Values Of Response Variables Research Articles

Decoding the performance of a blend of metal-oxide nanoparticles in Eichhornia crassipes biodiesel at varying injection timing through the routes of thermodynamic analysis and statistical optimization

Biodiesel, known for its large cetane number, low pour point, and lower aromatics and sulfur content, has appeared as a promising alternative to diesel. This study focuses on testing the biodiesel extracted from Eichhornia Crassipes with TiO2 nanoparticles at 150 ppm concentration in a diesel engine. The experiment involved varying injection timings (ITs) of the biodiesel and nanoparticle blend, with three ITs of 20, 23, 25 and 28⁰bTDC, and a standard IT of 23⁰bTDC for diesel. The engine was tested at five different engine loads, ranging from 20 % to 100 %, with an increment of 20 % after each step. The experimentation was conducted at a constant an engine speed of 1500 rpm and a compression ratio of 17.5. The result showed an enhanced performance and a decreased emissions of carbon monoxide and hydrocarbons for 20⁰bTDC of biodiesel and nanoparticle blend. However, the emission of nitrogen oxides increased for the same blend and IT of 20⁰bTDC. The study suggests that using a 150 ppm concentration of TiO2 nanoparticles in Eichhornia Crassipes biodiesel could be an alternative to diesel for diesel engines when IT is set at 20⁰bTDC. Further, the statistical optimization using RSM suggests that the engine load around 61 % and IT of 20 ⁰bTDC gives optimum values of response variables.

A Retrospective Analysis of Polymer Selection Using Solvent Casting: Formulation and DoE Optimization of the Amorphous Solid Dispersion of Amoxicillin Trihydrate by a Spray Drying Method

Background. Amoxicillin trihydrate possesses poor solubility, compressibility, and flow behavior. Amorphous solid dispersion prepared by spray drying could solve all three problems at the same time. Objective. To prepare amorphous solid dispersion after screening of polymers by solvent casting method using a spray drying method. Methods. The solvent casting method was used to screen polymers, PVP/VA S-630, PVP K30, Soluplus, PEG 4000, HPMC AS, and HPMC HP55, in 1 : 1 and 2 : 3 ratios and followed by spray drying after polymer selection. Results. The dissolution performance of the formulation improved with time. The optimum feed rate and feed concentration were found to have an impact on the flow properties and particle size of spray-dried formulations, and they were selected as independent variables in a 32 full factorial statistical design. The ANOVA and regression analysis suggest that the developed regression model has a significant overall fit to the data and can explain a substantial proportion of the variability in the dissolution at 10 minutes. The optimized batch was selected based on the decisive factors of minimum and maximum values of response variables. Overall, the optimized batch demonstrated improved characteristics in terms of percentage yield (32.81%), dissolution at 10 min (49.70%), and angle of repose at 31.42°. Conclusion. This study provides valuable insights into optimizing formulation strategies for preserving the amorphous state of drugs and contributes to the development of stable pharmaceutical formulations.

Effects of Cutting Parameters on the Shearing Properties of Oilseed Rape (Brassica Napus L.) Shoots in Harvesting

Highlights Cutting parameters affect the shearing properties of ultimate shear stress and specific shear energy of oilseed rape shoots harvesting. The oilseed rape shoots harvester blade was optimized using a central composite design (CCD) experimental test. The interaction term of blade thickness and oblique angle significantly affect the ultimate shear force and specific shear energy. Provide a reference for designing oilseed rape shoots harvesting machinery. Abstract. Oilseed rape (Brassica napus L.) shoots are used as an edible nutritious and healthy vegetable. However, there is little information for the effects on cutting parameters on the oilseed rape stalk shearing properties to harvest fresh shoots. This study used the single factor method and response surface methodology (RSM) along with central composite design (CCD) to examine the effects of the blade operating and structural parameters, such as the blade loading velocity, the bevel angle, the oblique angle, and the thickness, on the response variables of ultimate shear stress and specific shear energy. The RSM analysis results demonstrated that the interaction term of blade oblique angle and thickness affect the response variables. Optimized operating and geometry parameters for minimizing response variable values were 195 mm min-1 for loading velocity, 16.4° for bevel angle, 16.3° for oblique angle, and 3.2 mm for thickness. Moreover, under these optimal parameters, the ultimate shear stress and specific shear energy were reduced by 31.37% and 40.95%, respectively, compared to the cutting parameters of the prototype harvester. This study concluded that the appropriate cutting parameters could effectively decrease the shear force and energy requirements, which provided a vital basis for designing and developing the cutting device for the oilseed rape shoots harvester. Keywords: Cutting parameters, Oilseed rape shoots, Shearing experimental test, Specific shear energy, Ultimate shear stress.

Fabrication of hybrid aluminum matrix composites and prediction of physical characteristics using artificial network

This study looks at how to make LM16 aluminum alloy-based Hybrid Aluminum matrix composites and how to forecast their physical properties (HAMC). The goal of this research is to forecast the Brinell hardness number (BHN) and density of Hybrid Aluminum matrix composites (HAMC). The Levenberg-Marquardt algorithm of a multiple-input multiple-output artificial neural network is used to make predictions (ANN). The HAMC with Nickel coated Graphite (Ni-Gr) and Silicon Carbide reinforcement into the LM16 aluminum alloy is prepared using the stir casting technique considering the brake lining applications. The L9 orthogonal array was chosen for testing to see how process parameters such Nickel coated Graphite (Ni-Gr) reinforcement, Silicon-Carbide (SiC) reinforcement, stirring speed, and stirring time affect the results. The Brinell hardness number (BHN) and density of the material are the output parameters investigated. The determination coefficient (R2) and mean square error were used to assess the ANN's prediction performance (MSE). The average R2 and MSE of 0.99 and 0.0041, respectively, suggest that the ANN fit the experimental experimental response variable values effectively. The correlation coefficient (R) of 0.99721 also supports the findings.

Effects of ultrasound time, xanthan gum, and sucrose levels on the osmosis dehydration and appearance characteristics of grapefruit slices: Process optimization using response surface methodology

In this work, the novel use of ultrasonic pre-treatment and edible coating treatment during osmosis dehydration to optimize the weight reduction, moisture loss, sucrose gain, rehydration, and surface shrinkage using a response surface methodology (RSM) based on a central composite design (CCD) technique was successfully conducted on grapefruit slices. The process parameters include sonication pre-treatment time (5–10 min), xanthan-gum-based edible coating (0.1%-0.3%, w/w), and sucrose concentration (20–50 Brix), were examined and optimized for osmosis dehydration of grapefruit slices. At each step, three grapefruit slices were immersed in an ultrasonic water bath at 40 kHz, 150 W, and 20 C. Then, the sonicated slices were placed in a container contain sucrose and xanthan, and the container was put in a 50 C water-bath for 1 h. The optimum concentration of xanthan gum, sucrose, and time of treatment were predicted to be 0.15%, 20.0 Brix, and 10.0 min, respectively. Under this optimum condition, estimated values of response variables are as follows: weight reduction 14.14%, moisture loss 25.92%, solids gain 11.78%, rehydration ratio 203.40%, and shrinkage 2.90%. The weight reduction and moisture loss increased when the sonication time and sucrose concentration increased. Results demonstrated that the experimental data could be adequately fitted into a linear model with p-value ranging from 0.0001 to 0.0309 for all the variables examined. The rehydration of dried samples increased when xanthan concentration increased. Also, the weight reduction, moisture loss, sucrose absorption, and shrinkage declined with increasing in the xanthan levels.

Competitive Removal of Antimony and Humic Acid by Ferric Chloride: Optimization of Coagulation Process Using Response Surface Methodology

The co-contamination of aquatic systems with antimony (Sb) and humic acid (HA) is a global concern due to their potential risks to human health and environment. In this research, three-level-three-variable Box–Behnken design (BBD) was investigated for simultaneous Sb(III) and total organic carbon (TOC) removal responses from groundwater by use of ferric chloride (FC) as a coagulant. This study focuses on three operating variables, including initial Sb(III) concentration (100, 550, and 1000 μg/L), HA concentration (2, 6, and 10 mgC/L) and FC dosages (20, 60, and 100 mg/L). The proposed quadratic model presented good correlation with experimental values having R2 and adjusted R2 values of response variables (Sb(III): 0.9981 and 0.9956) and (TOC: 0.9935 and 0.9851), respectively. The most pronounced influence of FC dosage was observed in the removal responses of TOC and Sb(III). Interestingly, the model revealed that a high level of FC dosage had the same TOC removal potential regardless of increasing HA concentration. The statistical model for both Sb(III) and TOC responses was used to optimize the experimental conditions. Moreover, the experimental results were successfully validated with predicted values having high accuracy. The sludge volume produced for studied variables followed the decreasing order as FC dosage > HA concentration > Sb(III) concentration. Under optimum conditions, 0.45 mL/L sludge volume was produced in the treatment of ground water in Nawabshah. In general, the results of the current study may provide some insights into the predictability of simultaneous removal performance of Sb species and organic substances from groundwater.

Using Response Surface Methodology to Optimize Edible Coating Formulations to Delay Ripening and Preserve Postharvest Quality of Tomatoes

Tomato is a nutrient-rich but highly perishable fruit. In order to delay the rapid ripening and degradation of fruits and reduce postharvest losses, response surface methodology (RSM) was used as the optimizing method to formulate edible coating based on pineapple peel extract and Arabic gum of twenty concentrations of pineapple (0.5–0.83 kg/l) and 20 concentrations of Arabic gum (5–15%, w/v). Tomatoes were soaked for 10–30 min in any of the coating solution. Five parameters including ripening rate, chlorophyll a content, firmness, total flavonoid content, and titratable acidity of tomatoes were evaluated after 8 days of storage at 24 ± 0.5°C and 82 ± 1.5% relative humidity. Results showed that the experimental data could be adequately fitted into a second-order polynomial model with coefficient of determination (R2) ranging from 0.775 to 0.976 for all the variables studied. The optimum concentrations were predicted as 0.70 kg/l pineapple peel extract and 17.04% with 18.72 min optimum time. Under these conditions, predicted values of response variables are as follows: ripening rate (RR) 40.75, chlorophyll a (Chl a) 8.11, firmness (Fir) 4.00, total flavonoid content (TFC) 43.51, and titratable acidity (TA) 0.30. It is concluded that RSM can be used to optimize pineapple peel extract and Arabic gum-based edible coating formulation to extend the shelf life or delay the ripening process of tomato fruit at ambient conditions.

Development and optimization of floating alginate microspheres of repaglinide using Box-Behnken experimental design

Background: Due to short-lasting action, fast clearance and enzymatic instability of anti-diabetic drugs result in low bioavailability as they are mainly absorbed from the stomach and the lower part of the gastrointestinal tract. The bioavailability of antidiabetic drugs can be significantly increased by enhancing the gastric residence time using gastroretentive drug delivery systems such as floating microspheres.
 Objectives: The present investigation deals with the development and optimization of floating microspheres for gastroretentive delivery containing repaglinide using Box-Behnken design and also investigates the effect of different process variables on the formation of the microsphere. Thereby, increasing the bioavailability and reducing the mentioned side effects of repaglinide.
 Materials and methods: Floating microspheres of repaglinide formulated by ionic gelation method and optimized using Box-Behnken design. The independent variables were the concentration of Hydroxy methylcellulose (HPMC) (A), Sodium alginate (B), and calcium chloride (C) while entrapment efficiency (R1), swelling index (R2), and in vitro drug release (R3) were considered as dependent variables. The optimized formulation of floating microspheres was characterized by various physicochemical properties, surface morphology, drug excipient interaction, in vitro release, and buoyancy studies.
 Results: Based on obtained 3D response surface plot factors A, B, and C were found to give a synergistic effect on R1, while factor A has a negative effect on R2. Interaction of AC was negative on R1 and R2 but positive on R3. The interaction of AC and BC was negative in all the responses. Scanning electron microscopy (SEM) revealed that microspheres were spherical with nearly smooth surface morphology. Good entrapment and buoyancy were observed for 12 h. The in vitro drug release was found to be controlled for more than 12 hours and followed the Higuchi model. The validations of response surface methodology (RSM) for three dependent variables were 100.09%, 99.68%, and 97.02%.
 Conclusion: Repaglinide floating microspheres were prepared and optimized by use of Box – Behnken process optimization software. The quantitative responses of particle size, entrapment efficiency, and in vitro drug release for different combinations of independent variables, sodium alginate as release retarding polymer, HPMC K100M as floating polymer, and calcium chloride as a cross-linking agent were obtained experimentally, and the results were found to fit the design model. The quantitative effect of these factors at different levels on the responses could be predicted using polynomial equations, and high linearity was observed between predicted and actual values of response variables. The results of the present study showed that the responses i.e, particle size, entrapment efficiency, and in vitro release are significantly affected by the concentration of polymer and a cross-linking agent. The formulation OF1 was found to be the optimum formulation predicted by the point prediction of the design expert software. The in vitro drug release was found to be controlled for more than 12 h and followed the Higuchi model. The validations of RSM for three dependent variables were 100.09%, 99.68%, and 97.02%. Therefore, it can be concluded that a floating microsphere for repaglinide was developed and optimized using a three-factor, three-level Box – Behnken design.

Development of Grain-Based Protein Rich Extruded Snack Foods Using D-Optimal Mixture Design

In developing nations, malnutrition is one of the main factors contributing to child mortality. To tackle this threat, the development of grain-based, protein-rich, and nutrient-dense extruded foods has been suggested. In this study, 16 experiments were generated using D-optimal mixture design and, the blending ratios of ingredients for the development of protein rich snack food were optimized. The optimum value for independent variables corresponding to highest desirability (0.775) was recorded as 25% corn, 23.79% rice and 11.21% defatted soy flour. The value of response variables in terms of nutritional quality of developed product viz. protein, fat, carbohydrate and energy content were found to be 20.52%, 1.34%, 55.13% and 337.23 kcal whereas, the sensory quality viz. texture and overall acceptability were obtained as 5.78 N and 7.53, respectively. The product packaged in PET/PET-MET/LDPE was found to be safe for consumption up to six months.

Prediction of single cylinder direct injection diesel engine performance fuelled with lemon peel oil biodiesel using artificial neural network

The aim of the present work is to predict the performance of single cylinder direct injection diesel engine fuelled with lemon peel oil biodiesel (B25) loaded by ceria nanoparticle additives and coated with yttria-ceria stabilized zirconia using Levenberg-Marquardt algorithm of single-input multiple-output artificial neural network (ANN). The dependent variables are brake power, indicated power, frictional power, brake thermal efficiency, indicated thermal efficiency, volumetric efficiency, fuel flowrate, air flowrate and specific fuel consumption at varying loads from 0 to 100%. The prediction ability of ANN was evaluated by determination coefficient (R 2 ) and root mean square error (RMSE). The average R 2 and RMSE of 0.93 and 0.82 show that the ANN fitted well to the experimental values of response variables at varying loads. The results also validate through correlation coefficient (R) of 0.99254.

Understanding uncertainty in forest resources maps

Maps of forest resources and other ecosystem services are needed for decision making at different levels. However, such maps are typically presented without addressing the uncertainties. Thus, the users of the maps have vague or no understanding of the uncertainties and can easily make wrong conclusions. Attempts to visualize the uncertainties are also rare, even though the visualization would be highly likely to improve understanding. One complication is that it has been difficult to address the predictions and their uncertainties simultaneously. In this article, the methods for addressing the map uncertainty and visualize them are first reviewed. Then, the methods are tested using laser scanning data with simulated response variable values to illustrate their possibilities. Analytical kriging approach captured the uncertainty of predictions at pixel level in our test case, where the estimated models had similar log-linear shape than the true model. Ensemble modelling with random forest led to slight underestimation of the uncertainties. Simulation is needed when uncertainty estimates are required for landscape level features more complicated than small areas.

ÇİMENTO YERİNE KISMİ AHŞAP TABAN KÜLÜ İÇEREN HARÇLARIN MEKANİK ÖZELLİKLERİ

Carbon dioxide (CO2) released into the atmosphere during the production of Portland cement (PC) is one of the important factors causing global warming. Therefore, studies are carried out on different materials to reduce PC consumption. The effect levels of the wood bottom ash (WBA) ratio and specimen age on the response variables (compressive strength, flexural strength, and ultrasonic pulse velocity) were investigated in this study. Mortar specimens were produced using PC, WBA, CEN standard sand, and distilled water. The produced specimens were cured in water until the test day. WBA ratios are 0%, 5%, 10%, 20%, 35% and 50% by weight of binder. As a result, it was determined that the optimum WBA ratio was 5%. In addition, R2 values of response variables were found to be high (ultrasonic pulsed wave velocity; 0.8925, flexural strength; 0.9356, compressive strength; 0.9404) by analysis of variance (ANOVA). This shows that the models have a high correlation. Moreover, the terms added to the models have a significant effect on the responses.

Optimization of protease production from Rhizomucor miehei Rm4 isolate under solid-state fermentation

BackgroundProtease is one of the most important industrial enzymes. The importance of protease bioproduction comes from meeting the increasing demand for this enzyme especially in the cheese industry. Rhizomucor miehei protease is the preferred substitute for the traditional rennet. Solid-state fermentation (SSF) shows promising results in enzyme production. An optimization strategy was applied to optimize the production of Rhizomucor miehei protease in a solid medium. The components of the fermentation medium were screened by using the one-factor-at-a-time (OFAT) approach. The optimization process then was performed by using the response surface methodology (RSM) approach based on five factors (fermentation time, temperature, pH, moisture content, nitrogen concentration) at five levels. Specific milk clotting activity and milk clotting activity/proteolytic activity ratio were considered as response variables in the optimization process. ResultsAmong several combinations, wheat bran was selected as the best substrate. Casein was selected based on preliminary screening of nitrogen sources. The optimal conditions identified by RSM analysis were found to be 81.21 h, 41.11°C, 6.31, 80%, and 1.33% for fermentation time, temperature, pH, moisture content, and casein concentration, respectively. The performed fermentation process under the optimized conditions gave an enzymatic extract with the values of 5.11 mg/mL, 2258.13 Soxhlet unit/mL, 441.90 Soxhlet unit/mg, 1.14 protease unit/mg, and 388.66 for protein content, milk clotting activity, specific clotting activity, specific proteolytic activity, and milk clotting activity/proteolytic activity ratio, respectively. The aforementioned values were close to the predicted values. ConclusionThe high milk clotting activity and the relatively low proteolytic activity signify higher specificity of the produced enzyme, which is favorable in cheese making. The observed results reveal the efficiency of the applied statistical approaches in obtaining desired values of response variables and minimizing experimental runs, as well as achieving good predictions for response variables.

Parameter Optimization of Newly Developed Self-Propelled Variable Height Crop Sprayer Using Response Surface Methodology (RSM) Approach

The number of spray deposits plays an important role in effective and efficient spraying. The spraying equipment is one of the most significant factors that affect the number of spray deposits. Therefore, the study was focused on the parameter optimization of a newly developed self-propelled variable height crop sprayer. Response surface methodology (RSM) along with Box–Behnken design (BBD) was used to study the effect of the independent variables (forward speed, spray height, and spray pressure) on response variables such as droplet density, coverage per-centage, and Volume Median Diameter (VMD). The experiment was conducted in the cotton field. Additionally, the RSM model was validated in this research. The results revealed that the coefficient of determination (R2) values was good for all response variables in the quadratic polynomial model. The optimized parameters were 6.5 km/h, 60 cm, 4 bar for fungicide application, and 8 km/h, 70 cm, 3 bar for insecticide and herbicide application. The predicted response variable values at the optimal conditions were 60.4 droplet/cm2, 27%, 230 µm for fungicides and 37.8 droplet/cm2, 19.1%, 225.4 µm for insecticide and herbicides application. The model validation is confirmed by the mean of actual response variable values at the optimal condition for insecticide and herbicides application, which was 41.35 ± 3.67 droplet/cm2, 21.10 ± 1.72%, 227.43 ± 1.22 µm, and the prediction error was 8.46%, 9.2%, and 0.9% for droplet density, coverage percentage, and VMD, respectively. This study can provide support for further optimizing the parameters of the sprayer.

Comparison of mixed model based approaches for correcting for population substructure with application to extreme phenotype sampling

BackgroundMixed models are used to correct for confounding due to population stratification and hidden relatedness in genome-wide association studies. This class of models includes linear mixed models and generalized linear mixed models. Existing mixed model approaches to correct for population substructure have been previously investigated with both continuous and case-control response variables. However, they have not been investigated in the context of extreme phenotype sampling (EPS), where genetic covariates are only collected on samples having extreme response variable values. In this work, we compare the performance of existing binary trait mixed model approaches (GMMAT, LEAP and CARAT) on EPS data. Since linear mixed models are commonly used even with binary traits, we also evaluate the performance of a popular linear mixed model implementation (GEMMA).ResultsWe used simulation studies to estimate the type I error rate and power of all approaches assuming a population with substructure. Our simulation results show that for a common candidate variant, both LEAP and GMMAT control the type I error rate while CARAT’s rate remains inflated. We applied all methods to a real dataset from a Québec, Canada, case-control study that is known to have population substructure. We observe similar type I error control with the analysis on the Québec dataset. For rare variants, the false positive rate remains inflated even after correction with mixed model approaches. For methods that control the type I error rate, the estimated power is comparable.ConclusionsThe methods compared in this study differ in their type I error control. Therefore, when data are from an EPS study, care should be taken to ensure that the models underlying the methodology are suitable to the sampling strategy and to the minor allele frequency of the candidate SNPs.

Optimization of Cutting parameters of AISI 1018 Low Carbon Mild steel in turning using green cutting fluid by Taguchi Method

The objective of this work is to optimize the response Cutting parameters (Tool wear and Material Removal Rate) of AISI 1018 Low carbon mild steel by Taguchi Method in straight turningprocess. We have taken speed, feed, depth of cut and types of cutting fluids as machining parameters with their three level values. In our study a commercial semi-synthetic cutting fluid (SSCF) and two vegetable based cutting fluids are used and values of response variables are analyzed to see if the performance of response machining parameters is increased by using Vegetable based cutting fluids for sustainable machining.For individual optimization,Taguchi‟s L9(34 ) orthogonal array and Analysis of Variance(ANOVA) are used. The optimum results are verified with the help of confirmation test

Tomato juice microfiltration process assisted with pressure-vacuum combination condition: A physicochemical investigation and optimization

In this study, the simultaneous effect of fluid pressure, vacuum pressure, and fluid temperature in the microfiltration process of tomato juice was investigated. For this purpose, a microfiltration device with a ceramic filter under pressure-vacuum combination was developed. Next, the effects of the device variables were investigated and optimised in the tomato juice microfiltration process. The results showed the statistically significant effects of fluid pressure, vacuum pressure, and fluid temperature on permeate flux response and total energy consumption. Also, they showed the effects of fluid pressure and fluid temperature on the response variables of lycopene and vitamin C content of tomato juice microfiltration. The optimal conditions of the microfiltration process of tomato juice were achieved at a fluid pressure of 200 kPa, the vacuum pressure of 5 kPa, and fluid temperature of 26 °C with a desirability index of 0.967. Under the optimal conditions, values of response variables including permeate flux, total energy consumption, lycopene, and vitamin C content of tomato juice filter were 215.09 × 10 −6 m 3 m −2 s −1 , 0.044 kWh, 2.42 mg100 g −1 , and 13.21 mg 100 g −1 , respectively. Overall, this type of tomato juice microfiltration, in addition to preserving the valuable nutrients of tomato juice, can cause high permeate flux and low process energy consumption. • Microfiltration performed under pressure-vacuum conditions for tomato juice. • A ceramic filter with a pore size of 0.2 μm was a good option. • Permeate flux was very high at first and then decreased gradually. • Optimal conditions included fluid pressure of 200 kPa, and fluid temperature of 26 °C. • Under the optimum condition, low energy consumption achieved.

Choosing priors in Bayesian ecological models by simulating from the prior predictive distribution

AbstractBayesian data analysis is increasingly used in ecology, but prior specification remains focused on choosing non‐informative priors (e.g., flat or vague priors). One barrier to choosing more informative priors is that priors must be specified on model parameters (e.g., intercepts, slopes, and sigmas), but prior knowledge often exists on the level of the response variable. This is particularly true for common models in ecology, like generalized linear mixed models that have a link function and potentially dozens of parameters, each of which needs a prior distribution. We suggest that this difficulty can be overcome by simulating from the prior predictive distribution and visualizing the results on the scale of the response variable. In doing so, some common choices for non‐informative priors on parameters can easily be seen to produce biologically impossible values of response variables. Such implications of prior choices are difficult to foresee without visualization. We demonstrate a workflow for prior selection using simulation and visualization with two ecological examples (predator–prey body sizes and spider responses to food competition). This approach is not new, but its adoption by ecologists will help to better incorporate prior information in ecological models, thereby maximizing one of the benefits of Bayesian data analysis.

Experimental investigation on the effect of laser powder bed fusion process variables on dimensional accuracy, surface roughness and cylindricity of the AlSi10Mg alloy component

The present study deals with the influence of laser powder bed fusion process parameters on the selected linear dimension, surface roughness and cylindricity of AlSi10Mg alloy for manufacturing of a prototype connecting rod. The process variables used in this investigation are laser power, laser velocity, layer thickness and scanning speed. Response surface methodology is used to perform experiments and data analysis. The levels of process parameters are same that is, five for all the selected input process variables. An automotive component connecting rod is used as a component to analyze the effect of process variables on selected response variables. The optimum sating of process variables are different for dimensional accuracy, surface roughness and cylindricity. Minitab 14 software is used for the data analysis. The international tolerance grades of confirmation experiments are calculated as per the ISO standard UNI EN 20286-I and DIN 16901. A quadratic regression models are developed to estimate the response variables in terms of process parameters. The model is adequate within the experimental domain. X-chart of confirmation experiments is plotted. The deviation in the linear dimension is within the limit of ±3 sigma (σ). The lowest values of response variables at the best level of process parameters are obtained, that is, percentage error in dimensional accuracy of 2.65%, surface roughness of 2.57 µm and cylindricity of 0.09 mm. The novelty of this work lies in the fact that only a few studies have been conducted related to the form errors in the archival literature.

Multi-objective optimization and finite element method combined with optimization via Monte Carlo simulation in a stamping process under uncertainty

The response surface methodology (RSM), which uses a quadratic empirical function as an approximation to the original function and allows the identification of relationships between independent variables xi and dependent variables ys associated with multiple responses, stands out. The main contribution of the present study is to propose an innovative procedure for the optimization of experimental problems with multiple responses, which considers the insertion of uncertainties in the coefficients of the obtained empirical functions in order to adequately represent real situations. This new procedure, which combines RSM with the finite element (FE) method and the Monte Carlo simulation optimization (OvMCS), was applied to a real stamping process of a Brazilian multinational automotive company. For RSM with multiple responses, were compared the results obtained using the agglutination methods: compromise programming, desirability function (DF), and the modified desirability function (MDF). The functions were optimized by applying the generalized reduced gradient (GRG) algorithm, which is a classic procedure widely adopted in this type of experimental problem, without the uncertainty in the coefficients of independent factors. The advantages offered by this innovative procedure are presented and discussed, as well as the statistical validation of its results. It can be highlighted, for example, that the proposed procedure reduces, and sometimes eliminates, the need for additional confirmation experiments, as well as a better adjustment of factor values and response variable values when comparing to the results of RSM with classic multiple responses. The new proposed procedure added relevant and useful information to the managers responsible for the studied stamping process. Moreover, the proposed procedure facilitates the improvement of the process, with lower associated costs.