Experimental Design Techniques Research Articles

A novel pedicle screw design to maximize screw-bone interface strength using finite element analysis and design of experiment techniques.

Basic study. This study aimed to utilize finite element (FE) analysis and design of experiment (DoE) techniques to propose and optimize a novel pedicle screw design and compare its pull-out force with that of a control device. Pedicle screw-based fixation is the gold-standard treatment for spine diseases, particularly in fusion procedures. However, pedicle screw loosening and breakage still occur in osteoporotic and non-osteoporotic patients. This research investigates screw design modifications to enhance screw-bone interface strength and reduce the likelihood of loosening. We conceptualized a novel pedicle screw considering vertebral bone morphology and strength differences. A validated FE model was developed and used in conjunction with DoE to determine the screw՚s optimum geometrical parameters. The FE model was validated through simulation and laboratory experiments using the control device. The optimized thread profiles for cortical bone and cancellous bone were determined, with pull-out force as the primary factor for screw design evaluation. FE analysis results for the control device closely matched experimental results, with less than 5% difference. The chosen unique pitch/depth ratio showed maximum pull-out force for cortical bone, while DoE enabled the optimization of design parameters for cancellous bone. The optimized pedicle screw exhibited a 15% increase in pull-out force compared to the control device. The study proposes a novel pedicle screw design with better pull-out strength than the control device. Combining FE analysis with DoE is an effective approach for screw design optimization, reducing the need for extensive prototyping tests. A two-variable analysis suffices for optimizing cortical bone design parameters, while a multi-variable analysis is more effective for optimizing cancellous bone design parameters.

Green Order Sorting Problem in Cold Storage Solved by Genetic Algorithm

This study investigates the efficiency of cold storage warehouses and contributes to sustainable supply chain management by integrating eco-friendly practices into storage operations. In facilities for milk and its derivatives, unregulated order handling significantly increases energy consumption due to frequent door openings in the cooler. To address this challenge, we developed a novel mathematical model aimed at optimizing order sequences and minimizing energy costs, addressing a previously unexplored gap in the literature. A genetic algorithm (GA) was employed to solve this model, with careful consideration of carbon emissions generated during the algorithm’s execution. We utilized the Yates notation, an experimental design technique, to systematically optimize the GA’s parameters, ensuring robust and statistically valid results. This methodology enabled a thorough analysis of the factors influencing energy consumption. The findings enhance energy efficiency in cold storage warehouses, leading to reduced carbon dioxide emissions and fostering sustainable practices within supply chain management. Ultimately, this study successfully integrates green practices into cold storage operations, supporting broader sustainability objectives.

Machining performance analysis of wire-EDM for machining of titanium alloy using multi-objective optimization and machine learning approach

This study investigates the multi-objective optimization of process parameters in wire electrical discharge machining (WEDM) using titanium alloy grade 5 and zinc-coated brass microwire. The novelty lies in the comprehensive investigation of the effects of varying pulse on time (Ton), wire tension (WT), and wire feed rate (WF) on output responses, including surface roughness (SR), surface crack density (SCD), corner diameter (CD), and kerf width (KW). The Box-Behnken design, a sophisticated response surface methodology (RSM) technique, is employed to efficiently explore the complex relationships and interdependencies between these input and output parameters. The study's novel approach incorporates machine learning algorithms, including decision tree, light gradient boosting machine (LightGBM) and random forest, to predict the characteristics of the laser-cut components based on the input parameters. The contour plots generated provide valuable insights into the underlying patterns and associations between input and output features, aiding in the understanding of the WEDM process. The results reveal that the optimized values for SR, SCD, CD, and KW are obtained at specific machining conditions: Ton of 10 μs, WF of 3.79 m/min, and WT of 8 gf. Among the machine learning algorithms employed, random forest outperforms the others in predicting KW, SR, SCD and CD, exhibiting significantly lower mean squared error (MSE) and mean absolute error (MAE) values. This study contributes to the field of WEDM by providing a comprehensive analysis, incorporating advanced experimental design techniques and machine learning algorithms, which can aid in process optimization and quality control in manufacturing applications.

The Utilization of Central Composite Design for the Production of Hydrogel Blends for 3D Printing

Central composite design (CCD) is a statistical experimental design technique that utilizes a combination of factorial and axial points to study the effects of multiple variables on a response. This study focused on optimizing hydrogel formulations for 3D printing using CCD. Three biopolymers were selected: sodium alginate (SA), gelatin (GEL), and carboxymethyl cellulose (CMC). The maximum and minimum concentrations of each polymer were established using a Google Scholar search, and CCD was employed to generate various combinations for hydrogel preparation. The hydrogels were characterized in accordance with their swelling degree (SD) in phosphate-buffered saline (PBS) and Dulbecco’s Modified Eagle Medium (DMEM), as well as their printability in 2D and 3D assays. The formulation consisting of 7.5% SA, 7.5% GEL, and 2.5% CMC exhibited the best swelling properties and exceptional printability, surpassing all other tested formulations. This study highlights the effectiveness of design of experiment methodologies in accelerating the development of optimized hydrogel formulations for various applications in 3D printing and suggests avenues for future research to explore their performance in specific biological contexts.

Recovery of samarium and cobalt/iron oxide from SmCo magnets through acid baking and water leaching

AbstractRare earth elements (REEs) and cobalt (Co) are listed as critical raw materials because of their importance in global industrial production growth, high supply risk, and economic significance. The recovery of Co and REEs from secondary resources is therefore proposed as a key countermeasure to address this concern. In this study, a straightforward process that integrates acid baking and water leaching is proposed for the recovery of samarium (Sm) and Co from scrap SmCo magnets. Firstly, the chemical composition of SmCo magnets is revealed by ICP-OES and XRF. The Taguchi experimental design technique is employed to optimize nitric acid baking and water leaching. Based on the thermal decomposition behavior of Co, Fe, and Sm, the acid baking temperature is studied for the conversion of metal nitrates, excluding REEs nitrates, into metal oxides. The optimal conditions for acid baking and water leaching are identified, and a reactor for the pilot-scale acid baking process is proposed. The optimum parameters are tested with the proposed reactor.

The Versatility of the Taguchi Method: Optimizing Experiments Across Diverse Disciplines

AbstractThe Taguchi method, a robust experimental design technique, establishes a strong connection between input and output variables. Known for its capacity to yield precise results with fewer trials and minimized errors, this method has gained widespread application in various fields such as engineering, physics, chemistry, economics, finance, and more. In this paper, the authors examine the importance of the Taguchi orthogonal array method, its step-by-step optimization procedure, and its potential for future applications. Through a thorough literature review, the authors investigate how the Taguchi method has been effectively employed to identify key factors influencing response variables. The versatility of the Taguchi method becomes apparent when considering its applications across diverse disciplines. Researchers in engineering have successfully utilized this technique to optimize processes and enhance product quality. Furthermore, in scientific fields like physics and chemistry, the Taguchi method has proven invaluable for conducting experiments efficiently, resulting in more accurate and reproducible outcomes. Researchers gain critical insights into the effects of factors on the response variable by employing statistical tools such as mean analysis, variance analysis, and signal-to-noise ratio. The Taguchi method remains a valuable and broadly applicable tool for optimizing experiments and identifying influential factors across multiple disciplines. This paper’s extensive literature review emphasizes its significance in various fields and outlines the step-by-step procedure to leverage its potential for optimization.

A cross-sectional study to assess the knowledge and risk of diabetic polyneuropathy using Michigan neuropathy screening instrument among type 2 diabetes mellitus

Diabetic polyneuropathy, arising from elevated blood sugar levels damaging nerves microvasculature, manifests as tingling, numbness, muscle weakness, coordination challenges, sensitivity alterations, digestive problems, and sexual dysfunction, necessitating meticulous blood sugar control, medications, physical therapy, lifestyle changes, and regular medical supervision for prevention and management. Aim: The aim of the study is to assess the knowledge and risk of diabetic polyneuropathy using Michigan Neuropathy screening Instrument among type ii diabetes mellitus. Methods: In this study Non Experimental Research study design and purposive sampling technique is used and a total of 599 samples were assessed. Knowledge Questionnaire and physical examination using standardized Michigan Neuropathy Screening Instrument were used . The mean score for knowledge is <5 and the preliminary assessment risk score is <4 and the physical examination mean score is <2.5. Results: Participants above 50 years (46.6%) of age with chronicity of diabetes of 1- 10 years (77.5%) with abnormal preliminary risk score >4 (32.2%) and abnormal physical examination score >2.5(74.6%). Females has good knowledge on diabetic polyneuropathy and also they show high risk of developing diabetic polyneuropathy. Conclusion: The study concluded that increasing years of chronicity of diabetes develops diabetic polyneuropathy, in this study females are much prone for developing diabetic polyneuropathy.

Experimental investigation of performance, emission, and combustion characteristics of a diesel engine using blends of waste cooking oil-ethanol biodiesel with MWCNT nanoparticles

In this study, blends of 5 % and 10 % ethanol with waste cooking oil biodiesel are mixed with Multi-Walled Carbon Nanotubes (MWCNT) at a concentration of 30 ppm. MWCNTs, known for their high surface area and unique properties, are added to potentially enhance combustion efficiency and reduce emissions. These blends are then tested in a diesel engine to evaluate their performance, emission and combustion characteristics using Design of experiment technique. After conducting various experiments and analyses, the blend of 20 % biodiesel, 10 % ethanol, and 30 ppm MWCNT was identified as the most optimal due to its favorable engine characteristics. Brake Thermal Efficiency (BTE) was increased from 3.1 % to 3.4 % with the addition of MWCNTs, indicating enhanced fuel efficiency. Moreover, average Fuel Consumption is decreased from 2.2 % to 2.5 %, suggesting improved fuel utilization. Using MWCNT 30 ppm in B20 ethanol blends (MWCNT 30 ppm B20+E10) resulted in 35 % reduction in nitrogen oxide (NOx) emissions, 37 % reduction in CO emissions and 39 % reduction in HC emissions. Hence MWCNTs demonstrated effectiveness in mitigating harmful exhaust emissions The optimized values for all parameters fall within acceptable ranges, indicated successful optimization using Response surface methodology. Additionally, statistical analysis reveals that the machine learning- XGBoost model outperformed all other advanced machine learning models across all tested metrics, including MSE, MAE, and R-square.

Performance investigation of punching operation to optimize the over cut of circular hole using Design of Experiment Technique “Taguchi Method”

Current research investigates the optimization of over-cut in circular hole punching operations using a CNC turret punching machine. The study employs the Design of Experiment (DOE) technique, specifically the Taguchi method, to systematically analyze the influence of three critical factors: cutting load, hit rate, and material type. The factors were varied across three levels: cutting load (10, 12, and 14 kN), hit rate (100, 150, and 200 punches per minute), and materials (EN-8, EN-24, and EN-31). An L9 orthogonal array was used to design the experimental runs, resulting in nine unique combinations to explore the effects on over-cut (OC). Over-Cut (OC) was the key response parameter, measured using a microscopic testing machine to capture high-resolution images of the punched holes. ImageJ software was employed for image processing to accurately assess the deviations. The data were analyzed using Minitab software, focusing on signal-to-noise (S/N) ratio analysis with the criterion "smaller is better." The S/N ratio results indicated that the cutting load had the most significant impact on minimizing dimensional deviation, followed by material type and hit rate. The study revealed that the lowest dimensional deviation was achieved at a cutting load of 10 kN, hit rate of 200 punches per minute, and using EN-8 material. The mean effect and S/N ratio plots further confirmed that increasing the cutting load generally led to higher over-cut values, while higher hit rates improved precision. Interaction plots indicated that EN-31 material performed robustly across varying conditions, making it a suitable choice for diverse operational settings. ANOVA analysis supported these findings, with cutting load contributing the most to the variability in OC, followed by material type and hit rate. Current study provides valuable insights into the optimization of punching parameters to achieve minimal over-cut and enhance machining accuracy. The findings underscore the importance of selecting appropriate levels of cutting load, hit rate, and material type to optimize the performance of CNC punching operations.

Optimisation of nickel ferrite production conditions for the preparation of magnetic composite photocatalysts

Ferrites of non-ferrous metals are promising magnetic catalysts that can be easily separated from the reaction mixture after use by applying a magnetic field. However, these materials have a fast electron-hole relaxation time, which reduces their activity in photoreactions. This problem is overcome by creating hybrid nanostructures based on ferrites, for example with zinc oxides. The catalytic activity of such structures depends highly on the method of their synthesis. In this work, the alkaline co-precipitation of Fe2+ and Ni2+ ions, which have similar values for hydroxides, was used to obtain stoichiometric and homogeneous nickel ferrite precursors. The influence of the reaction parameters on the purity of the nickel ferrite phase and the size of the particles was studied using the experimental design technique. Spherical nanoparticles 15.9 ± 1.1 nm in diameter were produced under the optimal conditions identified. Based on the obtained material, NiFe2O4/ZnO magnetic composites of different quantitative compositions were prepared. The photocatalytic activity of the hybrid structures was demonstrated by photodegradation of crystal violet dye.

Investigation of adsorption potential of waste jewelry meerschaum powder for Cu(II) and cationic dye

The presence of heavy metals and pollutant dyes can have detrimental effects on aquatic ecosystems and compromise aquatic aesthetics. This study investigates the use of unprocessed waste gem meerschaum powder as a new adsorbent in the removal of both Cu(II) and methylene blue (MB) from aqueous solutions to reduce water pollution. The structure of the waste powder was characterized by FT-IR, XRD, SEM and BET methods. Optimization of Cu(II) and MB dye removal was carried out using design of experiment technique. Under optimum conditions, remarkable removal efficiencies of 95.5% (± 3.7) for Cu(II) and 97.8% (± 0.4) for MB were achieved. The removal of Cu(II) followed the Freundlich isotherm model, while the removal of MB dye adhered to the Langmuir isotherm model. Both adsorption processes obeyed the pseudo-second-order kinetic model and occurred spontaneously. This innovative approach offers a promising solution to water pollution by highlighting the importance of sustainable and cost-effective waste use.

Optimization of Yucca filamentosa fiber based graft copolymer through response surface methodology and evaluation of physico-chemical properties

The study of the physico-chemical modification of Yucca filamentosa (Yf) natural fiber by graft copolymerization with ethylmethacrylate using ferrous ammonium sulfate-potassium persulfate as a redox initiator has been reported in the article. Initially, six process parameters; reaction duration, reaction temperature, solvent amount, pH, FAS:KPS ratio, and monomer concentration were used in the study in a sequential experimental design technique, and the significant process variables affecting the yield of the graft copolymer were identified. The Resolution-V design method identified the significant parameters as the reaction temperature, amount of solvent, and the concentration of monomer. In second phase of the study, the screened variables were utilized in the development of a model through the technique of response surface methodology (RSM) for the prediction of the yields, and its optimization. The developed RSM model fitted well with the experimental data, and predicted for the optimal conditions of reactions as temperature 50 °C, solvent 100 ml, and the monomer 3.05 × 10−3 mol/L; at which the highest graft yield percentage obtained was 124.2%. The techniques of FTIR, SEM, and XRD were used for the characterization graft copolymers. Studies of the various physico-chemical properties showed that the produced graft copolymers were more resistant than the natural fibers.

Understanding the column and batch adsorption mechanism of pesticide 2,4,5-T utilizing alginate-biomass hydrogel capsule: A computational and economic investigation

Water pollution has remained a pressing concern in recent years, presenting multifaceted challenges in search of effective mitigation strategies. Our study, which targets mitigating pollution caused by 2,4,5-trichlorophenoxyacetic acid (2,4,5-T), a significant aquatic pollutant, is innovative in its approach. We have identified adsorption as a promising, cost-effective method for its removal. Our research strategy involves dynamic adsorption utilizing a peristaltic pump and composite beads containing activated carbon and sodium alginate (CA/Alg), a novel combination that mimics industrial processes. To optimize column adsorption, we examine bead stability under varied pH conditions and optimize parameters such as concentration, adsorption time, and pH through batch adsorption experiments, employing experimental design techniques. Additionally, we optimize column adsorption factors, including bead height, circulation time, and flow rate, crucial for process efficiency, and under these optimum conditions (C2,4,5-T = 80 ppm. pH = 2, t = 27h30min, H = 30 cm and D = 0.5 mL/min) the capacity of adsorption equal to 748.25 mg/g. Characterization techniques like SEM, EDX, BET analysis, XRD, and FTIR provide insights into the morphology, composition, surface area (331 m2/g), pore volume (0.11 cm3/g), crystal structure, and functional groups of the CA-P/Alg adsorbent. Theoretical analysis elucidates the adsorption mechanism and interaction with pollutants. Economic analysis, encompassing CAPEX and OPEX estimation, evaluates the feasibility of implementing this cleanup method at an industrial scale, considering initial investment and ongoing operational costs, indicating potential savings of 64 % compared with the activated carbon normally used on the Moroccan market. This comprehensive and innovative approach addresses water pollution challenges effectively while ensuring economic viability for industry-scale implementation.

Designing optimal experiments in metabolomics.

Metabolomics data is often complex due to the high number of metabolites, chemical diversity, and dependence on sample preparation. This makes it challenging to detect significant differences between factor levels and to obtain accurate and reliable data. To address these challenges, the use of Design of Experiments (DoE) techniques in the setup of metabolomic experiments is crucial. DoE techniques can be used to optimize the experimental design space, ensuring that the maximum amount of information is obtained from a limited sample space. This review aims at providing a baseline workflow for applying DoE when generating metabolomics data. The review provides insights into the theory of DoE. The review showcases the theory being put into practice by highlighting different examples DoE being applied in metabolomics throughout the literature, considering both targeted and untargeted metabolomic studies in which the data was acquired using both nuclear magnetic resonance (NMR)spectroscopy and mass spectrometry techniques. In addition, the review presents DoE concepts not currently being applied in metabolomics, highlighting these as potential future prospects.

Quality managment and labor productivity of formal companies in Perú: A non – experimental design and causal machine learning techniques

Quality managment and labor productivity of formal companies in Perú: A non – experimental design and causal machine learning techniques

Optimizing Sr-doped CaSiO3 synthesis for enhanced bioceramic properties: A Plackett–Burman approach

Doping Sr has the potential to enhance bioceramic properties. However, controlling purity, Sr concentration, and properties is crucial. Therefore, precise evaluation of synthesis variables is essential, achieved through experimental design techniques. This study synthesized Sr-doped calcium silicate wollastonite using a Plackett–Burman design with 11 variables to identify key factors affecting synthesis yield and physical, structural, and chemical attributes for tissue engineering. Results indicate that the strontium proportion significantly impacts material properties, affecting porosity, surface area, and pore volume. Moreover, observations reveal that calcination temperature contributes to heightened crystallinity and strontium doping. Adjusting calcium and strontium precursor concentrations while reducing the silicon to calcium and strontium ratio improves efficiency crystallinity, purity, and promotes the formation of the preferred β-CaSiO3 phase. Overall, consistent and reproducible findings provide valuable insights into the variables influencing the process and facilitate optimization for future ceramic synthesis.

Unveiling influential factors in the capital asset pricing model: A novel approach utilizing Taguchi method

This study uses a Design of Experiment (DOE) approach and the Taguchi method to examine how different factors affect the expected return within the framework of the CAPM. The Capital Asset Pricing Model (CAPM) is a financial framework that forecasts an investment’s expected return by taking into account its systematic risk. This model factors elements like the risk-free rate, market risk premium, and beta to ascertain the expected return for a given asset. Typically, historical market data and financial analysis provide the inputs for CAPM. This method, frequently used in manufacturing and engineering, is modified for use in the financial domain to evaluate the importance of variables like beta, market risk premium, and the risk-free rate. The research identifies the factors that influence investment performance and shows how much each factor contributes to the expected returns. It is possible to ascertain each factor's percentage contribution to the expected return through statistical analysis. This work improves knowledge of CAPM and creates opportunities for improving investment decision-making processes by bridging the gap between financial theory and experimental design. Financial practitioners can achieve more reliable and accurate investment return forecasts by incorporating DOE techniques into their existing analytical toolkit.

Using Response Surface Methodology Approach to Modeling and Optimization of the Combustion Process in Cement Kiln

AbstractIn this study, this process has been modeled and optimized using the design of experiment technique and response surface methodology (RSM). The RSM was employed to optimize the combustion process in the cement kiln. Kiln feed (X1 = 160–190 t h−1), fan speed (X2 = 840–870 rpm), kiln rotational speed (X3 = 2.8–3.4 rpm), kiln fuel rate (X4 = 3800–4150 L h−1), calciner fuel rate (X5 = 5700–6200 L h−1), pressure of kiln inlet (X6 = 98–102 Pa), and limestone saturation factor (LSF) (X7 = 94–95.8 %) as independent variables were investigated. The carbon monoxide and the kiln body temperature in RSM were considered as response variables. The absolute average error in the model predictions for the combustion process in the cement kiln, for the kiln body's temperature, is about 1.6 % and for carbon monoxide is about 15.5 %.

Development of a mathematical model for the prediction of drill quality in voice-activated ultrasonic vibration assisted drilling process.

In different industries, drilling quality play an important role in the success of operations by producing millions of quality drilled parts. The production of high-quality parts at the lowest possible cost to the manufacturer is the primary goal of contemporary process and assembly industries. To assess the quality index of drilling holes; circularity index and surface roughness are important metrics. In order to produce better drilled parts at lower prices, this study work attempts to determine the key process variables using voice acting modules to investigate the induced vibrations effects that have the greatest impact on surface roughness and circularity index in a vertical CNC drilling machine. In order to forecast the surface roughness and circularity index of drilled holes within a given operational range, a mathematical model has been proposed by adopting a coupled algorithm using design expert and statistical approach. As an experimental design technique, Response Surface Methodology with small central composite design is used to enable the evaluation of various process parameters related with machining and vibration parameters and their effects on surface roughness and circularity index. The created model is improved using statistical tools to ensure the best match to the experimental data. It has been observed with the increase of RPM and vibration frequency the surface roughness increases up to a certain limit and then decrease whereas with the increase feed the surface roughness increases. But in terms of circularity index with the increase of the process parameters the plotted graph trends under perturbation analysis shows the similar pattern CI decreases up to a certain limit and then increases.

Enhancing Mycosporine-Like Amino Acids (MAAs) Extraction Yield from Fischerella sp. F5 using Experimental Design

Background: Mycosporine-like amino acids (MAAs) are ultraviolet-absorbing metabolites found in cyanobacteria, fungi, algae, and animals, known for their antioxidant and anti-inflammatory properties. Objectives: This study aimed to optimize MAAs extraction from Fischerella sp. F5 using experimental design techniques. Methods: The central composite design was employed to determine the optimal levels of five factors: Total extraction time, temperature, sonication time, methanol ratio, and solvent volume. Each run involved extracting 100 mg of dried biomass under the specified conditions. Mycosporine-like amino acids content was quantified using high performance liquid chromatography (HPLC), and the antioxidant activity of the extract was assessed using the DPPH radical scavenging test. The best model was selected based on the highest R2 value, and the optimized extraction conditions were determined. Results: Mycosporine-like amino acids content in the final extracts ranged from 27.7% to 89%, with antioxidant activities ranging from 15% to 59.3%. Overall, all factors showed a positive correlation with MAAs content in the final extract. However, no significant model was established to correlate extraction conditions with MAAs purity ratio (MAA%) or extract antioxidant activity. Conclusions: The bioactivity of partially purified MAAs may be influenced by factors such as associated impurities extracted alongside MAAs, which were not investigated in this study. Further research is necessary to identify the exact factors affecting MAAs extraction yield and purity, aiming to obtain partially purified MAAs extracts with optimal bioactivity.