Statistical Optimization Techniques Research Articles

Fitting Epidemic Models to Data: A Tutorial in Memory of Fred Brauer.

Fred Brauer was an eminent mathematician who studied dynamical systems, especially differential equations. He made many contributions to mathematical epidemiology, a field that is strongly connected to data, but he always chose to avoid data analysis. Nevertheless, he recognized that fitting models to data is usually necessary when attempting to apply infectious disease transmission models to real public health problems. He was curious to know how one goes about fitting dynamical models to data, and why it can be hard. Initially in response to Fred's questions, we developed a user-friendly R package, fitode, that facilitates fitting ordinary differential equations to observed time series. Here, we use this package to provide a brief tutorial introduction to fitting compartmental epidemic models to a single observed time series. We assume that, like Fred, the reader is familiar with dynamical systems from a mathematical perspective, but has limited experience with statistical methodology or optimization techniques.

A tandem approach for precipitant-free highly selective recovery of valuable metals from end-of-life lithium-ion batteries using a green deep eutectic solvent

Recently, recycling lithium-ion batteries (LIBs) waste and the efficient recovery of valuable metals has become crucial for the circular economy. Deep eutectic solvents (DESs) have shown immense potential for metal extraction from end-of-life (EoL) LIBs. However, a complicated subsequent separation process is required due to the non-selectivity of the DES components for metal. Herein, we propose a tandem precipitant-free hydrometallurgical process for the selective recovery of Li and other valuable metals (Ni, Co, Mn) from EoL LIBs using a green DES. The process parameters, viz., reaction temperature (T), time (t), pulp density (S/L), and molar ratio (MR) of ethylene glycol to tartaric acid, were optimized via the statistical optimization technique of response surface methodology. The results revealed maximum extraction efficiencies of 99.2 ± 0.5%, 96.1 ± 1.4%, 95.2 ± 1.2%, and 97.8 ± 1.5% for Li, Ni, Co, and Mn, respectively, under the optimized process conditions of T = 118 °C, t = 17 min, S/L = 42 g/L, and MR = 3:1. The kinetic study showed that interfacial reaction governed the extraction process. A plausible mechanism depicting the role of DES constituents in synergistically facilitating the extraction reaction was elucidated. Moreover, Li and other metals (Ni, Co, and Mn) were selectively separated and recovered with an overall efficiency of ∼99%. The reusability of the recovered metal products and the DES were also assessed for a closed-loop recycling process. At the outset, the proposed process paves the way for a greener alternative for recycling valuable metals from EoL LIBs rather than conventional acid-based leaching processes.

Advancing wavefront shaping with resonant nonlocal metasurfaces: beyond the limitations of lookup tables

Resonant metasurfaces are of paramount importance in addressing the growing demand for reduced thickness and complexity, while ensuring high optical efficiency. This becomes particularly crucial in overcoming fabrication challenges associated with high aspect ratio structures, thereby enabling seamless integration of metasurfaces with electronic components at an advanced level. However, traditional design approaches relying on lookup tables and local field approximations often fail to achieve optimal performance, especially for nonlocal resonant metasurfaces. In this study, we investigate the use of statistical learning optimization techniques for nonlocal resonant metasurfaces, with a specific emphasis on the role of near-field coupling in wavefront shaping beyond single unit cell simulations. Our study achieves significant advancements in the design theoretical conception of resonant metasurfaces. For transmission-based metasurfaces, a beam steering design outperforms the classical design by achieving an impressive efficiency of 80% compared to the previous 23%. Additionally, our optimized extended depth-of-focus (EDOF) metalens yields a remarkable five-fold increase in focal depth, a four-fold enhancement in focusing power compared to conventional designs and an optical resolution superior to 600 cycle/mm across the focus region. Moreover, our study demonstrates remarkable performance with a wavelength-selected beam steering metagrating in reflection, achieving exceptional efficiency surpassing 85%. This far outperforms classical gradient phase distribution approaches, emphasizing the immense potential for groundbreaking applications in the field of resonant metasurfaces.

Optimization for nutritional fortification of wheat–millet composite flour mixture by response surface methodology

This study intends to optimize the production and evaluation of the nutritional quality of composite flour comprising wheat and millet. The experimental design for the composite flour was laid down using the I-optimal mixture design of response surface methodology (RSM). With an extensive literature survey, wheat, finger millet, barnyard millet, and foxtail millet were selected to understand the health benefits of wheat flour. A total of 24 flour blends were prepared by incorporating wheat flour (40–85%), finger millet flour (5–20%), barnyard millet flour (5–20%), and foxtail millet (5–20%) as independent variables; however, the responses were protein, antinutrient (Tannin), and antioxidants (1,1-diphenyl 2-picryl hydrazyl). The statistical and numerical optimization technique of RSM was used to optimize multiple responses simultaneously. After optimization, eight distinct solutions were obtained using the software. Out of the eight solutions, the best optimum level of fortified mixture was selected and wheat (51.09%), finger millet (20%), barnyard millet (8.91%), and foxtail millet (20%) with protein content (23.19 mg g–1), antinutrient content (9.07 mg g–1), and antioxidant activity (61.52%), along with the highest desirability (0.845), were found to be the best. The optimized mixed flour obtained from wheat and millet blends had superior nutritional properties, making it an appropriate choice for consumers with dietary requirements, particularly those allergic to gluten. Furthermore, the optimized wheat–millet flour is suitable for large-scale production and can be used to improve consumers’ nutrient delivery and protein requirements. The study optimized composite flour through mixture design application and highlighted the applicability of RSM in the food industry. The outcome indicates that fortified flour finds its applicability in the food industry, particularly for products such as biscuits, noodles, pasta, and other bakery items.

Optimizing Piezoresistive MEMS Pressure Sensor on a Double Cross Beam Silicon Diaphragm With Statistical Curve-Fitting and Optimization Techniques

Optimizing Piezoresistive MEMS Pressure Sensor on a Double Cross Beam Silicon Diaphragm With Statistical Curve-Fitting and Optimization Techniques

The Application of Response Surface Methodology (RSM) In the Computational Optimization of Sustained Release (SR) For Phenothiazine Derivative Matrix Tablet

Introduction: This study explores the use of Response Surface Methodology (RSM), a statistical optimization technique, to optimize the SR properties of prochlorperazine maleate (PCM) matrix tablets. PCM is a phenothiazine derivative used for treating schizophrenia, nausea, and vomiting. Sustained-release formulations offer extended drug delivery, potentially improving patient compliance and reducing side effects. RSM helps identify optimal combinations of critical formulation factors influencing drug release, such as polymer type and concentration, filler type, and drug/polymer ratio. The study likely involves designing experiments based on chosen RSM designs (e.g., Box-Behnken) with varying factor levels. Formulate SR tablets with different factor combinations. Evaluating the drug release profiles of each tablet formulation. Analyzing data using RSM software to build mathematical models relating factors to drug release and identifying optimal factor combinations that maximize desired release characteristics. Objective: The ongoing research purpose to improve the advancement of a sustained release tablet containing Phenothiazine derivative PCM loaded matrix. This is achieved by utilizing DoE as a computational method to statistically validate the formulation. Methodology: Basically two methods involved in the formulation using direct compression; a simple and efficient method where all dry ingredients are blended and directly compressed into tablets. Results: The obtained outcomes closely matched the anticipated values derived from the experimental setup. The enhanced PCM matrix tablets demonstrated a prolonged and uniform discharge of PCM over a span of 6 hours. Conclusion: This study Based on a 23 FD, response surface methodology was used to successfully develop the current research of PCM matrix tablets for sustained release application. The corresponding contour plots and 3D response methodology represent the important variables for the optimisation process.

Toward the optimization of fuel consumption on container-on-barge operations in river transportation

The national policy for fuel prices in Colombia and the management of the fluvial route of the Magdalena River has led to effects on productivity and benefits for companies and maritime ports due to contracts for dredging that guarantee drafts for six months. Moreover, increases in fuel prices have generated higher operating costs than those of previous years. Data analysis is a crucial decision-making tool for operational-level decision-makers in companies, supported by the measures of indicators, competitiveness, and productivity in terminal services. The transportation and shipping of cargo entail significant costs in terms of fuel, labor, and time, as well as other costs that must be met for meeting customer commitments.This paper presents a methodology for fuel consumption optimization in the specific case of transportation by a Colombian river transportation company, using regression models and response-surface methodology as optimization tools. This approach yields significant results, reducing fuel consumption by approximately 25% through a combination of various factors involved in barge transportation operations, including labor, fuel, velocity, weight, and other operational aspects. The primary contribution of this research lies in its promotion of sustainable operations through cost reduction and the reduction of fuel consumption reduction. The use of statistical optimization techniques makes this methodology applicable to similar cases of shipping and transportation. It additionally addresses national policy changes related to emissions, fuel costs, and sustainability, thereby aligning with a focus on energy transition to promote cost reduction.

Selecting an optimum kinetic model for gas–solid adsorption based on statistical approaches and a model selection criterion

Adsorption heat transformation (AHT) techniques can help to safeguard the environment by reducing the use of fossil fuels. When constructing an adsorption-based dynamic heat pump system, analyzing the kinetic characteristics of the adsorbate–adsorbent pair is important. The recently established time-adapted linear driving force (LDF) and commonly used models such as LDF, modified LDF, and Fickian diffusion (FD) models are available in the literature for analyzing the kinetic characteristics, making it challenging for researchers to select the optimum one for simulating the complete real system. This study applies a non-parametric statistical approach known as bootstrapping for simulating the replica of experimental kinetics data for all studied pairs. The kinetics models mentioned above are used to correlate the bootstrap samples. The estimated values of the statistical parameters are compared, and the optimum kinetics model for gas–solid physical adsorption is proposed. The parameters of the model are estimated using the generalized reduced gradient (GRG) non-linear optimization method. For each bootstrap sample, the optimum model is selected based on the minimum values, which means less information loss, of a set of information-based model selection criteria (ICs). The time-adapted LDF incurs smaller values of IC compared to other kinetics models. The p-value of the proportion test for water adsorption onto AQSOA-Z01, RD silica gel, and Al-Fum is very small, which is less than 0.01. So, the test is significant at a 1% level, which implies that time adapted LDF model is significantly optimum. This is the first time that a rigorous statistical optimization technique has been used to identify the optimum kinetic model for gas–solid adsorption. The current findings are critical for a comprehensive analysis of an adsorption system and its design.

Process optimization for textile industry-based wastewater treatment via ultrasonic-assisted electrochemical processing

The treatment of industrial wastewater at a reasonable cost and in a sustainable approach is a major challenge. The primary objective of this study is to perform textile wastewater treatment by exploring the ultrasonic-assisted electrochemical (UEC) process with parameter optimization. Statistical modeling has been carried out in order to investigate the influence of operating parameters, including pH (4–10), current density (5–20 mA/cm2), electrolysis time (10–30 min), and ultrasonic power (0–500 watt) on color, chemical oxygen demand (COD), and turbidity removal efficiency. The combination of response surface methodology (RSM) and artificial neural network (ANN) based statistical models are applied to predict and examine the reliance of the operating parameters on removal efficiencies. The results validate that at optimal operating condition (pH 7.3, current density 15.7 mA/cm2, processing duration 21.5 min, and ultrasonic power 420 W), the actual removal efficiencies of color, COD, and turbidity was 97%, 66%, and 79% respectively. The central composite design (CCD) based RSM model predicts the removal efficiencies of color, COD, and turbidity was 93.9 %, 63.6 %, and 77.1 %, respectively. In contrast, the ANN model predicts removal efficiencies of color, COD, and turbidity of 99.3%, 82.1%, and 78.7%, respectively at optimized operating conditions. The results indicated that the ANN model had a greater coefficient of determination (R2) value and smaller mean square error (MSE) than RSM model, showing that ANN is preferable at forecasting response, while RSM accurately predicts impacts of operating parameters. The present manufacturing process with statistical optimization techniques may be envisioned for the large-scale treatment of industrial wastewater.

Improvement of ultrasound-assisted extraction of polyphenolic content of the plant Cytisus triflorus L'Her as a natural resource using artificial neural network modeling and multi-objective optimization

The improvement of environmental performance of production processes is a critical problem right now. Processes that are effective in terms of productivity and environmental effects are the center of interest of researchers. This study established such a method for an ultrasound-assisted extraction procedure. The plant Cytisus triflorus L'Her was used to extract polyphenolic chemicals using ultrasound-assisted extraction as a quick, less energy consuming, high-yield technology. This work offers a new experimental planning approach using Artificial Neural Networks, that enables the extraction process to be optimized using multi-objective optimization in order to identify the best extraction conditions to obtain the maximum polyphenolic compounds possible. Advanced modeling using Artificial intelligence and statistical optimization techniques was used to determine the best combinations of ethanol concentration, extraction temperature and time for the maximum extraction of total phenols, total flavonoids, total flavonols, and total tannins. An artificial neural network (ANN) was used to build the models for each of the four goals, then they were multi-objectively optimized using a genetic algorithm technique. The maximum polyphenolic contents including TPC (4.986 ± 0.006 mg GA/g DE), TFC (14.026 ± 0.070 mg Q/g DE), TFolC (5.225 ± 0.010 mg R/g DE), and TTC (6.240 ± 0.021 mg C/g DE) were obtained using an extraction process under optimal conditions, which included a solvent concentration of 78%, an extraction temperature of 52 °C, and an extraction time of 55 min which were obtained using the MOO Pareto optimum solution selection approach Gray Relational Analysis GRA.

Design of experiment for hydrogen production from ethanol reforming: A state-of-the-art review

Hydrogen production from bioethanol has garnered significant research attention due to its renewability, sustainability, and net zero emission. This research aims to review two statistical optimization techniques, response surface methodology (RSM) and the Taguchi method, for hydrogen production from ethanol thermochemical conversion. The RSM model demonstrated that temperature increases hydrogen production, which peaked between 500 °C and 600 °C for ethanol steam reformation (ESR) and >700 °C for ethanol autothermal reforming (ATR) processes. Maximum hydrogen synthesis occurs at steam-to-ethanol (S/E) ratios of 3–5 mol.mol−1 for both ethanol steam and autothermal reforming. Adding oxygen, a characteristic parameter of autothermal reforming, reduces hydrogen production. Ethanol autothermal reforming may be less efficient than ethanol steam reforming for hydrogen production. The impacting parameters for ethanol reforming identified by Taguchi techniques are steam-to-carbon ratio, ethanol steam reforming temperature, and water–gas shift reaction temperature, where steam-to-carbon ratio and ethanol steam reforming regulate hydrogen production substantially. The Taguchi approach reveals that an ethanol flow rate of 2 cm3.min−1, a steam-to-carbon ratio of 5, and an ethanol steam reforming temperature of 500 °C are optimal reaction conditions. Optimization strategies improve biohydrogen production and make the following reaction more precise. For example, only optimization approaches can determine if a parameter should be reinforced or lowered.

Optimization of process parameter in AI7075 turning using grey relational, desirability function and metaheuristics

ABSTRACT Even though metal may be effectively shaped by a variety of other manufacturing techniques, machining continues to play a significant role in industries. Turning is a conventional chip-forming operation that removes undesirable or surplus material from a cylindrical workpiece. The major objective of process optimization in turning operation research is focusing on the development of statistical modeling and optimization techniques for boosting production rate, lowering costs, and reducing product rejection. The current work aims to improve the CNC turning process of Al 7075 using coated carbide inserts. This investigation uses grey relational analysis, desirability function analysis, Multi-objective Genetic Algorithm (MOGA) and Multi-objective Genetic Particle Swarm Optimization (MOPSO) to optimize factors to minimize responses like surface roughness and cutting force. Comparative evaluation of results of optimum input parameter sets results in close agreement, both in traditional optimization and metaheuristic-based optimization. In particular, MOGA is found to be more efficient to solve this stated multi-criterion decision-making problem as compared to other methods.

Hyperbolic matrix factorization improves prediction of drug-target associations

Past research in computational systems biology has focused more on the development and applications of advanced statistical and numerical optimization techniques and much less on understanding the geometry of the biological space. By representing biological entities as points in a low dimensional Euclidean space, state-of-the-art methods for drug-target interaction (DTI) prediction implicitly assume the flat geometry of the biological space. In contrast, recent theoretical studies suggest that biological systems exhibit tree-like topology with a high degree of clustering. As a consequence, embedding a biological system in a flat space leads to distortion of distances between biological objects. Here, we present a novel matrix factorization methodology for drug-target interaction prediction that uses hyperbolic space as the latent biological space. When benchmarked against classical, Euclidean methods, hyperbolic matrix factorization exhibits superior accuracy while lowering embedding dimension by an order of magnitude. We see this as additional evidence that the hyperbolic geometry underpins large biological networks.

Remarkable improvement of levansucrase production from a newly isolated Aspergillus niger MK788296 strain using agro-industrial wastes through statistical optimization techniques

Background and objectiveMicrobial levansucrase (LS) is a good source for the production of biologically active fructo-oligosaccharides and levan, which have diverse applications in pharmaceutical industries. Therefore, recent studies have focused on the enhancement of LS production through searching for potent microbial producers and optimization of the fermentation conditions. The present study aimed to use agro-industrial waste as a cost-effective carbon source for LS production and maximize the enzyme yield by optimization of the cultural conditions.Materials and methodsA potent fungal producer of LS was isolated from an Egyptian soil sample that was collected from Giza Governorate at a depth of 5 cm and identified based on internal transcribed spacer identification and then submitted to the gene bank database. The production of LS by the isolated strain was optimized by evaluating the best fermentation state and agro-industrial waste to be used in the fermentation process. After that, further optimization of culture medium composition was established by two statistical designs: the Plackett–Burman design followed by central composite design.Results and conclusionThe isolated strain was identified asAspergillus nigerMK788296. The first optimization approach declared that using the submerged fermentation technique and utilizing potato peels as the main carbon source led to a 2.4-fold increase in LS production. The statistical optimization resulted in a massive LS production (18870.3 U/ml) with a 59.4-fold increase in enzyme yield than the nonoptimized culture conditions.

Comparative analysis of variation in macroscopic design process parameters of triboelectric nanogenerators

Triboelectric Nanogenerators (TENGs) are novel energy harvesting devices capable of harvesting energy from ambient surroundings by the contact-induced triboelectric charge transfer across two dissimilar materials attached to separate electrode plates. These charges get transferred due to the relative motion between the two plates. The use of TENGs in various applications ranging from self-powered sensors to large-scale energy generation plants has stirred up tremendous interest in favour of TENGs. Though numerous types of modifications have been proposed at the microscopic level, there have been limited studies into the effect of macroscopic process parameters on the output generated. This paper aims to study the effect of macroscopic parameters such as area, thickness, and load–frequency on the open-circuit voltage and short-circuit current. A pneumatic piston set-up capable of emulating the vertical contact separation between two triboelectric materials polytetrafluoroethylene (PTFE) and Kapton (poly (4,4′-oxydiphenylene-pyromellitimide)) was fabricated. The results are analysed using Taguchi's (L16) statistical optimization technique and Analysis of variance method (ANOVA), and also the best combination of factors and levels are predicted. Based on the analysis, it is predicted that the TENG combination with 81 cm2 area, 4.5 Hz frequency, and thickness of 0.2 mm yields the highest output. In conclusion, the Taguchi optimization technique can be used to predict the optimal combination of the macro-level parameters to maximize the current and voltage generated according to the specific application of TENG.

Recent Advances and Technologies in Chitinase Production Under Solid-State Fermentation

ABSTRACT: Our target is to evaluate recent literature on chitinase production from different sources via solid-state fermentation and to analyze several strategies to improve chitinase production via solid-state fermentation. Plant pathogen biocontrol, sequential transformation of chitin into bioactive molecules such as chito-oligosaccharides and N-acetylglucosamine, protoplast synthesis from filamentous fungi, and single-cell protein production are some of the applications for chitinase. Despite their enormous biological importance, chitinases have received little commercial importance due to the smaller percentage of microbes with high efficiencies, the enzymes' decreased activity and consistency, and the cost of production. Solid-state fermentation (SSF) is less expensive, requires fewer vessels, uses less water, requires fewer wastewater treatments, produces a greater product yield, has a lower risk of bacterial contamination, and requires less energy expenditure. Despite its higher productivity and lower cost, the SSF technique is now mostly limited to lab scales. Furthermore, the crude SSF products can be used as an enzyme source for biotransformation. There are many findings on different microorganisms that produce chitinase by SSF. So it is very critical to isolate new organisms for such production. So we assessed the traditional approach to medium optimization, which focuses on changing one factor at a time while leaving the others constant, and statistical optimization techniques such as response surface methodology (RSM), artificial neural networks (ANNs), and genetic algorithms (GA).

Intensification of autocatalytic methyl oleate synthesis in continuous flow rotating recycle reactor under hybrid radiation: Process optimization and Scale-up

Intensification of the autocatalytic esterification of methanol with oleic acid for producing methyl oleate (biodiesel) was accomplished using a continuous flow rotating reactor under recycle mode. To exaggerate the biodiesel yield, highly efficient hybrid electromagnetic radiation (near-infrared and microwave irradiation) was used as an energy source. Spherical-shaped nano-zinc-titanate photocatalyst (low band-gap energy: 2.01 eV and low recombination rate) was prepared and utilized in the aforementioned novel reactor. Box-Behnken statistical optimization technique was applied to attain the optimum condition (catalyst concentration 30 wt%, reaction temperature 333 K, methanol: oleic acid mole ratio 11:1, LHSV: 0.20 min−1) resulting in maximum biodiesel yield. Moreover, at optimum rotating speed (∼235 rpm), high external and internal mass transfer coefficients were achieved which rendered a remarkably augmented reaction rate under ideal reactor behavior (Dispersion number: 6.42 × 10−7). A geometric-based COMSOL model was simulated which confirmed uniformity of hybrid irradiation and temperature distribution within the rotating catalytic packed bed; which was applied for the development of a parallel-autocatalytic reaction kinetic model. Langmuir Hinshelwood kinetics of the parallel autocatalytic esterification is well represented in the experimental data (RSSQ=1.52×10-7). Without recycle stream, the reaction exhibited a non-autocatalytic reaction with a lower yield of 79.04 % biodiesel within 0.20 min−1 LHSV, whereas, under recycle mode, the biodiesel yield was increased up to 93.55 % even at a higher LHSV of 0.25 min−1. For larger-scale production of the methyl oleate, the ASPEN PLUS simulator has been deployed for a throughput scale-up factor of 1000 (geometric similarity), which corroborates well with lab-scale yield/reactor performance.

Enhancing the Surface Quality of FDM Processed Flapping Wing Micro Mechanism Assembly through RSM–TOPSIS Hybrid Approach

Improving surface quality attributes is a critical task in the production of micro-sized near-net-shaped components for end-use applications using additive manufacturing techniques. In the present study, we investigated the effect of fused deposition modeling (FDM) process parameters such as layer thickness, part orientation, raster width and raster angle on the surface quality characteristics of as-fabricated test specimens in order to develop the assembly of a flapping wing micro mechanism. Through a Box–Behnken design, a suitable experimental strategy was developed, and test specimens were manufactured. The performance of the experiments was statistically assessed using multi-response analysis of variance (ANOVA). The microstructures of the test specimens produced with various processing parameters were examined using a scanning electron microscope to identify micro surface flaws under various processing conditions. Furthermore, the optimal FDM parameters for improved surface quality attributes such as Ra, Rz and Rq were obtained using a statistical optimization technique known as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

Critical review of bio‐inspired data optimization techniques: An image steganalysis perspective

AbstractImage steganalysis involves the discovery of secret information embedded in an image. The common method is blind image steganalysis, which is a two‐class classification problem. Blind steganalysis extracts all possible feature variations in an image due to embedding, select the most appropriate feature data, and then classifies the image. The dimensionality of the extracted image features are high and demand data reduction to identify the most relevant features and to aid accurate classification of an image. The classification is under two classes namely, clean (cover) image and stego (image with embedded secret data) image. Since the classification accuracy depends on selection of most appropriate features, opting for the best data reduction or data optimization algorithms becomes a prime requisite. Research shows that most of the statistical optimization techniques converge to local minima and lead to less classification accuracy as compared to bio‐inspired methods. Bio‐inspired optimization methods obtain improved classification accuracy by reducing the high‐dimensional image features. These methods start with an initial population and then optimize them in steps till a global optimal point is reached. Examples of such methods include Ant Lion Optimization (ALO), Fire Fly Algorithm (FFA), and literature shows around 54 such algorithms. Bio‐inspired optimization has been applied in various fields of design optimization and is novel to image steganalysis. This article analyses the various bio‐inspired optimization techniques and their accuracy in image steganalysis pertaining to the discovery of embedded information in both JPEG and spatial domain steganalysis.This article is categorized under: Technologies > Classification Technologies > Computational Intelligence Technologies > Artificial Intelligence

Parametric optimization of durable sheeting fabric using Taguchi Grey Relational Analysis

ObjectivesThe present work describes the application of Taguchi Grey Relational Analysis in order to optimize the durability of sheeting fabrics. The existing study investigates the multi-response optimization of certain parameters of yarn process including FL, RS and RT on tensile strength, tear strength and abrasion resistance of durable fabric sheets after washing. MethodsIn the development process L9 orthogonal array in Taguchi design was used. The results have been analyzed by using statistical multi-response optimization technique, grey relational analysis to set the process parameters and to decide the simultaneous optimization of responses including tensile strength, tear strength and abrasion resistance. In addition, the analysis of variance (ANOVA) was used to determine the most significant factors. Reference sheeting fabric with same construction parameters was also tested. ResultsThe results show a greater improvement in parameters of developed samples quality. The experimental results show parameter fiber length has the most significant effect on the multiple performance characteristics. ConclusionsThe developed fabric sheet having durable life would help to reduce consumption of resources of both consumers and manufacturers. Manufacturers can have great chance to establish their image and can increase the profit margin over products. Therefore, the integration of grey relational analysis and the Taguchi Method can be applicable for the optimization of process parameters and help to conserve resources by extending the lifetime of sheeting fabrics.