Full Factorial Experimental Design Research Articles

Bacterial ligands as flexible and sensitive detectors in rapid tests for antibodies to SARS-CoV-2

Lateral flow immunoassay (LFIA) is widely employed as point-of-care tests (POCT) for the diagnosis of infectious diseases. The accuracy of LFIA largely depends on the quality of the immunoreagents used. Typical LFIAs to reveal the immune response to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) employ anti-human immunoglobulin (hIG) antibodies and recombinant viral antigens, which usually are unstable and poorly soluble. Broad selective bacterial proteins, such as Staphylococcal protein A (SpA) and Streptococcal protein G (SpG) can be considered alternatives to anti-hIG to increase versatility and sensitivity of serological LFIAs because of their high binding capacity, interspecies reactivity, and robustness. We developed two colorimetric LFA devices including SpA and SpG linked to gold nanoparticles (GNP) as detectors and explored the use of a specific, stable, and soluble immunodominant fraction of the nucleocapsid protein from SARS-CoV-2 as the capturing agent. The optimal amount of SpA-GNP and SpG-GNP conjugates and the protein-to-GNP ratios were defined through a full factorial experimental design to maximize the diagnostic sensitivity of the LFIAs. The new LFA devices were applied to analyze 105 human serum samples (69 positive and 36 negatives according to reference molecular diagnostic methods). The results showed higher sensitivity (89.9%, 95% CI 82.7–97.0) and selectivity (91.7%, 82.6–100) for the SpA-based compared to the SpG-based LFA. In addition, 18 serum samples from cats and dogs living with COVID-19 patients were analyzed and 14 showed detectable levels of anti-SARS-CoV-2 antibodies, thus illustrating the flexibility of the SpA- and SpG-based LFAs.Graphical abstract

Study of the Influence of the Manufacturing Parameters on Tensile Properties of Thermoplastic Elastomers.

Additive manufacturing (AM) has increased its field of application, not only for prototypes but also for final parts. Therefore, the need to study new materials is currently growing. This paper aims to study the effect of the printing parameters used in two different thermoplastic elastomers (PEBA 90A and TPU 98A) subjected to tensile tests, evaluating a competent alternative to the currently most used 3D printed materials. To achieve it, a full factorial design experiment is applied to analyze the influence on the tensile responses of two printing parameters: the layer height and the fill density. In addition, an analysis of variance (ANOVA) is used to describe the relations among the parameters and the mechanical responses obtained. Moreover, assessment of damping properties was done. Results show that each thermoplastic elastomer should be studied separately, although the proposed methodology can be used for each material independently of their nature. Finally, a correlation between the printing parameters and the mechanical behavior of TPU 98A and PEBA 90A was found: the layer height and the infill are statistically influential parameters for both materials.

PolyJet 3D printing: Predicting color by multilayer perceptron neural network

PolyJet 3D printing can be used to fabricate colored physical models of anatomical structures such as skull and heart with realistic appearances. These medical models can be used for surgical simulation and planning of complex operations, as well as anatomy teaching. PolyJet is theoretically capable of producing any color by mixing multiple materials. However, the measured color of a sample printed by PolyJet is often different from the specified color in the printer software. Therefore, it is often difficult to predict the measured color of a sample before printing. This paper reports a study on predictive relationships between measured color and four control factors of PolyJet (i.e., three RGB values of specified color and finish type) by design of experiments and application of multilayer perceptron (MLP) neural network model. Experimental data are collected using a full factorial design of experiments. These data are used to train and test the MLP model using 5-fold cross validation. Then, the prediction performances of the MLP model are compared with a linear regression model and a cubic regression model. The results show that the MLP model is capable of predicting measured color with higher accuracy.

Kinetic and statistical perspectives on the interactive effects of recalcitrant polyaromatic and sulfur heterocyclic compounds and in-vitro nanobioremediation of oily marine sediment at microcosm level

A halotolerant biosurfactant producer Pseudomonas aeruginosa strain NSH3 (NCBI Gene Bank Accession No. MN149622) was isolated to degrade high concentrations of recalcitrant polyaromatic hydrocarbons (PAHs) and polyaromatic heterocyclic sulfur compounds (PASHs). In biphasic batch bioreactors, the biodegradation and biosurfactant-production activities of NSH3 have been significantly enhanced (p < 0.0001) by its decoration with eco-friendly prepared magnetite nanoparticles (MNPs). On an artificially contaminated sediment microcosm level, regression modeling and statistical analysis based on a 23 full factorial design of experiments were trendily applied to provide insights into the interactive impacts of such pollutants. MNPs-coated NSH3 were also innovatively applied for nanobioremediation (NBR) of in-vitro diesel oil-polluted sediment microcosms. Gravimetric, chromatographic, and microbial respiratory analyses proved the significantly enhanced biodegradation capabilities of MNPs-coated NSH3 (p < 0.001) and the complete mineralization of various recalcitrant diesel oil components. Kinetic analyses showed that the biodegradation of iso- and n-alkanes was best fitted with a second-order kinetic model equation. Nevertheless, PAHs and PASHs in biphasic batch bioreactors and sediment microcosms followed the first-order kinetic model equation. Sustainable NBR overcome the toxicity of low molecular weight hydrocarbons, mass transfer limitation, and steric hindrance of hydrophobic recalcitrant high molecular weight hydrocarbons and alkylated polyaromatic compounds.

Effect of Runner Thickness and Hydrogen Content on the Mechanical Properties of A356 Alloy Castings

Earlier studies demonstrated the detrimental effect of entrained bifilm defects on aluminum cast alloys’ tensile and fatigue properties. It was suggested that hydrogen has a contributing role as it diffuses into the bifilms and swells them out to form hydrogen porosity. In this study, the effect of the runner height and hydrogen content on the properties of A356 alloy castings was investigated using a two-level full factorial design of experiments. Four responses, the Weibull modulus and position parameter of both the ultimate tensile strength (UTS) and % elongation, were assessed. The results suggested that decreasing the runner height and adopting procedures intended to decrease the hydrogen content of the casting caused a considerable enhancement of the Weibull moduli and position parameters of the UTS and % elongation. This was reasoned to the more quiescent practice during mold filling, eliminating the possibility of bifilm formation as well as the decreased hydrogen level that eliminated the amount of hydrogen diffused into the bifilms and accordingly decreased the size of the entrained defects. This, in turn, would allow the production of A356 cast alloys with better and more reproducible properties.

Application of Pressurized Liquid Extractions to Obtain Bioactive Compounds from Tuber aestivum and Terfezia claveryi

A PLE (pressurized liquid extraction) method was adjusted following a full-factorial experimental design to obtain bioactive-enriched fractions from Tuber aestivum and Terfezia claveryi. Temperature, time and solvent (water, ethanol and ethanol–water 1:1) parameters were investigated. The response variables investigated were: obtained yield and the levels of total carbohydrate (compounds, β-glucans, chitin, proteins, phenolic compounds and sterols). Principal component analysis indicated water solvent and high temperatures as more adequate parameters to extract polysaccharide-rich fractions (up to 68% of content), whereas ethanol was more suitable to extract fungal sterols (up to 12.5% of content). The fractions obtained at optimal conditions (16.7 MPa, 180 °C, 30 min) were able to protect Caco2 cells from free radical exposure, acting as antioxidants, and were able to reduce secretion of pro-inflammatory cytokines in vitro: IL-6 (50%), and TNFα (80% only T. claveryi ethanol extract), as well as reduce high inhibitory activity (T. aestivum IC50: 9.44 mG/mL).

On the laser forming process of copper/aluminum bi-metal sheets with a functional thickness

Bi-metal sheets with a functional thickness are the heterogeneous materials in which physical/mechanical properties of the sheet change from upper layer to the bottom layer. The scope of this work is to investigate the forming of copper/aluminum bi-metal sheets with a functional thickness using laser forming process. In fact, the combined properties of a bi-metal sheet make it complicated to predict the forming behavior of these materials. For this reason, laser forming process of bi-metal sheets has been simulated using a DFLUX subroutine to define the continuous scan path of laser beam. A detailed discussion has been presented on how to activate the temperature gradient mechanism during the process. Furthermore, a set of experimental tests was also performed to examine the validity of the numerical tests. Thus, the effect of each input process parameter including laser power, scan velocity, and beam diameter has been examined. A full-factorial design of experiment has been utilized to specify the quantitative influences of the parameters on the final bending angle. A statistical model has been also derived for predicting the bending angle in laser forming of bi-metal sheets. The numerical results show that by increasing the laser power and scan velocity and decreasing the beam diameter a maximum forming magnitude would be achieved.

A study of signal noise reduction of the mass air flow sensor using the flow conditioner on the air induction system of heavy-duty truck

The mass air flow meter is a critical sensor that works based on thermal hot wire technology, used to determine the fuel to be injected into the cylinder and calculate the fuel-air ratio. In order to measure the airflow rate accurately, the flow should be uniform and smooth upstream of the sensor. The flow disturbance with a short straight length upstream of the flow meter results in the noise of the sensor signal. This noise causes unstable mass flow measurement on the system. Flow conditioners can be used to smooth the velocity profile of the flow. In this study, experimental and numerical methods were used to characterize the performance and operating accuracy of the mass flow meter used in heavy-duty truck applications. The flow conditioners were implemented to smooth the velocity profile around the mass flow meter that was disrupted by bends. The flow structures with and without flow conditioner were examined using Particle Image Velocimetry (PIV) to measure the time-averaged velocity. As well as the validated computational fluid dynamics (CFD) model provides data to understand the flow uniformity effect of the conditioner on the mass airflow (MAF) sensor. The optimization study was performed using a full factorial design of experiment (DOE) for flow conditioner design. A robust methodology was developed for the flow conditioner characteristics and mass airflow sensor implementation on the air induction system.

Multi-objective optimization of edge quality and surface integrity when wire electrical discharge machining of polycrystalline diamonds in cutting tool manufacture

This work presents an energy-saving approach to the single-pass wire electrical discharge machining (WEDM) of polycrystalline diamond with no degradation in edge geometry or surface integrity in cutting tool manufacture. The proposed boundary conditions and discharging wavelets enable the least number of pulse-off durations to effectively recover the electrical potential for the extended discharging action and the successively energy saving via a secondary MOSFET circuit. Secondly, the determined pulse-off durations could sufficiently improve the effectiveness of the Joule heat model which did not consider the extrinsic interference from the removed materials in the discharging gap. Full factorial design experiments were performed to validate the effects of the operating parameters on the material removal rate, surface roughness, cutting edge radius, and surface waviness in single-pass WEDM for the shaping of diamond cutting tools. Precision levels in the fitting of analytic models to experiment results were presented. Linear algebra was introduced to evaluate the selection of operating parameter combinations aimed at optimizing of observations with adjustable weightings. Thermal induced defects and alterations in the microstructures were reported and discussed. The proposed ready-to-use diamond cutting tools present a good balance between energy savings and appreciable outcomes.

Vol4 num2-14 Machining of Hybrid Composite Materials

Composite materials have been having an increasing importance in the industry. Their high specific properties and their high flexibility that allows tailoring materials that suit the needs of practically every project, have been leading to a growth in the demand of this kind of materials. Fiber-Metal Laminates have an established usage on the aeronautic industry and lately have been subject of research by the automotive industry.&nbsp; Since composite manufacturing methods allow the obtention of components with their final geometry, drilling is the most used machining operation in the machining of composites. Drilling of composites is very demanding from the tools point of view, with very high wear rates and several defects can occur to the workpiece like delamination, fiber pull-outs, burrs and matrix degradation. When combined with a metal, additional challenges appear in drilling these materials such as loads of different magnitudes during a single operation and surface defects on the interface because of chip removal. Orbital drilling is seen as a promising alternative to conventional drilling, presenting several advantages that result in holes with higher quality.&nbsp; Given the interest the automotive industry has been showing in fiber-metal laminates constituted by steel and carbon fiber reinforced plastics (CFRP) and due to the lack of information regarding drilling of this combination of materials, the aim of this work was to evaluate the influence of the machining parameters in the quality of holes obtained by orbital drilling. A full factorial design of experiments was done and the influence of the parameters was then evaluated by means of an Analysis of Variance (ANOVA).&nbsp;

Influence of tool geometry on cutting zone temperature during turning of aluminium alloy AA2219

Machining of aluminium alloys is considered to be highly important owing to their wide spectrum of applications. Analysis of machinability with respect to the cutting zone temperature decides the behaviour of the material when used in various functional areas. Current research aims to investigate the cutting zone temperature during the turning of aluminium alloy using cemented carbide inserts. From the results, it could be noted that the feed and nose radius were the most dominant factors which influenced the cutting zone temperature. A regression model was developed by using the Taguchi’s full factorial design of experiment. Cutting zone temperature regression model exhibited least deviation when compared with the actual response within the range of considered turning process parameters. The adequacy and accuracy of the regression equation was justified by using a comparative study. It was found that the optimal combinations of machining parameters to minimize the temperature were 0.4 mm nose radius, 0.1 mm/rev of feed rate, 1500 rpm speed and 0.3 mm depth of cut.

Experimental studies and mathematical modelling of Inconel 600 with CVD coated TiN/TiCN/Al2O3/ZrCN inserts under dry machining Conditions

Inconel 600 is a nickel-based super alloy with applications in the field of Aerospace, Nuclear energy, Heat treatment, and chemical processing industries, and is a difficult to cut material due to its high hot hardness and strength. Coated carbide inserts can improve the machinability of alloys like Inconel 600 and other super alloys. This work is about the machinability characteristics study on Inconel 600 alloy under dry turning environment with high speed machining using CVD coated TiN/TiCN/Al2O3/ZrCN cutting tool insert. Cutting speed (200, 250, and 300 m/min), feed rate (0.05, 0.1, 0.15 mm/rev), and back rake angle (-7, -5, -3°) are considered as machining process parameters. Full factorial design of experiments were performed to evaluate the performance of process parameters on surface roughness and material removal rate. It was found that surface roughness decreased with increase in cutting speed and increases with increase in feed rate. Surface roughness increases with increase in rake angle in negative direction. Material removal rate increases with increase in both cutting speed and the feed rate whereas rake angle had minimal influence. Mathematical modelling was done on the obtained results and found that Rˆ2 value for surface roughness and material removal rate were 99.14% and 98.69% respectively. Analysis of Variance on surface roughness found that feed rate* feed rate is the most influencing parameter with maximum contribution of 34.16% whereas feed rate* cutting speed parameter has maximum influence on material removal rate with contribution of 83.03 %.

Influence of the effective thermal conductivity of the powder bed on the properties of overhanging areas in Laser-based Powder Bed Fusion of Ti-6Al-4V

In this paper the influence of a variation of the effective thermal conductivity of the powder bed on the properties and manufacturability of overhanging component areas in laser-based powder bed fusion (PBF-LB/M) of Ti-6Al-4V is investigated. The variation of the effective thermal conductivity of the powder bed is realized by contactless support structures under the over-hanging test specimens. The downskin angle between the overhanging test specimens and the base plate as well as the distance between the overhanging area and the test specimens are varied in a full factorial experimental design. The test specimens are examined with regard to the properties of relative density, surface roughness and geometric accuracy. In addition, a key figure is introduced which makes the results comparable with those obtained with other system configurations and parameters such as laser spot diameter, hatch distance and layer thickness.

Optimization of injection molded polymer lab-on-a-chip for acoustic blood plasma separation using virtual design of experiment

This study presents the development towards the optimization and manufacturing of a novel cost-effective fully polymer-based ultrasound actuated chip as a Point-of-care (POC) diagnostic platform for blood plasma separation and analysis. The chip was designed following multi-physic functional simulation to obtain optimal dimensions with respect to blood separation enhanced capacity. Initial dimensional guidelines showed most favorable performance to be achieved with design dimensions of 150 µm height, 375 µm width and 34 mm length. The chip encompasses a micro-fluidic channel with a relatively long length compared to the height and the width. As such, main manufacturability challenge is represented by the capability to maintain the channel dimensional conformance throughout the entire length. In order to tackle the challenge and optimize the results, developing a feedback loop was initiated for the device process development where the data of the preliminary micro-injection molding (µIM) production was exploited to implement a simulation-based full-factorial virtual design of experiment (DOE) for all the four materials of interest: polymethyl methacrylate (PMMA), cyclic olefin copolymer (COC), polycarbonate (PC) and polystyrene (PS). The four essential µIM parameters namely, mold temperature, melt temperature, injection velocity and packing pressure were used as factors. Overall, simulations were performed based on the virtual DOE inputs for the four aforementioned materials. The input data for the utilized process parameters in the virtual DOE was provided by our first production batch. Ultimately, in the case of PMMA, parts were molded and characterized based on the process settings that had demonstrated the lowest linear shrinkage in our entire virtual DOE responses. For the linear shrinkage of these parts, a respective width and height difference of 1% and 3.3% between the simulations and production results were obtained. Moreover, as a secondary study sink marks were also inspected and minimized through simulations. Initially, sink marks as deep as 80 µm were spotted on the chip. After the use of optimization cycle the value decreased to as low as 10 µm. The results confirm the positive impact of the virtual experiments for future rapid assessment of potential new chip designs.

Parametric study of electric discharge machining of titanium grade 2 alloy in distilled water

The current article provides a detailed discussion on the machining of titanium grade 2 alloy in an electric discharge machine (EDM). Copper is taken as a tool electrode, and distilled water is taken as dielectric for machining purpose. Machining with hydrocarbon oil in EDM causes environmental pollution and severe threats to the operator's health. Machining is carried out in distilled water to replace the hazardous hydrocarbon oil. The full factorial experimental design method is adopted to realize the outcome of input parameters on the output parameters. The consequence of current (Ip) and pulse-on time (Ton) are analyzed on response measures viz. material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). The cause of change in MRR, TWR, and SR trends is discussed. All the response measures show a rising trend with a rise in the value of process parameters. Analysis of Variance is implemented to notice the contribution fraction of each control variable on the response measures. Optical microscopic images are analyzed at lower and higher energy settings to observe the difference in surface morphology. MRR varies from 2.142 to 10.116 mg/min, TWR from 0.251 to 1.356 mg/min, and SR from 4.1 to 9.6 µm, respectively. Overall evaluation criteria are used to obtain input parameters to optimize the performance parameters.

Optimisation of branched-chain amino acid requirements for 15–21-day-old broiler chickens by using the Taguchi method

Context There are complex inter-relationships among the branched-chain amino acids (BCAAs), making the use of full factorial design experiments more complicated, costly and time-consuming when attempting to estimate the optimal levels of the BCAAs. Aims Two experiments were conducted to determine optimal concentrations of BCAAs by using the Taguchi method and mathematical models for broiler chickens during 15–21 days of age. Methods In the first experiment, the optimal concentrations of BCAAs were determined for broiler chickens, and the accuracy of the results was evaluated in the second experiment. The orthogonal array layout of L16 (43) was applied to test four concentrations of leucine (Leu), isoleucine (Ile) and valine (Val). To test the validity of the results from 16 diets selected by the Taguchi method and four additional diets from the full factorial design method, a total of 960 broiler chickens was assigned to these 20 experimental diets, with six replicates of eight birds each. The Taguchi method, the multi-objective genetic algorithm (MOGA) and the random search algorithm (RSA) were used to determine the best concentration of BCAAs on the basis of optimal bird performance, which was measured by bodyweight gain, feed intake, and feed conversion ratio (FCR). In the second experiment, six dietary treatments were formulated to establish the reliability of the predictions obtained from the first experiment (Experiment 1). Key results No exceptional concentrations of BCAAs were achieved for a maximum average daily gain and a minimum FCR; however, the diet containing 1.177–1.353% standardised ileal digestible (SID) Leu, 0.742–0.810% SID Ile, and 0.832–0.915% SID Val showed the best performance. The results of the second experiment indicated that the Taguchi method could be used instead of a full factorial design to optimise the amino acid requirements of broiler chickens. Conclusions The requirements of BCAAs were overestimated using the MOGA and RSA methods compared with the Taguchi method. Implications In commercial diets containing high levels of SID Leu, supplementation of an extra 5% SID Ile and Val may improve broiler performance during 15–21 days of age.

The Effect of Heat Treatment on Specific Gravity and Organoleptic Properties of Jack Bean (Canavalia ensiformis) Milk

Most of the dairy products sold in the market in Indonesia are animal milk but those products cannot be consumed by people with lactose intolerance and allergies. Plant-based milk is an alternative substitute for animal milk, and the most widely known is soy milk, but the production of soybeans in Indonesia is low. Jack beans (Canavalia ensiformis) are an alternative raw material substitute for plant-based milk. Meanwhile, the protein content of jack beans is similar to soybeans, but it is not popular as a food ingredient. This study aims to obtain the acceptable combination factors in processing jack bean milk. A full factorial design experiment was conducted to analyze the effect of temperature and heating duration on the specific gravity of jack bean milk and its organoleptic properties such as color, aroma, taste, and texture. The results showed that the temperature and heating time influenced the specific gravity and organoleptic properties of jack bean milk. In addition, the best combination of factors and levels is at a heating temperature of 100 °C for 20 minutes, which results in the specific gravity of jack bean milk, which is 1.0758.

Integrated Surrogate Optimization of a Vertical Axis Wind Turbine

In this work, a 3D computational model based on computational fluid dynamics (CFD) is built to simulate the aerodynamic behavior of a Savonius-type vertical axis wind turbine with a semi-elliptical profile. This computational model is used to evaluate the performance of the wind turbine in terms of its power coefficient (Cp). Subsequently, a full factorial design of experiments (DOE) is defined to obtain a representative sample of the search space on the geometry of the wind turbine. A dataset is built on the performance of each geometry proposed in the DOE. This process is carried out in an automated way through a scheme of integrated computational platforms. Later, a surrogate model of the wind turbine is fitted to estimate its performance using machine learning algorithms. Finally, a process of optimization of the geometry of the wind turbine is carried out employing metaheuristic optimization algorithms to maximize its Cp; the final optimized designs are evaluated using the computational model for validating their performance.

Parametric Optimization of NACA 4412 Airfoil in Ground Effect Using Full Factorial Design of Experiment

Parametric Optimization of NACA 4412 Airfoil in Ground Effect Using Full Factorial Design of Experiment

Indirect tool wear measurement and prediction using multi-sensor data fusion and neural network during machining

The quality produced of the machined workpiece is affected by various machining parameters including the tool wear. The condition of tool can be monitored by analyzing various machining responses through sensors. The use of multiple sensors allows for a comparison of data and information gathered from various sources, assisting in deciding the state of the tool and the workpiece. In current work, multiple sensors are attached to a lathe during the machining of AISI 4140. This paper presents a multi-sensor data fusion method for flank wear measurement and prediction using various parameters such as vibration, power, temperature, force, and surface roughness. Dry machining is performed by using PVD coated tool insert, and the flank wear is analyzed. Experiments are performed with a full factorial experimental design L27 orthogonal array. Taguchi approach has been utilized to evaluate the effect of machining parameters on tool wear. Also, the tool wear is predicted through an artificial neural network (ANN) model, and the results are compared with manually measured data. The results showcased the effectiveness of the proposed tool wear prediction model with the least measurement error. The work presents a novel neural network approach for the accurate prediction of tool wear and hence eliminates the necessity of manual measurement of tool wear and leading towards the increased productivity and quality of the work part.