted values, resulting in the lowest normalized mean absolute error (3.4%) by constructing a polynomial response surface using a multi-fidelity additive scaling variable that calculates the difference between the low-fidelity and high-fidelity predictions of the condensation mass flow rate.

Impact of Pulse Parameters of a DC Power Generator on the Microstructural and Mechanical Properties of Sputtered AlN Film with In-Situ OES Data Analysis.

In the present study, the sputtered aluminum nitride (AlN) films were processed in a reactive pulsed DC magnetron system. We applied a total of 15 different design of experiments (DOEs) on DC pulsed parameters (reverse voltage, pulse frequency, and duty cycle) with Box-Behnken experimental method and response surface method (RSM) to establish a mathematical model by experimental data for interpreting the relationship between independent and response variables. For the characterization of AlN films on the crystal quality, microstructure, thickness, and surface roughness, X-ray diffraction (XRD), atomic force microscopy (AFM), and field emission-scanning electron microscopy (FE-SEM) were utilized. AlN films have different microstructures and surface roughness under different pulse parameters. In addition, in-situ optical emission spectroscopy (OES) was employed to monitor the plasma in real-time, and its data were analyzed by principal component analysis (PCA) for dimensionality reduction and data preprocessing. Through the CatBoost modeling and analysis, we predicted results from XRD in full width at half maximum (FWHM) and SEM in grain size. This investigation identified the optimal pulse parameters for producing high-quality AlN films as a reverse voltage of 50 V, a pulse frequency of 250 kHz, and a duty cycle of 80.6061%. Additionally, a predictive CatBoost model for obtaining film FWHM and grain size was successfully trained.

Accuracy of the robust design analysis for the flux barrier modelling of an interior permanent magnet synchronous motor

Mass-produced electrical machines are subjected to manufacturing uncertainties in terms of geometry. A robust design is inevitable to ensure the consistent performance of the electric motor. Some parts of the rotor geometry are often simplified, like the flux barrier at the end of the magnets. This paper presents a design optimisation regarding the torque ripple and the average torque. The aim is to assess the effects of the flux barrier on the main properties of a permanent magnet synchronous motor. Also, robust design analysis is presented on the flux barrier. The computational burden of the robust design analysis is immense, even if uniform uncertainties are assumed. In this case, different Design of Experiment (DoE) methods reduce the number of simulations. The efficiency of the DoE methods is compared in terms of simulation number and extreme value approximation. We found that the Central Composite method is the most accurate, while the Plackett–Burman is the most efficient in this particular case.

Encapsulation of pomegranate peel extract in sucrose matrix by co-crystallization

Many researchers have turned their attention to the polyphenolic content of pomegranate peels. However, polyphenols are unstable compounds and this is the biggest deterrent to their use in the food industry. The aim of this study was the effective encapsulation of pomegranate peel phenolic extract in the matrix of sucrose by co-crystallization utilizing three different experimental designs, the two level factorial design (F), the Central Composite design (CCP), and the Box-Behnken design (ΒΒ), from which the one with the higher predictive ability was selected. The examined parameters were the temperature (125–140 °C) at which the extract is incorporated into the sucrose matrix, the extract soluble solids concentration extract (30–60 °Brix), as well as the dry extract to sucrose ratio (0.1–0.7 g/g). The formed powders were analyzed for their physicochemical properties (moisture content, hygroscopicity, solubility, bulk density, color, antioxidant activity). Both the individual optimum conditions for maximizing efficiency and the composite optimum conditions for simultaneously maximizing efficiency and minimizing moisture content across all three different designs of experiments were determined. Of the studied designs, the CCP was the one with the better predictive ability as it presented the smallest deviations between experimental and predicted efficiency values in both optimum and randomized experiments.

Automotive Rubber Product Design Using Response Surface Method

In rubber bumper design one of the most important technical properties of the product is the force-displacement curve under a compression load, which is a highly nonlinear behavior because of the large deformation, the rubber material and the contacts. Finite element analysis is a good way to evaluate the working characteristics of the rubber part. Fulfillment of customer needs requires a general iterative design method where the objective can be reached with the modification of the product shape. The determination of the optimum requires numerous iterations of finite element analysis which is computationally expensive. With the integration of the Response Surface Method (RSM) into the design process of a two-variable shape optimization task, the optimal design can be achieved more efficiently. Four different Design of Experiments (DOE) methods were used to intelligently chose design points. As a metamodeling technique, Genetic Aggregation was selected to predict the relation between the sampled geometric variables and the nonlinear objective function value. The Nonlinear Programming by Quadratic Lagrangian (NLPQL) and the Mixed-Integer Sequential Quadratic Programming (MISQP) algorithms with different settings were tested to find the optimum of the response surface. As a result, the most accurate and efficient DOE method and optimization algorithm were determined. The introduced Response Surface Method-based optimization is proved to be suitable to determine the shape of the rubber jounce bumper, which meets the technical requirements.

A review of fused filament fabrication (FFF): Process parameters and their impact on the tribological behavior of polymers (ABS)

The current review is focuses on a comprehensive investigation of the process parameters such as height of layer, orientation, speed of printing, Air gap, fill density, Infill structure, Raster angle, Width of raster, temperature of bed and how they influence the tribological behavior of the ABS specimens fabricated using the Fused Deposition Modelling (FDM), The tribological properties of FDM build parts are highly affected by these process parameters. Due to this, researchers have explained optimum values of these parameters to enhance the tribological behavior. According to the application, for which the part is produced, these process parameters should be chosen cautiously. For a particular demand, some of process parameters are important than rest, so there is need to be identify and optimize these important parameters for these researchers have investigated and employed different Design of Experiments (DOE) which are discussed in this paper. Another way of improving tribological behavior of ABS is the addition of material such as, such as graphite, PTFE, Zirconia, CaCO3, and, carbon fiber etc. The addition of materials to polymers has a significant impact on the wear resistance quality. This not only affect wear but also improve the friction coefficient as compare to the pure polymer. This paper aims at reviewing recent research on improving tribological behavior of ABS parts produced using FDM process.

Effect of annealing temperature and cutting angle of specimens on metallurgical and mechanical properties of Mg-7Li-1Zn alloy via Taguchi approach and response surface analysis

The main aim of this research is to assess the effect of annealing temperature and cutting angle of specimens on the metallurgical and mechanical properties of superlight Mg-Li-Zn alloy thin sheets. To this end, as-rolled LZ71 (Mg-7%Li-1%Zn) alloy was annealed at different temperatures (0, 250, and 350° C) for 3 hours. After that, the test specimens were cut at different angles relative to the rolling direction (0, 45, and 90 °). Next, several experiments were accomplished to study the microhardness, microstructure, grain size, and tensile behavior of the LZ71 alloy. Finally, different Design of Experiment (DOE) techniques were used to determine the individual and interaction effects of annealing temperature and cutting angle of specimens on the material characterization of LZ71 alloy. In this regard, annealing temperature and cutting angle were considered as input parameters in both Taguchi Approach (TA) and Response Surface Analysis (RSA). Also, key parameters of the tensile test including Ultimate Tensile Strength (UTS), Yield Stress (YS), Elastic Modulus (EM), and Strain at BreakPoint (SBP) were considered as outputs. It was concluded that annealing temperature influence on the static behavior of LZ71 alloy is several times greater than the effect of cutting angle (i.e., the effect of annealing temperature on the UTS, YS, EM, and SBP is 92%, 76%, 61%, and 66%, respectively). Moreover, the results of the Response Surface Optimization Algorithm (RSOA) showed that to achieve the best static properties in different directions, the annealing temperature should be selected to be 350°C.

Comparison of design of experiment methods for modeling injection molding experiments using artificial neural networks

Abstract Different design of experiments (DoE) strategies for generation of injection molding process data and later usage as training database, e. g. for an artificial neural network substitute model, are investigated and compared. The objective is to find the most efficient and effective strategies for the modeling. The effects of six injection molding parameters on the length, width and weight of polypropylene plate specimen are simulated with different DoE methods from the following categories: full and fractional factorial designs, central composite designs, orthogonal Taguchi arrays, d -optimal designs and a space-filling design. The prediction performance, e. g. of the artificial neural networks, for unknown test data is evaluated. A 26−3 fractional factorial design including a center point is very efficient for this modeling task, whereas an inscribed central composite design is most effective. The artificial neural network with the latter experimental design as training data achieves a coefficient of determination R ² of 0.930 without a hyperparameter tuning to the specific data set.

Novel Room Temperature Ionic Liquid for Liquid-Phase Microextraction of Cannabidiol from Natural Cosmetics

This study presents the synthesis of a novel asymmetric 1,3-di(alkoxy)imidazolium based room temperature ionic liquid, more precisely 1-butoxy-3-ethoxy-2-ethyl-imidazolium bis(trifluoromethane)sulfonimide, and its application as an extraction solvent in liquid-phase microextraction of cannabidiol from natural cosmetics. Quantification was implemented, using a high performance liquid chromatography system coupled to ultraviolet detection. Molecular structure elucidation was performed by nuclear magnetic resonance spectroscopy. The extraction procedure was optimized by means of two different design of experiments. Additionally, a full validation was executed. The established calibration model, ranging from 0.6 to 6.0 mg g−1, was linear with a coefficient of determination of 0.9993. Accuracy and precision were demonstrated on four consecutive days with a bias within −2.6 to 2.3% and a maximum relative standard deviation value of 2.5%. Recoveries, tested for low and high concentration within the calibration range, were 80%. Stability of extracted cannabidiol was proven for three days at room temperature and fourteen days at 4 °C and −20 °C. An autosampler stability for 24 h was validated. Liquid-phase microextraction of cannabidiol from different formulated cream based cosmetics was performed, including four ointments and four creams. The results show that a significantly higher selectivity could be achieved compared to a conventional extraction methods with methanol.

Regression models for magnetic flux density using DoE techniques and geometric optimization of MR valve

Magnetorheological (MR) fluids have found wide application in engineering devices, especially, in MR dampers. Calculation of magnetic flux density in the MR valve plays a critical role in the performance analysis of MR dampers. In the present study, regression models have been fitted for magnetic flux densities in the active and core regions of the MR valve for a commercially available MR fluid. The fitted model is presented as a function of the MR valve’s geometric parameters and applied current. For fitting the regression model, simulation experiments have been performed in ANSYS-APDL software using different design of experiment (DoE) techniques. For each technique, the model is improved upon by p-value test and the best model among different DoE techniques is selected by analyzing the coefficients of determination. The best-improved models are then used for geometric optimization of MR valve in the MATLAB® environment and the optimal results are compared to the one obtained from the approximated magnetic circuit with constant relative magnetic permeability.

Polymer-Based Liner TSV Fabrication Scheme and Its Resistance Variation

In this paper, a process scheme for polymer TSV fabrication is proposed to solve the difficulty of polymer liner formation in TSV technology. By using ring trench etching and BCB deposition in a vacuum environment, uniform BCB liner can be successfully fabricated. Electrical measurement by using Kelvin structure is completed to ensure the performance of fabrication. However, a new phenomenon causing tiny resistance changes with the arrangement of TSV is discovered during the measurement of TSV chain. In this research, this effect is discussed in detail by designing different design of experiment (DOE) to pursue good electrical connection and reduce the variance of delay property of each TSV. Based on statistical results, impacts of TSV manufacturing process variance are discussed and the guideline in high density TSV design is found.

Exemplified screening standardization of potent antioxidant nutraceuticals by principles of design of experiments

Standardization is an essential step in the development of nutraceutical based formulations or products. The objective of the present study was to exemplify screening standardization of the potent antioxidant nutraceuticals (generic), namely curcumin, quercetin, hesperidin, gallic acid, citric acid, L-ascorbic acid, phytic acid and α-tocopherol based on ferric reducing antioxidant power (FRAP) value and also tried to justify the reliability of two different designs of experiments i.e. Plackett–Burman fold-over design and Taguchi L18 in screening related experiments. First screening of antioxidant nutraceutical was performed by Plackett–Burman fold-over design (resolution IV) and the outcome was analysed based on Pareto chart of standardized effect where the quercetin, gallic acid and curcumin have shown p value of <0.05 and the same factors were found to be positive outliner in half normal probability analysis. Further, the results were validated using factorial Taguchi orthogonal array, based on SN ratio (maximum the best response). Both statistical designs have given the same order of potency based on FRAP value as: quercetin > gallic acid > curcumin. Therefore it can be concluded that the statistical design of experiments is reliable in screening related experiments for development of nutraceutical formulations or products.

Structural Damage Identification Using Response Surface-Based Multi-objective Optimization: A Comparative Study

Non-destructive structural damage identification (SDI) and quantification of damage are important issues for any engineering structure. In this study, a comparative assessment of the damage identification capability of different design of experiment (DOE) methods (such as, 2 k factorial design, central composite design, Box–Behnken design, D-optimal design and Taguchi’s OA design) used in response surface methodology (RSM) has been carried out. Three different structures (simply supported beam, spring mass damper system and fibre reinforced polymer composite bridge deck) have been used for various single and multiple damage conditions to access the comparative ability of the aforementioned methods in identifying damage addressing two critically important criteria: accuracy and computational efficiency. The study reveals that central composite design and D-optimal design are most recommendable among the five considered DOE methods for SDI. Two different input parameter screening methods (sensitivity analysis using RSM utilizing 2 k factorial design and D-optimal design, general sensitivity analysis) have been explored in this study, and their comparative performance is also discussed. It is found that both the methods used in sensitivity analysis for the purpose of input parameter screening in the damage identification process work satisfactorily. Performance of RSM-based damage identification algorithm for different DOE methods under the influence of noise has also been addressed in this paper.

Comparing Diffuse Approximation and Kriging Method for Predicting the Tool Life when Milling Ultrahigh Strength Steel

Tool wear can affect surface quality of the work-piece and frequent tool changing leads to low machining efficiency. Tool life of milling ultrahigh strength steel is thus an important indicator in manufacturing. In this study, we compare Diffuse Approximation (DA) and Kriging method for predicting tool life of milling ultrahigh strength steel. Latin hypercube method is employed as design of experiments after comparing three different designs of experiments (full factorial design, Latin Hypercube Sampling (LHS) and random design). An example with LHS is taken to investigate accuracy of approximation of DA and Kriging method. The results show that the DA provides significant improvement in accuracy of prediction compared with Kriging method. Tool life of milling ultrahigh strength steel predicted by DA is almost close to ‘exact’ values, especial for big mutation of adjacent measured values. The most errors with DA are less than ones with Kriging method.

Characterization of Gas‐Solid Reactions using In Situ Powder X‐ray Diffraction

AbstractX‐ray diffraction is a superior technique for structural characterization of crystalline matter. Here we review the use of in situ powder X‐ray diffraction (PXD) mainly for real‐time studies of solid‐gas reactions, data analysis and the extraction of valuable knowledge of structural, chemical and physical properties. Furthermore, the diffraction data may also provide knowledge on reaction mechanisms, kinetics and thermodynamic properties. Thus, in situ PXD simultaneously provides properties as a function of pressure, temperature and/or time at different length scales, i.e. nanoscale structural data and bulk sample properties. Initially, a brief description of experimental and methodological details is provided, followed by a variety of examples of different designs of experiments and methods of data analysis. Additionally, it is discussed how a range of physical properties can be accessed by diffraction techniques, e.g. crystallite size. The aim of this review is to provide new inspiration for utilization of in situ PXD for characterization of a wide range of properties beyond the scope of crystal structure solution.

BiotecVisions 2013, January

BiotecVisions 2013, January

Statistical vs. Stochastic experimental design: An experimental comparison on the example of protein refolding

Optimization of experimental problems is a challenging task in both engineering and science. In principle, two different design of experiments (DOE) strategies exist: statistical and stochastic methods. Both aim to efficiently and precisely identify optimal solutions inside the problem-specific search space. Here, we evaluate and compare both strategies on the same experimental problem, the optimization of the refolding conditions of the lipase from Thermomyces lanuginosus with 26 variables under study. Protein refolding is one of the main bottlenecks in the process development for recombinant proteins. Despite intensive effort, the prediction of refolding from sequence information alone is still not applicable today. Instead, suitable refolding conditions are typically derived empirically in large screening experiments. Thus, protein refolding should constitute a good performance test for DOE strategies. We compared an iterative stochastic optimization applying a genetic algorithm and a standard statistical design consisting of a D-optimal screening step followed by an optimization via response surface methodology. Our results revealed that only the stochastic optimization was able to identify optimal refolding conditions (~1.400 U g(-1) refolded activity), which were 3.4-fold higher than the standard. Additionally, the stochastic optimization proved quite robust, as three independent optimizations performed similar. In contrast, the statistical DOE resulted in a suboptimal solution and failed to identify comparable activities. Interactions between process variables proved to be pivotal for this optimization. Hence, the linear screening model was not able to identify the most important process variables correctly. Thereby, this study highlighted the limits of the classic two-step statistical DOE.

A multicriteria simulation optimization method for injection molding

Abstract Injection molding is the most important process for mass-producing plastic products. To help improve and facilitate the molding of plastic parts, advanced computer simulation tools have been developed. While modeling is complicated by itself, the difficulty of optimizing the injection molding process is that the performance measures involving the injection molding process usually show conflicting behaviors. Therefore, the best solution for one performance measure is usually not the best in some other performance measures. This paper introduces a simulation optimization method which considers multiple performance measures and is able to find a set of efficient solutions without having to evaluate a large number of simulations. The main components of the method are metamodeling, design of experiments, and data envelopment analysis. The method is illustrated and detailed here using a simple test example, and it is applied to a real injection molding case. The performance of the method using a different design of experiments is also discussed.