Design Of Experiments Results Research Articles

Enhancing the Mechanical Properties of a 6061 Aluminum Alloy by Heat Treatment from the Perspective of Taguchi Design-of-Experiments

This research investigates the heat treatment parameters of 6061-aluminum alloy to enhance its mechanical properties. The Taguchi design-of-experiments (DOE) method was employed to systematically examine the effects of solutionizing temperature, solutionizing time, aging temperature, and aging time on the tensile strength of the alloy. Mechanical testing suggested a major influence of solutionizing and aging temperatures on the ultimate tensile strength of the alloy. The samples subjected to a solutionizing temperature of 540 °C for 3 h, followed by aging at 170 °C for 18 h, exhibited the highest ultimate tensile strength (293.7 MPa). Conversely, the samples processed at the lowest levels of these parameters displayed the lowest ultimate tensile strength (193.7 MPa). Microstructural analysis confirmed the formation of equiaxed grains, strengthening precipitates, precipitate clusters, and β (Mg2Si) precipitates alongside Fe-Al-Si dispersoids. Energy-dispersive X-ray spectroscopy (EDS) analysis detected the presence of elemental precursors of β phase (Al-Mg-Si) and dispersoid-forming elements (Al-Fe-Si). X-ray diffraction spectroscopy (XRD) analysis revealed the persistence of the β phase in the alloy, indicating its contribution to the improved mechanical properties, which are mainly obtained by aging precipitation phases. Fracture analysis showed a ductile fracture mechanism, and examining fractured samples supported the findings of enhanced tensile properties resulting from the adequate selection of heat treatment parameters. We employed ANOVA (analysis of variance) to analyze the DOE results, using a multiple regression model to express the ultimate tensile strength of the alloy in terms of the variables used in the design. This yielded an adjusted coefficient of determination of 89.75%, indicating a high level of explained variability in the test data for evaluating the model’s predictive capacity.

Development of a Versatile Lipid Core for Nanostructured Lipid Carriers (NLCs) Using Design of Experiments (DoE) and Raman Mapping.

The objective of this study was to develop a versatile lipid core for the 'brick-dust type of drugs' (poorly water-soluble and poorly lipid-soluble drugs). In the first step, excipients of different polarities were classified according to their behavior in aqueous solutions. Subsequently, binary mixtures were prepared with cetyl palmitate (Crodamol™ CP pharma, Campinas, São Paulo, Brazil) as the solid lipid, and its miscibility with other excipients was evaluated using Raman mapping and classical least squares (CLS). Based on the results, the excipients Crodamol™ CP pharma (hydrophobic), Super Refined™ DMI (dimethyl isosorbide; hydrophilic, Mill Hall, PA, USA), and Super Refined™ Lauryl Lactate (lauryl lactate, medium polarity, Mill Hall, PA, USA) were chosen to compose the lipid core. The ideal proportion of these excipients was determined using a mixture design and the standard deviation (STD) of image histograms as the response variables. After statistical evaluation of the DoE results, the final composition was determined, and drugs with different logP (0 to 10) and physicochemical characteristics were evaluated in the optimized mixture. The drugs butamben (Sigma-Aldrich Co., Spruce Street, St. Louis, MO, USA), tacrolimus (NutriFarm, São Paulo, Brazil), atorvastatin calcium, and resveratrol (Botica da Terra, Campinas, Brazil) presented a homogeneous distribution in the optimized lipid core, indicating that this is a promising system to be used in nanostructured lipid carrier (NLC) formulations of such types of drugs.

Optimizing vehicle aerodynamics through the synergistic application of Design of Experiments (DoE) and Computational Fluid Dynamics (CFD)

The aerodynamic design of cars not only creates aesthetic and diverse car shapes, but also ensures their safety and fuel economy. According to Mustafa Cakir's research[1], approximately of the fuel consumption of a car traveling at high speeds is used to counteract air resistance. However, with the increasing severity of global warming, emissions standards for automobile exhaust are becoming increasingly stringent. Considering that gasoline vehicles still dominate the household car market, optimizing the aerodynamic design of automobiles is imperative. This work analyzes and optimizes the aerodynamic design of automobiles through the synergistic application of Design of Experiments (DoE) and Computational Fluid Dynamics (CFD). Before conducting experiments, the article first introduces and derives the basic control equations of computational fluid dynamics, namely the continuity equation and Navier-Stokes equation, and describes how they are used in experiments. Then, the article briefly introduces the drag coefficients of automobiles, which are important theoretical basis for subsequent aerodynamic design optimization in this article. In the experimental part, this study focuses on the influence of four important design parameters: angle of the front window , angle of the hood , angle of the back window , and length of the trunk . Using Solidworks 3D modeling software, the article simulates these design parameters and prepares for CFD numerical simulation by geometry, setting computational domain, boundary conditions, and objective functions, and generating mesh. Based on the DoE and numerical simulation results, the article ultimately selects a front window angle of , a hood angle of , a back window of , and as the optimized design. Compared with the basic model, the optimized model reduces the drag coefficient by about . The conclusion of this study provides ideas and theoretical basis for the aerodynamic optimization design of new generation automobiles with the incorporation of DoE. However, the design parameters of automobiles are countless, and in the future, this article will strive to research and simulate more diverse automobile designs and analyze their aerodynamic performance to explore more optimized designs.

Numerical investigation of the use of flexible blades for vertical axis wind turbines

This study proposes a novel vertical axis wind turbine (VAWT) design with flexible blades aiming to improve their energy extraction capability. The blade deformation is achieved using Ansys Fluent dynamic mesh and user-defined functions to control the position of the blades nodes at specific azimuthal angles. An optimization study is carried out using computational Fluid Dynamics (CFD) simulations and Design of Experiments (DOE) to get the optimal design parameters of the flexible blade at different Tip Speed Ratios (TSRs). In addition, a comparison between the DOE results of both 2D and 3D CFD simulations is achieved to assess the use of 2D simulations for optimization purposes. Also, a detailed numerical study of the aerodynamic performance of the turbine based on this new design is done to compare the rigid and flexible blade models' power coefficient and flow fields around the blades. Results of 2D and 3D CFD simulations show that the flexible blades are able to improve the power coefficient of the turbine by up to 66% and 32% respectively at low tip speed ratios diminishing gradually with increasing tip speed ratios. It is also noticeable that similar pattern in power coefficient results was obtained from the 2D and 3D simulations of both turbine models. This, in turn, demonstrates the efficacy of 2D simulations in accurately determining optimized design parameters while significantly reducing computational costs compared to 3D simulations.

Optimization and performance studies of PES/SAPO-34 membranes for CO2/N2 gas separation

SAPO-34 nanocrystals were prepared and used as inorganic filler in polyethersulfone (PES) membranes. The influence of preparation conditions on permeation properties of CO2 and N2 gases and CO2/N2 selectivity were studied to achieve the highest separation performance possible for this mixed-matrix membrane. Gas permeation properties of PES/SAPO-34 MMMs with different contents of SAPO-34, PES and initial film thickness were investigated according to a full factorial design of experiments (DOE). The permeation tests and DOE results revealed that the optimized conditions to maximize the selectivity for PES/SAPO-34 are: composition of 18.5 wt% of SAPO-34, 15 wt% of PES, and a casted film thickness of 400 μm resulting in a CO2/N2 selectivity of 30.85. The findings showed that a multivariable model was validated and able to predict the selectivity with determination coefficient R2 of 0.969 and the results are in accordance with values presented in literature for PES/SAPO-34 membranes. An economic analysis shows that the commercial grade price for the optimized membrane is EUR 9.43/m2 which represents around 2.2 times cheaper than polymeric commercial membranes.

DoE-ML guided optimization of an active pre-chamber geometry using CFD

An optimized active pre-chamber geometry was obtained by combining computational fluid dynamics (CFD) and machine learning (ML). A heavy-duty engine operating with methane under lean conditions was considered. The combustion process was modeled with a multi-zone well-stirred reactor (MZ-WSR) with a skeletal methane oxidation mechanism. The simulations were run for a complete cycle. For the optimization study, the pre-chamber was parametrized; six independent and three dependent variables were considered, while the volume was kept constant. Three hundred pre-chamber designs were generated, and a one-shot design of experiments (DoE) optimization was first considered. A merit function was adopted to rank the designs, and an optimum design was found from the DoE results, which yielded considerable improvements in merit ranking, considering fuel consumption, engine-out emissions, noise, and safety; secondly, machine learning algorithms were trained by utilizing the DoE results aiming at finding a globally optimum geometry for the considered operating condition. Five sequential iterations were performed, and the ML algorithms were capable of proposing a new design with superior performance compared to the best DoE.

Application of prediction intervals to the interpretation of the robustness study of a UHPLC method for the separation of cannabinoids

Design of Experiments (DoE) is a well-established tool used for analytical methods robustness studies, because of its ability to assess the effect of a great number of factors in a minimal number of experiments. However, when assessing the robustness of an analytical method the analysis of the individual effect of each factor is not sufficient on its own. Some factors may not influence the robustness of the method, but their effect combined with the effects of other factors may have a significant contribution on the robustness of the method, which is not given by conventional analysis of DoE results. The aim of this work is to propose, in addition to the analysis of the individual effects of the factors, to estimate the joint effect of the factors by means of the matrix experimental results prediction interval. This prediction interval is the interval in which, with a given probability, should fall the next results, therefore it is an interesting tool to estimate the variation limits of the method results during routine use. We also propose the use of two other prediction intervals which can help to analyze the DoE results and give a conclusion on the method robustness. The first one is based on the DoE experimental error information, and it gives an estimation of the experimental error component impact on the factors joint effect. The second one is based on the factors non-significance limits, and it provides the information regarding the factors impact on the responses in the case where the conditions are, by definition, robust. We applied these proposals to the robustness study of a UHPLC method for the separation of phytocannabinoids and we could demonstrate that, in addition to the calculated effects values and robustness information, the use of the prediction intervals information provided additional information that allowed a better interpretation of the method performance parameters.

Optimization and troubleshooting of preparative liquid chromatography using statistical design of experiments: Four small-molecule case studies

Statistical design of experiments (DoE) is used to aid in the development and execution of preparative liquid chromatography (LC) for large-scale purification of active pharmaceutical ingredients (API) and pharmaceutical intermediates. Four purification case studies were undertaken. In case study 1, a normal phase preparative silica method is developed and modeled. After initial method screening, DoE results were used to set mobile phase composition, flowrate, and sample diluent. Of the three particle sizes studied (10 µm, 20 µm, 50 µm) only 10 µm silica resin was able to produce purified API at the yield (>96%) and productivity (> 1 kg/kg-resin/day) necessitated by the project. The second case study uses DoE studies to identify critical process parameters of column load, mobile phase solvent ratio and basic modifier level for a low-resolution, preparative, chiral separation. Trade-offs between purity, yield and productivity are quantified in a tight separation which made compromising on process outcomes a necessity. The third case study troubleshoots a loss of yield experienced during operation of a process-scale reverse-phase LC purification. DoE is used to identify a critical interaction between levels of acetonitrile and phosphoric acid in the mobile phase. An operating region which increased yield from around 85% to 97% was defined and implemented. The fourth case study was initially designed as a preparative chromatography purification of API. DoE was used to screen mobile phase solubility. These experiments uncovered conditions where API is soluble, and impurities are not. The solubility model in acetonitrile/water mixtures is further defined via a response surface DoE. The resulting targeted solvent mixture allows bulk purification via dissolution of API while three less-polar impurities remain in the solid phase and are removed by filtration. These four case studies demonstrate the efficiency of DoE and response surface modeling as tools for process development and optimization.

IoT Sensor Networks in Smart Buildings: A Performance Assessment Using Queuing Models.

Smart buildings in big cities are now equipped with an internet of things (IoT) infrastructure to constantly monitor different aspects of people’s daily lives via IoT devices and sensor networks. The malfunction and low quality of service (QoS) of such devices and networks can severely cause property damage and perhaps loss of life. Therefore, it is important to quantify different metrics related to the operational performance of the systems that make up such computational architecture even in advance of the building construction. Previous studies used analytical models considering different aspects to assess the performance of building monitoring systems. However, some critical points are still missing in the literature, such as (i) analyzing the capacity of computational resources adequate to the data demand, (ii) representing the number of cores per machine, and (iii) the clustering of sensors by location. This work proposes a queuing network based message exchange architecture to evaluate the performance of an intelligent building infrastructure associated with multiple processing layers: edge and fog. We consider an architecture of a building that has several floors and several rooms in each of them, where all rooms are equipped with sensors and an edge device. A comprehensive sensitivity analysis of the model was performed using the Design of Experiments (DoE) method to identify bottlenecks in the proposal. A series of case studies were conducted based on the DoE results. The DoE results allowed us to conclude, for example, that the number of cores can have more impact on the response time than the number of nodes. Simulations of scenarios defined through DoE allow observing the behavior of the following metrics: average response time, resource utilization rate, flow rate, discard rate, and the number of messages in the system. Three scenarios were explored: (i) scenario A (varying the number of cores), (ii) scenario B (varying the number of fog nodes), and (iii) scenario C (varying the nodes and cores simultaneously). Depending on the number of resources (nodes or cores), the system can become so overloaded that no new requests are supported. The queuing network based message exchange architecture and the analyses carried out can help system designers optimize their computational architectures before building construction.

Quality by Design Methodology Applied to Process Optimization and Scale up of Curcumin Nanoemulsions Produced by Catastrophic Phase Inversion.

In the presented study, we report development of a stable, scalable, and high-quality curcumin-loaded oil/water (o/w) nanoemulsion manufactured by concentration-mediated catastrophic phase inversion as a low energy nanoemulsification strategy. A design of experiments (DoE) was constructed to determine the effects of process parameters on the mechanical input required to facilitate the transition from the gel phase to the final o/w nanoemulsion and the long-term effects of the process parameters on product quality. A multiple linear regression (MLR) model was constructed to predict nanoemulsion diameter as a function of nanoemulsion processing parameters. The DoE and subsequent MLR model results showed that the manufacturing process with the lowest temperature (25 °C), highest titration rate (9 g/minute), and lowest stir rate (100 rpm) produced the highest quality nanoemulsion. Both scales of CUR-loaded nanoemulsions (100 g and 500 g) were comparable to the drug-free optimal formulation with 148.7 nm and 155.1 nm diameter, 0.22 and 0.25 PDI, and 96.29 ± 0.76% and 95.60 ± 0.88% drug loading for the 100 g and 500 g scales, respectively. Photostability assessments indicated modest loss of drug (<10%) upon UV exposure of 24 h, which is appropriate for intended transdermal applications, with expected reapplication of every 6–8 h.

Design-of-experiments-guided optimization of slurry-cast cathodes for solid-state batteries

Laboratory research into bulk-type solid-state batteries (SSBs) has been focused predominantly on powder-based, pelletized cells and has been sufficient to evaluate fundamental limitations and tailor the constituents to some degree. However, to improve experimental reliability and for commercial implementation of this technology, competitive slurry-cast electrodes are required. Here, we report on the application of an approach guided by design of experiments (DoE) to evaluate the influence of the type/content of polymer binder and conductive carbon additive on the cyclability and processability of Li 1+x (Ni 0.6 Co 0.2 Mn 0.2 ) 1−x O 2 (NCM622) cathodes in SSB cells using lithium thiophosphate solid electrolytes. The predictions are verified by charge-discharge and impedance spectroscopy measurements. Furthermore, structural changes and gas evolution are monitored via X-ray diffraction and differential electrochemical mass spectrometry, respectively, in an attempt to rationalize and support the DoE results. In summary, the optimized combination of polymer binder and conductive carbon additive leads to high electrochemical performance and good processability. Statistical optimization of slurry-cast NCM cathodes for solid-state batteries Cycling performance and processability correlate with binder chemistry and content Design of experiments (DoE) results are corroborated experimentally Operando gas analysis reveals (electro-)chemical binder instability Teo et al. apply a statistical approach, DoE, to facilitate the transition from pelletized to slurry-cast cathodes for solid-state batteries. Datasets from electrochemical and mechanical tests are used to build a model that allows effective tailoring of the slurry recipe. The DoE predictions/results are evaluated using various analytical techniques.

Studying the Effective Parameters on Teeth Height in Internal Gear Flowforming Process

The flowforming process is a chipless metal forming process that is used to produce precise thin walled tubes. Manufacturing of internal gears using flowforming process is a difficult-to-achieve, but very interesting process in which the gear may be produced without the need for high forming forces and r high tooling cost. In this study, manufacturing of internal gears using flowforming process is studied. The process has been numerically analyzed and simulated. The plastic behavior of the material, and friction conditions were determined using tensile and friction tests, respectively. Several controlled test were performed to evaluate the validity of simulation results. A comparison of simulation and experimental results indicates very good agreement. Once the simulation is verified, the effects of roller diameter, thickness reduction percentage, feed rate and attack angle on tooth height were obtained using design of experiments (DOE) procedure. According to DOE results, attack angle (α), thickness reduction percentage (T), interaction between roller diameter and attack angle (D×α), and interaction between roller diameter and feed rate (D×f) are the most significant parameters affecting the tooth height. The tooth height increases with increasing the roller diameter and thickness reduction, but decreases with increasing the feed rate and attack angle.

Pyrolysis and anaerobic digestion of the Egeria Densa biomass in different pretreatment conditions for potential use as a substrate in the generation of bioproducts

In recent years, the interest in clean and sustainable energy technologies has grown significantly. As an alternative to fossil fuels, organic raw materials can reduce the emission of pollutants and greenhouse gases, positively impacting the environment. Submerged aquatic plants, such as Egeria densa, have become an interesting alternative. Due to the high capacity for proliferation and its physicochemical characteristics, E. densa has potential in the generation of bio-oil and biogas for thermochemical and biological processes, respectively. Here, the authors study the pyrolysis of lignocellulosic biomass and the feasibility of methane gas production through anaerobic digestion, after different pretreatments. The design of experiments (DOE) was used to investigate mass loss, biogas yield, and extractives content. Changes in surface morphology (coloration, roughness, and porosity), during pretreatment steps, were evaluated by colorimetric assays and scanning electronic microscopy. The kinetics of biogas production was modeled through a first-order equation. According to DOE results, the degradation of lignocellulose and the efficiency of methane production were favored by the alkaline pretreatment. The maximum yield of methane at 35 °C was 231.82 mL of CH4 g−1 (VS). Changes in the chemical constitution of biomass pretreated with acid, via thermochemical strategy, were observed in colorimetric and microstructural tests, justified by the increase in the concentration of extracts. The maximum mass loss was 80 % (pyrolysis) after biomass was submitted to an acid catalyst. The results showed that the physically-chemically pre-treated aquatic macrophyte E. densa is a renewable source in bio-oil and methane production.

Development of HPLC Method for the Purity Test by Design of Experiments and Determination of Activation Energy of Hydrolytic Degradation Reactions of Sofosbuvir

Background: The present study was focused on the development of HPLC method for purity testing of sofosbuvir by the Design of Experiments and determination of the activation energy of hydrolytic degradation reactions of sofosbuvir using HPLC based on the kinetics of sofosbuvir degradation. Methods: Following four factors for the Design of Experiments were selected, stationary phase, an organic modifier of the mobile phase, column temperature and pH of the mobile phase. These factors were examined in two or three level experimental design using Modde 11.0 (Umetrics) software. The chromatographic parameters like resolution, USP tailing and discrimination factor were calculated and analysed by partial least squares. The chromatography was performed based on Design of Experiments results with the mobile phase containing ammonium phosphate buffer pH 2.5 and methanol as an organic modifier. Separation was achieved using gradient elution on XBridge BEH C8 at 50 °C and a flow rate of 0.8 mL/min. UV detection was performed at 220 nm. The activation energy of hydrolytic degradation reactions of sofosbuvir was evaluated using two different calculation methods. The first method is based on the slope of dependence of natural logarithm of the rate constant on inverted thermodynamic temperature and the second approach is the isoconversional method. Results and Conclusion: Calculated activation energies were 77.9 ± 1.1 kJ/mol for the first method and 79.5 ± 3.2 kJ/mol for the isoconversional method. The results can be considered to be identical, therefore both calculation methods are suitable for the determination of the activation energy of degradation reactions.

Design of experiments guided multivariate calibration for the quantitation of injectable colistimethate sodium by ultra performance liquid chromatography – High resolution mass spectrometry

Colistimethate sodium (CMS) is a widely administrated old-generation prodrug for the treatment of the life-threatening infections caused by multi-resistant Gram-negative bacteria. Until now, the quality control procedure of the CMS commercial products is based on microbiological assays. The aim of the study is the development of a chemical analysis methodology based on liquid chromatography - mass spectrometry (LC-MS) that could be used for the quality control of CMS products. The careful optimization of the LC and QToF-MS parameters was deemed crucial, as CMS is known to be a very complex mixture. Thus, a two stage Design of Experiments (DoE) pipeline has been followed, aiming towards the separation of the mixture components. According to the DoE results, a baseline-resolved chromatogram revealing more than 20 compounds was achieved. The separation was performed using a Waters Acquity BEH C8 column employing gradient elution. The mobile phase consisted of aq. ammonium formate 0.005 M (pH 6) (solvent A) and methanol/acetonitrile 79/21 (v/v) (solvent B). A second optimization experiment for the MS signal was employed in order to achieve maximum sensitivity. The singly charged signals were monitored for the validation in the positive ion mode. The calibration curve range was 50–110 μg mL−1, corresponding to the 80–120% of the nominal CMS amount in the commercial products. Due to the complexity of the CMS chromatograms and the corresponding spectrum of each chromatographic peak, untargeted and targeted approaches were performed employing the MZmine software. Furthermore, apart from the classical univariate statistical analysis, partial least squares regression (PLS-R) model was also employed, as the variables were more than the observations. The developed methodology has been employed to analyze several batches and inconsistences have been discovered.

Effects of Refill Friction Stir Spot Weld Spacing and Edge Margin on Mechanical Properties of Multi-Spot-Welded Panels

Refill friction stir spot welding (RFSSW) is an emerging technology for joining aerospace aluminum alloys. The aim of the study is to investigate the effects of the refill friction stir spot weld spacing and the edge margin on the mechanical properties of multi-spot-welded AA7075-T6 panels. AA7075-T6 is a baseline aerospace aluminum alloy used in aircraft structures. The study employs an innovative robotic RFSSW system that is designed and developed by Kawasaki Heavy Industries (KHI). The experimental strategy uses Design of Experiments (DoE) to characterize the failure loads of multi-spot-welded panels in terms of the spot weld spacing, edge margin, and heat-affected zone (HAZ) of the spot weld. The RFSSW process leaves behind a thermal “imprint” as HAZ in heat-treatable aluminum alloys. According to the DoE results, larger spot weld spacings with no HAZ overlap produce higher failure loads of multi-spot-welded panels. On the other hand, edge margins that are equal to or less than the spot weld diameter demonstrate abnormal plastic deformations, such as workpiece edge swelling and weld crown dents, during the RFSSW process. The larger edge margins do not demonstrate such abnormal deformations during the welding process.

Improving sensitivity for the targeted LC-MS/MS analysis of the peptide bradykinin using a design of experiments approach

The nonapeptide bradykinin is endogenously present only in low picomolar plasma concentrations, subsequently making reliable detection using liquid chromatography coupled to mass spectrometry (LC-MS/MS) challenging. Furthermore, non-specific adsorption during sample preparation and storage can lead to unpredictable peptide losses. To overcome these issues, a design of experiments (DoE) approach was applied, which consisted of a screening to identify impacting factors, optimisation and confirmation runs. On the one hand, different injection solvent compositions and sample collection materials were investigated in order to decrease non-specific adsorption. On the other hand, the addition of modifiers, which are known to enhance the signal intensity in LC-MS/MS, to the chromatographic mobile phase was examined. Polypropylene was the most suitable material among those investigated and resulted in a factor increase of 12.0 compared to LC-MS glass. The advantages of protein low-binding polypropylene versus standard polypropylene were fully compensated by the optimisation of the injection solvent. The latter substantially contributed to a decrease of non-specific adsorption of bradykinin. In this regard, bradykinin further benefitted from an organic fraction and a high amount of formic acid. Based on the DoE results, the final optimised injection solvent—consisting of 8.7% formic acid in 49.4/5.3/36.6 water/methanol/dimethyl sulfoxide (v/v/v)—was established. Furthermore, optimisation of the mobile phase composition yielded a signal intensity increase by a factor of 7.7. The transferability of the optimisation results conducted in neat solutions were successfully confirmed in human plasma. The applicability of this approach was further supported by the successful determination of low-abundance endogenous bradykinin levels in human plasma using LC-MS/MS.

Fabrication and characterization of an electrostatically bonded PEEK‐ hydroxyapatite composites for biomedical applications

In this study, it was aimed to produce electrostatically induced polyetheretherketone (PEEK) and strontium substituted hydroxyapatite (SrHA) composites. SrHA nanoparticles (5 and 10 vol%) were introduced in the PEEK matrix to increase its mechanical properties and osseointegration. In order to disperse and homogeneously distribute the nanoparticles within the matrix, an electrostatic bond was developed between the PEEK and nanoparticles by wet processing through the attraction of the oppositely charged particles. Particles were pressed and sintered according to the Taguchi Design of experiments (DoE) array. The effects of SrHA reinforcement, sintering temperature and time on the density, crystallinity and crystallite sizes were determined with density test, DSC and XRD, respectively. The disks were also analyzed via SEM, FTIR, compression, microhardness, and nanoindentation tests and were immersed into the simulated body fluid (SBF). The composites produced from electrostatically induced powders presented a homogenous microstructure as SEM analysis illustrated the homogenous dispersion and distribution of the SrHA nanoparticles. The SrHA nanoparticles decreased the relative density and crystallinity of the composite, whereas, the rise in the sintering temperature and time enhanced the relative density, according to the DoE results. SrHA reinforcement improved the reduced modulus and nanoindentation hardness of the PEEK (348.47 MPa, 5.97 GPa) to 392.02 MPa and 6.65 GPa, respectively. SrHA promoted the bioactivity of the composite: an apatite layer covered the surface of PEEK/10SrHA composite after 14 days incubation. These promising results suggest that the electrostatically bonded composite powders would be used to produce homogenous PEEK based bioactive composites.

Use of a temperature ramp approach (TRA) to design an optimum and robust freeze-drying process for pharmaceutical formulations.

Freeze-drying, until now, has been a process that was designed using a trial and error experimental approach. This approach is often material and time consuming, and the resulting freeze-drying processes are neither optimum nor robust. Accordingly, the objective of this study was to develop a simple-to-use and experimental-based approach to design an optimum and robust freeze-drying process for any given formulation. The temperature ramp approach (TRA) detailed in this study involves the implementation of a customized design of experiments (DoE) to perform few (three or four) experiments using a given drug formulation. The DoE results are analyzed to define optimum processing conditions (i.e., shelf temperature and chamber pressure) based on a predefined range of target product temperature for primary drying, which could be defined from formulation characterization at its frozen state. In this study, a successful freeze-drying process of two model formulations using the TRA was designed. Verification experiments at the optimum processing conditions showed excellent agreement in both product temperature and sublimation rate with the values obtained using the TRA. Thus, the TRA detailed in this study offers a significant advantage to reduce development time and material, and enhance the efficiency and robustness of the resulting freeze-drying process.

Optimal Design of PMSM Based on Automated Finite Element Analysis and Metamodeling

To obtain accurate optimal design results in electric machines, the finite element analysis (FEA) technique should be used; however, it is time-consuming. In addition, when the design of experiments (DOE) is conducted in the optimal design process, mechanical design, analysis, and post process must be performed for each design point, which requires a significant amount of design cost and time. This study proposes an automated DOE procedure through linkage between an FEA program and optimal design program to perform DOE easily and accurately. Parametric modeling was developed for the FEA model for automation, the files required for automation were generated using the macro function, and the interface between the FEA and optimal design program was established. Shape optimization was performed on permanent magnet synchronous motors (PMSMs) for small electric vehicles to maximize torque while maintaining efficiency, torque ripple, and total harmonic distortion of the back EMF using the built-in automation program. Fifty FEAs were performed for the experimental points selected by optimal Latin hypercube design and their results were analyzed by screening. Eleven metamodels were created for each output variable using the DOE results and root mean squared error tests were conducted to evaluate the predictive performance of the metamodels. The optimization design based on metamodels was conducted using the hybrid metaheuristic algorithm to determine the global optimum. The optimum design results showed that the average torque was improved by 2.5% in comparison to the initial model, while satisfying all constraints. Finally, the optimal design results were verified by FEA. Consequently, it was found that the proposed optimal design method can be useful for improving the performance of PMSM as well as reducing design cost and time.