Combination Of Process Variables Research Articles

Antimicrobial Effect of Response Surface Optimized Pyocyanin Produced by Pseudomonas aeruginosa

Pseudomonas aeruginosa is notable for its natural bluish-green pigment production, known as pyocyanin. Researchers have credited pyocyanin as an antimicrobial agent, in addition to its other biomedical applications. This study focused on isolating Pseudomonas aeruginosa from the local environment with the ability to synthesize pyocyanin and optimize its cultural conditions for improved yield. A total of thirty-one experimental combinations of process variables using the Central Composite Design (CCD) of Response Surface Methodology (RSM) was ran to find the best conditions for the organism to make pigments. Of the fermentation media evaluated, nutrient broth supplemented with maltose was the best fermentation medium for the organism’s pyocyanin production. A regression model described the interaction between the test variables and the pyocyanin yield with an R2 value of 87.52%, indicating that the model had a satisfactory fitness level. The determined optimal conditions include a maltose concentration of 18 g/L, a pH of 6.8, an inoculum size of 2.3 mL, and an agitation speed of 120 rpm. The optimized condition resulted in a 4.12-fold increase in pyocyanin yield compared to an unoptimized condition. Pyocyanin exhibits high antimicrobial activity compared to the conventional antibiotics assessed against E. coli and Candida albicans. Our study’s findings suggest that statistical models can improve pyocyanin synthesis by Pseudomonas aeruginosa. Furthermore, this pigment has the potential to replace conventional antibiotics in the treatment of microbial infections

Development of Hybrid Optimization Model Using Grey-ANFIS-Jaya Algorithm for CNC Drilling of Aluminium Alloy

Aluminium alloys are gaining popularity in a diversity of engineering applications because of their extraordinary features such as strength, resistance to oxidation, and so on. AA5052 (Al-Mg series) is generally used in antirust uses, particularly in desalination related activities, due to its better resistance to corrosion in marine applications at temperature ranges up to 125°C, lower cost, better heat-carrying capacity, and nontoxicity of its corrosion components. Drilling is one of the most commonly adopted material removal processes that is adopted in numerous engineering uses. Taguchi’s technique is engaged to arrange and examine the tests, by treating the drilling diameter and speed and as independent process factors. The studies were carried out using an L27 orthogonal array. Material removal rate (MRR), surface roughness (SR), and form/orientation error are deemed as output characteristics. Taguchi’s analysis was engaged to discover the best process factors. ANOVA is used to examine the influence of process variables. Suitable application of artificial intelligence tools for making effective decision assists the manufacturer in accomplishing the benefits in numerous engineering domains. To obtain the maximum material removal and minimum roughness, circularity (circ), and perpendicularity errors (perp), the process variables have been optimized with the help of grey-ANFIS-amalgamated with Jaya algorithm. The multiperformance index was developed using grey theory. Statistical error analysis is used to estimate the performance of the established optimization model. Based on the investigative outcomes, the best-suited process variable combinations will be used to provide improved and enhanced multiperformance characteristics.

Dry sliding wear characteristics of Al7075 alloy‐reinforced with SiC and cenosphere particles

AbstractAluminum metal matrix composite (AMMC) is employed in all engineering applications, most notably as a substitute for conventional metals in the fields of defense, automotive, and aerospace. AMMC should have a wide variety of structural, mechanical, thermal, wear, and corrosion qualities that may require mutually exclusive features at an optimal level to achieve this. The current research focuses on the stir casting process for producing SiC and cenosphere reinforced Al7075 alloys. The response surface method's (RSM) face‐centered central composite design (CCD) was used to design the number of experimental trials, and a response surface technique was enforced to forecast the optimum combination of processing variables in the wear process. The SiC reinforcement improved the wear resistance of Al7075‐SiC‐cenosphere composites substantially. Overall, the experiments show that Al7075‐6 wt% SiC‐5 wt% cenosphere has excellent tribological properties. The hybrid Al7075/SiC/Cenosphere composites were shown to be effective, fit, and suitable in the ideal wear process parameters (load of 10 N, sliding distance of 1000 m, and sliding velocity of 1.5 m/s) for lowering wear rate (0.38035 × 10−3 mm3/m) at 6wt% SiC reinforced composite. This suggests that the combination of SiC and cenosphere improves the wear resistance of AMMC.

Optimization of an edible film formulation by incorporating carrageenan and red wine lees into fish gelatin film matrix

The study aimed to use response surface methodology (RSM) to create and understand a novel edible film made from fish gelatin (FG). This film includes wine lees (WL) and carrageenan (CAR). The concentrations of WL (0, 1, 2, and 3 %) and CAR (0, 1, and 3 %) were considered independent variables. The process variable combinations for the optimal response functions were 1.926 % WL and 3 % CAR, forming soft and rigid films with low tensile strength (TS) and high elongation at break (EAB%). Based on the evaluation of each response, FG film had the highest TS value, FG/CAR(3 %) film had the maximum EAB, and FG/WL (3 %)/CAR (3 %) film had the lowest vapor permeability (WVP) and the highest opacity (OP). The incorporation of WL considerably improved the functional properties of these films, enabling strong antioxidant activity and high phenolic content. Characterization of the films with analytical techniques: Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and X-ray diffraction (XRD) analysis demonstrated a considerable interaction between WL and FG, indicating a high level of compatibility between the two substances. Our data suggest that the formulation of edible films can be adjusted to fit the specific requirements of the design.

ANN MODELING AND OPTIMIZING THE DRILLING PROCESS PARAMETERS FOR AA5052-GLASS FIBER METAL LAMINATE USING GREY-FUZZY LOGIC APPROACH

The exterior layer of the fuselage skin structure is made of Glass-Reinforced Fiber Metal Laminates (GLARE-FMLs). Hole quality is impacted by various force components during the drilling process. The process variables of drilling are optimized to obtain quality holes by controlling such force components. Because of the very limited works on optimizing the significant input process variables in getting quality drilled holes on FML plates, the present investigation is aimed to analyze the impact of varying the significant drilling process variables referred from the literature study and the wt.% of Layered Double Hydroxide (LDH) mixed with the epoxy, on the force components generated during the drilling process. The FML plates proposed in the study have been drilled based on the test plan prepared by Taguchi’s DOE. Grey Relational Analysis has been used for discovering the best combination of process variables leading to good quality holes. Further, grey-fuzzy modeling technique has been employed to check the results of GRA. From the results, it is identified that 1000 rpm spindle speed, 20 mm/min feed rate and 4 wt.% inclusion of LDH to the epoxy resin combination within the considered range of input process variables were found to be the best input parameter combinations resulting in comparatively better output responses. Artificial Neural Network (ANN) model has been generated to bridge the input variables with the outputs measured. The fitness of the generated ANN model has been checked and reported. The effect of varying the process variables on obtaining the quality drilled holes has been revealed using Main Effect Plots (MEPs).

Mechanical behaviour of mild steel during multi-scan laser bending with forced cooling

Laser bending is an innovative sheet metal forming technique. Researchers are focusing optimizing the process for enhancing bend angles and mechanical properties. To achieve this, a waiting period between laser scans is vital to re-establish the temperature gradient and improve bend angles. Forced cooling is a critical element in reducing this waiting time. This study investigates the influence of cooling environments (natural and forced cooling) during laser bending of mild steel sheets. It examines the impact of key parameters such as laser power, scan speed, and beam diameter under both cooling conditions. The results reveal that under forced cooling, specific combinations of process variables lead to exceptional outcomes. Notably, a bend angle of 8.81° is achieved with higher laser power (1000 W) and slower scan speed (1000 mm/min) in forced cooling condition. This study also analyzes temperature variations concerning laser power, scan speed, beam diameter, and the number of scans. Furthermore, under forced cooling conditions, an increase in ultimate tensile strength and micro-hardness is observed within the irradiated region. The highest micro-hardness value of 198.5 HV, was attained under forced cooling conditions at 1000 W of laser power and a scan speed of 1000 mm/min. This research underscores the potential of forced cooling mechanisms to significantly enhance laser bending outcomes, encompassing both bend angles and material properties.

Influence of twin-screw elements on dispersion of nano-clay in vinyl ester polymer composites using Taguchi’s orthogonal array technique

AbstractThis work aims to experimentally investigate the influence of screw elements on the dispersion of Cloisite-15A in vinyl ester based on the design of experiments using MINITAB-V16 software. Experiments were designed considering two main factors such as Cloisite-15A loading (1, 2, 3, and 4 wt%) and type of screw elements with a varying number of kneading elements (type 1 with 55 mm, kneading elements and type 2 with 90 mm, kneading elements). The dispersion procedure of Cloisite-15A in vinyl ester was carried out by a combination of both ultrasonication and then twin-screw extrusion. The influence of these factors on the tensile strength and hardness was studied using an experimental layout possessing Taguchi’s L8 Orthogonal Array technique. ANOVA of the experimental results revealed the dispersion of Cloisite-15A with vinyl ester proved to be better when processed with type 2 screw elements having more kneading screws. The S/N ratio study showed that the Cloisite-15A loading had the greatest impact on the type 2 screw's tensile and hardness values for the Cloisite-15A/vinyl ester gel coats when treated at 230 rpm with 10 passes. The probability graphs led to the conclusion that, with a 95% confidence interval, all response values were distributed equally along the normal probability plot's trend. In order to achieve the best response in terms of mechanical strength, the Grey Relational Analysis (GRA) helped identify the best combination of process variables. This combination was known to be 4 wt% Cloisite-15A loading, with type 2 screw processed at 230 rpm with 10 passes. The SEM and XRD showed the absence of agglomeration and better exfoliation of Cloisite-15A in the gel coats with 4 wt% of clay loading.

Model-based optimization of multistage ultrafiltration/diafiltration for recovery of canola protein

The ultrafiltration/diafiltration (UF/DF) process is a crucial step in the canola protein isolation process from rapeseed meal. The process involves using a multi-stage membrane system to separate components of the mixture. As diafiltration dilutes the feed stream in the ultrafiltration system, a large amount of diafiltration water is required. Reducing the diafiltrate for sustainability reasons can be done by carefully selecting process variables or using recycle streams. However, finding the optimum process variables can be a meticulous process if performed experimentally or via trial and error. In this study, we performed an optimization using a mechanistic model integrated with a genetic algorithm to aid in finding an optimum combination of process variables. The mechanistic ultrafiltration model was derived by taking into account transport phenomena within the filtration system. Parameters were characterized experimentally in term of viscosity coefficient, membrane resistance, cake porosity, aggregate diameter equivalent, and material compressibility factors. Using the mechanistic model-based optimization in combination with actual experimental values, the performance of a four-stage UF/DF system could already be improved despite fixing the configuration, albeit at the cost of a reduction in purity. Further improvement could be achieved by using recycle streams. The optimized system achieved a diafiltrate reduction of up to 79%, an increase of purity of up to 31%, and an increase of dry matter content of up to 18%, while maintaining the product purity of the reference set-up.

Optimization of process parameters for degossypolisation of de-oiled cottonseed cake by response surface methodology (RSM)

The goal of this research is optimizing the process variables for removal of toxic compound gossypol from cotton seed meal by employing microwave pretreatment. A Box-Behnken Design (BBD) of response surface methodology (RSM) was employed, considering three process variables: moisture content for pretreatment (24–28 %), microwave power (300–900 W), and microwave time (2–6 min). This resulted in 17 experimental runs to evaluate the free gossypol, total gossypol, and protein content. The numerical optimization resulted in the best combination of process variables for the set of response properties included 26 % moisture content, 900 W microwave power level, and 6 min microwave time. Under the optimized conditions, the experimental values of free gossypol, total gossypol and protein content were found to be 0.047 %, 1.15 %, and 52.54 %, respectively. These values are closely related with the predicted values (free gossypol = 0.05 %, total gossypol = 1.19 % and protein content = 51.67 %) which indicates the models appropriateness. In comparison to traditionally extracted cottonseed meal, the improved microwave aided treatment reduced extraction time and solvent use. This investigation may help to reduce the amount of gossypol present in cottonseed meal, which would facilitate the utilization of cottonseed protein as a sustainable protein source in the future.

Mechanical characterization of particle reinforced jute fiber composite and development of hybrid Grey-ANFIS predictive model

ABSTRACT Natural fiber composites are a potential material in a range of engineering applications because of their excellent properties, which include reduced weight, better strength, and economic affordability. Aside from these features, these materials are biodegradable and renewable. The intent of this study is to look through into mechanical properties of aluminum oxide (Al2O3), boron carbide (B4C), and silicon carbide (SiC) particle-filled jute fiber polymer composite. The response surface methodology (RSM) with three levels/three factors is used to achieve the different combinations of process variables required to fabricate the desired polymer composites. In this regard, the effect of process variables on tensile characteristics, increase in weight %, and flexural characteristics is examined in detail. Further, the best combination of process parameters is chosen to produce composites with the desired mechanical qualities. The significance of such variables on each output variable is assessed using analysis of variance. A hybrid grey-based adaptive neuro-fuzzy inference system model is constructed for establishing multiple performance indexes. From the validation outcomes obtained, it is proved that the evolved model is proficient for precise prediction.

Perspectives of the extra corporeal membrane oxygenation – Key insights from mathematical analysis

Extracorporeal membrane oxygenator is a life-saving critical medical equipment for cardio-pulmonary support, for treatment of severe chronic respiratory distress. The equipment performance is dependent on the type of membrane, flow configuration, and blood physiology. Lot of research efforts are focused on the development of novel membrane. However, the cardiovascular pulsations and blood physiological conditions on the flow and associated transport is important, which was not explored significantly. Here, we investigate the impact of the frequency of flow oscillations on the oxygenation profile. The physiological parameters which affect the blood oxygenation profile is investigated in detail. The oscillations in the oxygen transfer rate profiles are suppressed, as the blood flow rate is reduced, and the oxygen saturation is completely out-of-phase with the flow rhythm (saturation is maximum when the flow is minimum). We present the phase space to easily decide on the required combination of process variables or design specifications, needed to achieve the target oxygen saturation, based on the patient blood physiology. The present effort in understanding the process, can lead to improved performance and design of this life-supporting equipment. The results provide clear recommendations from the clinical perspectives. The outcome of the work will be useful to the engineering (biomedical) community in realizing the role of fluid flow and transport phenomena in extracorporeal oxygenation, under varied physiological conditions.

Turning of hardened AISI H13 steel with recently developed S3P-AlTiSiN coated carbide tool using MWCNT mixed nanofluid under minimum quantity lubrication

This article aims to analyze the tool vibration, surface roughness, and chip morphology in hard turning of hot work AISI H13 steel under multi-walled carbon nanotubes mixed nanofluid with minimum quantity lubrication. Recently developed new-generation nanocomposite AlTiSiN coating deposited on carbide insert via scalable pulsed power plasma (S3P) strategy is used as cutting tool material. The experiments associated with 30 number of trials are performed by considering various machining parameters that includes cutting speed, nose radius, depth of cut, and feed. In perspective of predictive modeling and multi-response optimization, response surface methodology has been engaged as a means to minimize the tool vibration and surface roughness. At last, with the measured tool life and based on the Gilbert’s machining economic model, a distinctive cost analysis has been implemented to demonstrate the cost-effectiveness of coated carbide tool in hard turning. Statistical analysis confirms that nose radius with 36.65% has the highest contribution for surface roughness and cutting speed with 53.88% has the highest contribution for tool vibration. Increased flank wear and tool vibration are responsible for degradation of machined surface finish. Machining under nanofluid-MQL, chip morphology revealed the production of segmented type serrated saw-toothed chips. At higher cutting speed resulted: (a) obvious shape of saw tooth chip, (b) increase of distance among saw tooth, (c) reduction in chip segmentation frequency, (d) decrease of chip thickness. At optimum combination of process variables ( v = 55 m/min, d = 0.2 mm, r = 1.2 mm, f = 0.07 mm/rev), the life of AlTiSiN coated carbide tool is found to be 42 min under nanofluid-MQL and estimated the total machining cost expenditure per finished part of Rs. 153.52. New-generation AlTiSiN coated carbide tools can be competently and productively utilized for machining of hot work tool steel under ecologically cognizant minimum quantity cooling-lubrication, is the most accepted in industrial applications.

Investigations on mechanical properties of jute fibre reinforced with aluminium oxide fortified epoxy composite

ABSTRACTThe study focuses on determining mechanical properties of jute fibre and aluminium oxide-reinforced polymer composites. Further, response surface methodology, with three levels and three factors, is adopted to attain the various combinations of process variables for fabricating the desired polymer composites. The brunt of process variables on tensile modulus, tensile stress, percentage increase in weight, flexural stress and flexural modulus is investigated. The suitable combination of process parameters is determined for making the composites with expected mechanical properties. By analysis of variance and regression analysis, the importance of process factors for each output variable is evaluated. Interaction analyses are done to reveal the interaction effects on desired performance metrics of process variables. The optimised tensile and flexural stress values obtained are 43.49 and 73.47 MPa, respectively, and Tensile and Flexural modulus values are 4782.00 × 10−3 and 6483.52 × 10−3 GPa, respectively. A hybrid grey-based adaptive neuro-fuzzy inference system (ANFIS) model is developed for determining the multiple performance index. The competence of the developed model is validated and from the obtained outcomes, the constructed model is shown to be capable of accurately predicting the required performance measure.

WHITE-LAYER THICKNESS ON EDM-PROCESSED AISI A2 STEEL – MATHEMATICAL MODELING AND ANALYSIS

Electrical discharge machining is an electro-thermal technique where the recast layer on a machined surface and the heat-affected zone (HAZ) immediately below the machined surface are prevalent. As a result, assessing the white layer (recast layer) in EDM is a critical task. In this research, a response-surface methodology-based comprehensive mathematical model was developed to predict the white-layer thickness (WLT) on electrical discharge machine-processed AISI A2 steel. Also, the effects of various process parameters on the WLT were presented, the optimum combination of process variables was assessed and the minimum WLT was achieved by combining low-peak current and pulse-on time with high pulse-off time.

Isolation and statistical optimization of rhodanese (a thiosulphate sulphur transferase) production potential of Klebsiella oxytoca JCM 1665 using response surface methodology

Microorganisms are increasingly being used in cyanide bioremediation. Several organisms have been reported to thrive in cyanide contaminated wastewater due to their ability to produce cyanide detoxifying enzymes. However, to improve the production efficiency of these enzymes combinations of process variables need to be optimized. In this study, Klebsiella oxytoca JCM 1665 was isolated from industrial wastewater, identified by sequencing its 16S rRNA gene and subjected to rhodanese production using submerged fermentation. The conditions for production were optimized using response surface methodology (RSM). Central composite design was employed to evaluate the effects of three production parameters – peptone (1 – 5 %), KCN (0.1 – 0.5 %), and time of incubation (1 – 24 h). Second-order polynomial model was used to predict the response. Rhodanese activity in the experiments varied from 0.05 to 7.5 RU.mg-1. Under the optimum conditions of 4.35 % peptone, 0.4 % KCN and incubation time of 13 hr., the value for rhodanese yield was 7.810 U.mL-1. The R2 value for the model was 0.9925 (R2 = 0.9925). Also, the experimental values are in accordance with those predicted, indicating the suitability of the employed model and the success of RSM in optimizing the production conditions.

Gluten-free healthy snack with high nutritional and nutraceutical value elaborated from a mixture of extruded underutilized grains (quality protein maize/tepary bean)

An optimized gluten-free healthy snack (OGFHS) was developed from a mixture of protein quality maize/tepary bean (70:30), applying extrusion-cooking. The extrusion conditions were obtained from the axial combination of process variables (feed moisture content [FMC = 15%-25%]/extrusion temperature [ET = 120 °C-170 °C]/screw speed [SS = 50 rpm-240 rpm]). The desirability function response surface methodology was applied as an optimization technique in three response variables (expansion index [EI]/apparent density [AD]/hardness [H]) to obtain maximum EI and minimum AD and H. The best combination to produce OGFHS was FMC = 16.4%/ET = 137 °C/VT = 237 rpm. The optimized conditions to obtain OGFHS caused little change in the nutritional and nutraceutical properties of the unprocessed grains mixture. OGFHS (50 g) was compared with two commercial snacks (CheetosMR, TotisMR), showing better protein content, total dietary fiber and AoxA, as well as high acceptability and lower energy content than commercial snacks. Due to its great nutritional properties and AoxA,OGFHS could be used for health promotion and as an alternative to commercial snacks with low nutritional value and high energy content.

Comparative Study on EDM Parameter Optimization for Adsorbed Si3N4–TiN using TOPSIS and GRA Coupled with TLBO Algorithm

Electrical discharge machining is a thermo-physical-based material removal technique. 25 combinations of process variables were formulated with the aid of Taguchi technique for EDM of adsorbed Si3N4–TiN. Machining variables like pulse current, pulse-on time, pulse-off time, dielectric pressure, and spark gap voltage varied, and impact of each variables on the performance metrics (MRR, EWR, SR, ROC, θ, CIR, and CYL) was assessed. MCDM strategies like grey relational analysis and TOPSIS are utilized to find out the ideal arrangement of machining parameters to achieve most acute productivity of the multitude of reactions. Likewise, metaheuristic algorithm in particular GRA combined with teaching-learning-based optimization algorithm is utilized for getting global optimized input factors. Important factors like pulse current, pulse-on time, and spark gap voltage characteristically affect the outputs. It is recognized that the pulse-on time and the pulse current are the most significant input factors than others. The ideal machining parameters in view of GRA and TOPSIS techniques for acquiring better output factors are I, 12 amps; PON, 7 μsec; POFF, 4 μsec; DP, 12 kg/cm2; and SV, 36 volts.

Prediction of quality attributes (mechanical strength, disintegration behavior and drug release) of tablets on the basis of characteristics of granules prepared by high shear wet granulation.

High shear wet granulation is commonly applied technique for commercial manufacturing of tablets. Granulation process for tablets manufacturing is generally optimized by hit and trial which involves preparation of granules under different processing parameters, compression of granules and evaluation of the resultant tablets; and adjustment is made in granulation process on the basis of characteristics of tablets. Objective of the study was to optimize the process of high shear wet granulation and prediction of characteristics of tablets on the basis of properties of granules. Atenolol granules were prepared by high shear wet granulation method, using aqueous solution of polyvinyl pyrrolidone (PVP k-30) as binder. Concentration of binder solution and granulation time were taken as process variables, both studied at three levels. Different combinations of process variables were determined by Design Expert software. Granules were evaluated for different parameters on the basis of SeDeM-ODT (Sediment Delivery Model-Oro Dispersible Tablets) expert system. Granules from all the trials were compressed using round (10.5 mm) flat faced punches at compression weight of 250 mg/tablet. Tablets were evaluated of different quality control parameters as per USP. Results showed that both the process variables had positive effect on mechanical strength of tablets and negative effect on disintegration and dissolution rate. Granule prepared with highest level of binder concentration (15%) and highest granulation time (60 sec) resulted in tablets with highest crushing strength (11.8 kg), specific crushing strength (0.328 kg/mm2), tensile strength (0.208 kg/mm2), lowest value of friability (0.19%) and highest disintegration time (10.9 min), as predicted from granules characteristics on the basis of SeDeM-ODT expert system. Drug release from Trial-13 (processed under highest level of both process parameters) was also lower than rest of the trials. It is concluded from the study that quality characteristics of tablets can be predicted from granules characteristics using SeDeM-ODT expert system. Furthermore, SeDeM-ODT expert system can also be used for optimization of the process of high shear wet granulation.

Effect of process variables on imiquimod micronization using a supercritical antisolvent (SAS) precipitation technique

This study aimed to promote the micronization of imiquimod, a poorly water-soluble immunostimulant drug, using the supercritical antisolvent (SAS) process. The effect of temperature, pressure and drug concentration on drug particle formation, size and yield was evaluated. Increasing pressure tends to increase size and decrease yield. Increasing concentration results in raises in size and yield. Increasing temperature leads to size reduction and increase in yield. The combination of process variables shows that increase in pressure and concentration leads to an increase in size and a reduction in yield, and that pressure and temperature combined, as well as the combination of temperature and concentration, leads to reduction in size and yield. The micronization process was not capable of promoting improvement in drug dissolution rate or limit. However, a significant gain was observed in data reproducibility, which can contribute considerably to the quality control of formulations containing imiquimod.

Study on Thermal Comfort Properties of Eri Silk Knitted Fabrics for Sportswear Application

ABSTRACT The main focus of this study was to develop the different types of single jersey Eri silk knitted fabrics. This study also aimed to measure the effect of knitting process parameters on the dimensional, physical, and thermal comfort properties of the fabrics, which are the essential properties to be considered for active sportswear applications. Single Jersey (SJ), Single Pique (SP), and Honeycomb (HC) knitted structures were produced using Eri silk spun yarns of three different counts (25 Tex, 17.78Tex, and 14.26Tex) and two different stitch lengths. Eighteen different Eri silk single jersey fabric samples were developed. It was found that the Eri silk single knit fabrics structures had better dimensional, physical, and thermal comfort properties with reference to sport textiles. Statistical analysis indicated that the yarn linear density and stitch length significantly influenced the thermal conductivity, thermal resistance, air permeability, and water vapor permeability of the fabrics. Finally, a suitable combination of process variables was found for the production of Eri silk knitted fabric that was suitable for sportswear application