Three-level Full Factorial Design Research Articles

Comparison of response surface model with neural network in determining the surface quality of moulded parts

In this study, response surface (RS) model and an artificial neural network (ANN) are developed to predict surface roughness values error on mold surfaces. In the development of predictive models, cutting parameters of feed, cutting speed, axial–radial depth of cut, and machining tolerance are considered as model variables. For this purpose, a number of machining experiments based on statistical three-level full factorial design of experiments method are carried out in order to collect surface roughness values. An effective fourth order RS model is developed utilizing experimental measurements in the mold cavity. A feed forward neural network based on back-propagation is a multilayered architecture made up of one or more hidden layers (2 layers–42 neurons) placed between the input (1 layer–5 neurons) and output (1 layer-1 neuron) layers. The response surface model and an artificial neural network are compared with manufacturing problems such as computational cost, cutting forces, tool life, dimensional accuracy, etc.

Application of response surface methodology in the optimization of cutting conditions for surface roughness

This paper focuses on the development of an effective methodology to determine the optimum cutting conditions leading to minimum surface roughness in milling of mold surfaces by coupling response surface methodology (RSM) with a developed genetic algorithm (GA). RSM is utilized to create an efficient analytical model for surface roughness in terms of cutting parameters: feed, cutting speed, axial depth of cut, radial depth of cut and machining tolerance. For this purpose, a number of machining experiments based on statistical three-level full factorial design of experiments method are carried out in order to collect surface roughness values. An effective fourth order response surface (RS) model is developed utilizing experimental measurements in the mold cavity. RS model is further interfaced with the GA to optimize the cutting conditions for desired surface roughness. The GA reduces the surface roughness value in the mold cavity from 0.412 μm to 0.375 μm corresponding to about 10% improvement. Optimum cutting condition produced from GA is verified with the experimental measurement.

Computational modeling of cathodic limitations on localized corrosion of wetted SS 316L at room temperature

The ability of a SS 316L surface wetted with a thin electrolyte layer to serve as an effective cathode for an active localized corrosion site was studied computationally. The dependence of the total net cathodic current, I net, supplied at the repassivation potential E rp (of the anodic crevice) on relevant physical parameters including water layer thickness (WL), chloride concentration ([Cl −]) and length of cathode (Lc) were investigated using a three-level, full factorial design. The effects of kinetic parameters including the exchange current density ( i o,c) and Tafel slope ( β c) of oxygen reduction, the anodic passive current density ( i p) (on the cathodic surface), and E rp were studied as well using three-level full factorial designs of [Cl −] and Lc with a fixed WL of 25 μm. The study found that all the three parameters WL, [Cl −] and Lc as well as the interactions of Lc × WL and Lc × [Cl −] had significant impact on I net. A five-factor regression equation was obtained which fits the computation results reasonably well, but demonstrated that interactions are more complicated than can be explained with a simple linear model. Significant effects on I net were found upon varying either i o,c, β c, or E rp, whereas i p in the studied range was found to have little impact. It was observed that I net asymptotically approached maximum values ( I max) when Lc increased to critical minimum values. I max can be used to determine the stability of coupled localized corrosion and the critical Lc provides important information for experimental design and corrosion protection.

Response surface methodology-based approach to CNC drilling operations

Drilling operation is fundamental in the manufacturing industry to drill holes especially in sheet metal parts. This paper presents a mathematical model for correlating the interactions of some drilling control parameters such as speed, feed rate and drill diameter and their effects on some responses such as axial force and torque acting on the cutting tool during drilling by means of response surface methodology. For this exercise, a three-level full factorial design was chosen for experimentation using a PC-based computer numerically controlled drilling machine built in-house. The significance of the mathematical model developed was ascertained using Microsoft Excel ® regression analysis module. The results obtained show that the mathematical model is useful not only for predicting optimum process parameters for achieving the desired quality but for process optimization. Using the optimal combination of these parameters is useful in minimizing the axial force and torque of drilling operations; by extension, other drilling parameters such as cutting pressure, material removal rate, and power could be optimized since they depend on the combination of drilling parameters which affect the axial force and torque.

Warpage optimization of a bus ceiling lamp base using neural network model and genetic algorithm

In this study, optimum values of process parameters in injection molding of a bus ceiling lamp base to achieve minimum warpage are determined. Mold temperature, melt temperature, packing pressure, packing pressure time and cooling time are considered as process parameters. In finding optimum values, advantages of finite element software MoldFlow, statistical design of experiments, artificial neural network and genetic algorithm are exploited. Finite element analyses are conducted for combination of process parameters designed using statistical three-level full factorial experimental design. A predictive model for warpage is created using feed forward artificial neural network exploiting finite element analysis results. Neural network model is validated for predictive capability and then interfaced with an effective genetic algorithm to find the optimum process parameter values. Upon optimization, it is seen that genetic algorithm reduces the warpage of the initial model of the bus ceiling lamp base by 46.5%.

Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm

During the production of thin shell plastic parts by injection molding, warpage depending on the process conditions is often encountered. In this study, efficient minimization of warpage on thin shell plastic parts by integrating finite element (FE) analysis, statistical design of experiment method, response surface methodology (RSM), and genetic algorithm (GA) is investigated. A bus ceiling lamp base is considered as a thin shell plastic part example. To achieve the minimum warpage, optimum process condition parameters are determined. Mold temperature, melt temperature, packing pressure, packing time, and cooling time are considered as process condition parameters. FE analyses are conducted for a combination of process parameters organized using statistical three-level full factorial experimental design. The most important process parameters influencing warpage are determined using FE analysis results based on analysis of variance (ANOVA) method. A predictive response surface model for warpage data is created using RSM. The response surface (RS) model is interfaced with an effective GA to find the optimum process parameter values.

Determination of effecting dimensional parameters on warpage of thin shell plastic parts using integrated response surface method and genetic algorithm

Decreasing warpage is a very significant topic to improve the quality of injection-molded parts. Dimensional stability is an important factor for the minimum warpage of thin shell plastic part. In this study, efficient minimization of warpage on thin shell plastic parts by integrating finite element analysis, statistical design of experiment method, response surface methodology and genetic algorithm is investigated. A thin shell plastic part model is considered as an example. To achieve the minimum warpage, optimum process condition dimensional parameters are determined. X dimension, Y dimension, and Z dimension are considered as model variables. Another parameters of effecting minimum warpage are taken into consideration as constant, such as mold temperature, melt temperature, injection time, injection pressure, etc. Finite element analyses are conducted for combination of process parameters organized using statistical three-level full factorial experimental design. A predictive model for warpage is created using response surface methodology exploiting finite element analysis results. Response surface model is interfaced with an effective genetic algorithm to find the optimum process parameter values.

Application of central composite rotatable design to modelling the effect of some operating variables on the performance of the three-product cyclone

The central composite rotatable design (CCRD) was used to design an experimental program to model the effects of inlet pressure, feed density, and length and diameter of the inner vortex finder on the operational performance of a 150-mm three-product cyclone. The ranges of values of the variables used in the design were: inlet pressure: 80–130 kPa, feed density: 30–60%; length of IVF below the OVF: 50–585 mm; diameter of IVF: 35–50 mm. A total of 30 tests were conducted, which is 51 less than that required for a three-level full factorial design. Because the model allows confident performance prediction by interpolation over the range of data in the database, it was used to construct response surface graphs to describe the effects of the variables on the performance of the three-product cyclone. To obtain a simple and yet a realistic model, it was refitted using only the variable terms that are significant at greater than or equal to 90% confidence level. Considering the selected operating variables, the resultant model is significant and predicts the experimental data well.

Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm

In this study, optimum cutting parameters of Inconel 718 are determined to enable minimum surface roughness under the constraints of roughness and material removal rate. In doing this, advantages of statistical experimental design technique, experimental measurements, artificial neural network and genetic optimization method are exploited in an integrated manner. Cutting experiments are designed based on statistical three-level full factorial experimental design technique. A predictive model for surface roughness is created using a feed forward artificial neural network exploiting experimental data. Neural network model and analytical definition of material removal rate are employed in the construction of optimization problem. The optimization problem was solved by an effective genetic algorithm for variety of constraint limits. Additional experiments have been conducted to compare optimum values and their corresponding roughness and material removal rate values predicted from the genetic algorithm. Generally a good correlation is observed between the predicted optimum and the experimental measurements. The neural network model coupled with genetic algorithm can be effectively utilized to find the best or optimum cutting parameter values for a specific cutting condition in end milling Inconel 718.

Application of multivariate technique in method development for the direct determination of copper in petroleum condensate using graphite furnace atomic absorption spectrometry

A method has been developed for the direct determination of copper in petroleum condensate using detergentless micro-emulsions (three-component solutions) and graphite furnace atomic absorption spectrometry. The optimization was performed using a three-level full factorial design, resulting in pyrolysis and atomization temperatures of 920 °C and 2600 °C, respectively. Using the secondary copper line at 327.4 nm, which is less affected by the magnetic field of the Zeeman-effect background correction system, a characteristic mass of 39 pg, and detection limit of 0.4 μg l−1 was obtained using 20 μl of micro-emulsion, which corresponds to 1.2 μg l−1 in petroleum condensate. The precision, expressed as relative standard deviation, was 2.0% or 2.8% for copper concentrations of 30 μg l−1 and 5.0 μg l−1, respectively. The spike recovery of 105% for copper added to a petroleum condensate sample proved that this procedure could be applied satisfactorily to a determination in this matrix.

Microemulsion electrokinetic chromatography of drugs varying in charge and hydrophobicity Part II: Strategies for optimization of separation.

The separation of anionic, cationic, and neutral drugs in microemulsion electrokinetic chromatography (MEEKC) was studied. The concentration of sodium dodecyl sulfate (SDS; surfactant) and 2-propanol (organic solvent) was varied in a three-level full factorial design. 29 different model substances were chosen with different hydrophobicities and charges (neutral, positive, and negative). The models were calculated by means of multiple linear regression (MLR). The compounds were divided into five different subgroups, and different strategies for optimization of the separation within each group were investigated. The optimization was done by maximizing the selectivity using response surface plots in MODDE, by calculation of different chromatographic functions, and by using the software DryLab. For all the different groups, MODDE, almost all chromatographic functions and DryLab gave approximately the same settings of the factors for optimum separation. Attempts were made to fit descriptors of the compounds to the retention data from the three-level full factorial design by means of partial least squares projection to latent structures (PLS). Between 86 and 89% of all predictions of migration times were acceptable (80-120% of the observed value).

Optimization and validation of a capillary zone electrophoretic method for the simultaneous analysis of four atypical antipsychotics

A capillary zone electrophoretic method has been developed and optimized for separation of four atypical antipsychotics (AAPs): clothiapine (cT), clozapine (cZ), olanzapine (O), and quetiapine (Q). A three-level full-factorial design was applied to study the effect of the pH and molarity of the running buffer on separation. Combination of the studied parameters permitted the separation of the four AAPs, which was best carried out using 80 mM sodium phosphate buffer (pH 3.5). The same system can also be applied for the quantitative determination of these compounds. The method was then validated regarding linearity, precision, and accuracy. Especially, the possibility of simultaneous quantification and identification of the active ingredient in the finished product is very attractive.

Optimisation of steel fibre reinforced concretes by means of statistical response surface method

Abstract The objective of this research is to optimise the fracture parameters of steel fibre reinforced concretes to obtain a more ductile behaviour than that of plain concrete. The effects of the aspect ratio (L/d) and volume fraction of steel fibre (Vf) on fracture properties of concrete in bending were investigated by measuring the fracture energy (GF) and characteristic length (lch). For optimisation, three-level full factorial experimental design and response surface method were used. The results show that the effects of fibre volume fraction and aspect ratio on fracture energy and characteristic length are very significant.

Approaches to the rational design of molecularly imprinted polymers

In our experience the efficient design of molecularly imprinted polymer (MIPs) for novel templates has proved difficult. Following commonly used imprinting protocols, MIPs designed against one template show both a lack of capacity and poor specificity for rebinding either the template or structurally similar analytes. Optimisation methods that involve changing one factor at a time can be laborious. A novel approach for the optimisation of MIPs using chemometrics is described. Sulfonamides, common drug residues in foodstuffs, were used as the model analytes with a methacrylic acid/ethylene glycol dimethacrylate MIP. To avoid the inaccuracies in measurement caused by template bleed a multi-analyte competition rebind assay was developed to select suitable sulfonamides to be used as the template for the MIP, and for the rebind analyte in the chemometric optimisation study. The rebinding efficiencies were monitored by HPLC. The template sulfonamide was selected as sulfamethazine (SMZ), and the rebind analyte as sulfadimethoxine (SDIM). The template:monomer:cross-linker (T:M:X) ratio of the SMZ block MIP was then optimised using a three-level full factorial design to predict a MIP with the highest rebind capacity. On synthesis this was 38.8% for SDIM in a solid phase extraction (SPE) application agreeing with the predication. The factorial design was further utilised to predict an optimum T:M:X ratio for the production of a class specific MIP, capable of binding a range of sulfonamides simultaneously. The predicted optimum T:M:X ratios of (1:10:55) and (1:10:10) were found to be different to commonly used ratios from the MIP literature.

Optimisation by experimental design of a capillary electrophoretic method for the separation of several inhibitors of angiotensin-converting enzyme using alkylsulphonates.

A statistical experimental design was used to optimise a capillary electrophoretic separation method for eight inhibitors of the angiotensin-converting enzyme: enalapril, lisinopril, quinapril, fosinopril, perindopril, ramipril, benazepril, and cilazapril. Because a free solution capillary electrophoresis system did not achieve a complete separation of these eight compounds in one run, the usefulness of alkylsulphonates as ion-pairing agents was investigated. After preliminary investigations to determine the experimental domain and the most important factors, a three-level full-factorial design was applied to study the impact of the pH and the molarity of the ion-pairing agent on the separation. Improved separations were obtained suggesting a favourable effect of ion-pairing interactions between analytes and the additive; however, it remained impossible to separate them all in one run. A combination of two systems was still necessary for the selective identification of these structurally-related substances.

Investigating sensory characteristics and volatile components in boiled scallop aroma using chemometric techniques

A three-level full-factorial design for pH (2, 6.7 and 11.4) and parts (S: mantle muscle, M: adductor muscle and W: mantle and adductor muscle) was applied to investigating the influence of these factors on generating the boiled scallop aroma. Quantitative descriptive analysis, using nine attributes, was used to describe the aroma property of boiled scallop. M and W samples, at pH 6.7, showed higher scores for the characteristics “boiled scallop”, “kamaboko (steamed surimi)” and “sweet”. “Sour” and “irritate” were characteristic of M and W samples at pH 2 and pH 11.4, respectively. Response surfaces clearly showed how pH and parts influenced the generation of each attribute. Partial least squares regression (PLSR) models, calculated using influential GC–MS peaks, were highly predictable. Considering aroma properties of influential volatile components, selection by PLSR is easily interpretable in relation to each attribute.

Sensory characteristics and volatile components in aromas of boiled prawns prepared according to experimental designs

Comparing samples prepared according to a two-level fractional-factorial design, parts of prawns and pH were selected as essential factors for generating boiled prawn aroma. Aroma characteristics in samples prepared based on a three-level full-factorial design for pH (2, 7, and 12) and parts (shell: S, meat: M, and meat with shell: W) were quantitatively described using 10 attributes. “Sweet”, “cooked fish”, “roasted shrimp” and “boiled prawn” scores were higher in M and W samples at pH 7, but “sewage” scores were higher in all S samples. Response surfaces in statistically significant models obtained for seven attributes clearly visualized how parts and pH influenced them. Partial least squares regression models composed of selected influential peaks for seven attributes were highly predictable.

Optimised separation of endogenous urinary components using cyclodextrin-modified micellar electrokinetic capillary chromatography.

In this study both native and chemically modified cyclodextrins (CDs) were investigated as buffer additives to improve the micellar electrokinetic capillary chromatography (MEKC) separation of endogenous bioanalytes in human urine. The following CDs were investigated: alpha, beta, gamma-CDs; hydroxypropyl-alpha-CD, hydroxypropyl-beta-CD, methylated beta-CD, sulphated beta-CD, sulphobutyl ether-beta-CD and hydroxypropyl-gamma-CD. The separations were compared to MEKC without additives. The best improvement in peak resolution and separation of urine components was observed with the sulphated beta-CD. A four-factor three-level full factorial design study was conducted on voltage, temperature, pH and sulphated beta-CD molarity. The optimum conditions were 25 mM sodium tetraborate, pH 9.5, 75 mM sodium dodecyl sulphate (SDS) and 6.25 mM sulphated beta-CD and were able to resolve 70 peaks from a urine pool in 12 min. These optimum conditions have been successfully applied to a number of clinical samples.

Optimization of seed production for a simultaneous saccharification cofermentation biomass-to-ethanol process using recombinantZymomonas

The five-carbon sugar D-xylose is a major component of hemicellulose and accounts for roughly one-third of the carbohydrate content of many lignocellulosic materials. The efficient fermentation of xylose-rich hemicellulose hydrolyzates (prehydrolyzates) represents an opportunity to improve significantly the economics of large-scale fuel ethanol production from lignocellulosic feedstocks. The National Renewable Energy Laboratory (NREL) is currently investigating a simultaneous saccharification and cofermentation (SSCF) process for ethanol production from biomass that uses a dilute-acid pretreatment and a metabolically engineered strain of Zymomonas mobilis that can coferment glucose and xylose. The objective of this study was to establish optimal conditions for cost-effective seed production that are compatible with the SSCF process design.Two-level and three-level full factorial experimental designs were employed to characterize efficiently the growth performance of recombinant Z. mobilis CP4:pZB5 as a function of nutrient level, pH, and acetic acid concentration using a synthetic hardwood hemicellulose hydrolyzate containing 4% (w/v) xylose and 0.8% (w/v) glucose. Fermentations were run batchwise and were pH-controlled at low levels of clarified corn steep liquor (cCSL, 1-2% v/v), which were used as the sole source of nutrients. For the purpose of assessing comparative fermentation performance, seed production was also carried out using a "benchmark" yeast extract-based laboratory medium. Analysis of variance (ANOVA) of experimental results was performed to determine the main effects and possible interactive effects of nutrient (cCSL) level, pH, and acetic acid concentration on the rate of xylose utilization and the extent of cell mass production. Results indicate that the concentration of acetic acid is the most significant limiting factor for the xylose utilization rate and the extent of cell mass production; nutrient level and pH exerted weaker, but statistically significant effects. At pH 6.0, in the absence of acetic acid, the final cell mass concentration was 1.4 g dry cell mass/L (g DCM/L), but decreased to 0.92 and 0.64 g DCM/L in the presence of 0.5 and 1.0% (w/v) acetic acid, respectively. At concentrations of acetic acid of 0.75 (w/v) or lower, fermentation was complete within 1.5 d. In contrast, in the presence of 1.0% (w/v) acetic acid, 25% of the xylose remained after 2 d. At a volumetric supplementation level of 1.5-2.0% (v/v), cCSL proved to be a cost-effective single-source nutritional adjunct that can support growth and fermentation performance at levels comparable to those achieved using the expensive yeast extract-based laboratory reference medium.

Development and Evaluation of a Monolithic Floating Dosage Form for Furosemide

The poor bioavailability of orally dosed furosemide (60%), a weakly acidic drug, is due to the presence of a biological window comprised of the upper gastrointestinal tract. The purpose of the present study was to develop and optimize in vitro a monolithic modified-release dosage form (MMR) for furosemide with increased gastric residence time and to evaluate the in vivo performance of the dosage form. The principle of floatation was used to restrict the MMR to the stomach. A two-factor three-level full factorial experimental design was employed for formulation development. A flow-through cell was designed to evaluate in vitro dissolution parameters. Quadratic regression models indicated the polymer viscosity and polymer:drug ratio to be significant (p < 0.05) formulation factors in determining the duration of buoyancy and the release profile. Statistical optimization using response surface methodology with certain physiological constraints relating to gastric emptying time predicted an optimal MMR. In vivo evaluation of the optimized MMR in beagle dogs resulted in a significant increase (p < 0.05) in the absolute bioavailability for the MMR dosage form (42.9%) as compared to the commercially available tablet (33.4%) and enteric product (29.5%). Significant in vitro/in vivo correlations (p < 0.05) were obtained for the MMR using deconvolution analysis normalized for bioavailability. The floating dosage form was found to be a feasible approach in delivering furosemide to the upper gastrointestinal tract to maximize drug absorption.