Central Composite Design Method Research Articles

New insights on the removal of diclofenac and ibuprofen by CWPO using a magnetite-based catalyst in an up-flow fixed-bed reactor

This research has been focused on the removal of two anti-inflammatory drugs, diclofenac (DCF) and ibuprofen (IBU), by a continuous catalytic wet peroxide oxidation (CWPO) process using a lab-synthesized nanomagnetic catalyst (Fe3O4/MWCNTs). The central composite rotatable design (CCRD) method was used to study the effect of DCF and IBU concentration (expressed as theoretical oxygen demand (ThOD) between 0 and 52.5 mg L−1) and of the feed stream pH (from 3 to 7) on the removal of total organic carbon (TOC) and the concentration of aromatic compounds (Arm) and total phenolic compounds (TP) by CWPO. It could be observed that DCF was preferably removed from the DCF-IBU aqueous mixture at pH values ranging from 3 to 5. In addition, feed stream pH had a significant effect on the pollutants removal, as well as on TOC, TP and aromatic compounds removal, observing an increasing in the pollutants degradation when feed stream pH decreased from 7 to 3. Quadratic models predicted for response variable, such as TOC, TP and aromatic compounds removal, and their maximum model-predicted removal values were of 90.0, 80.2 and 90.0%, respectively. Finally, as a proof of concept, three environmentally-relevant aqueous matrices, spiked with DCF-IBU mixture, were treated. In this case, relatively high TOC degradation values were found after 20 h reaction time (ca. 57.7, 73.9 and 54.5% in surface water, WWTP effluent and hospital wastewater, respectively). This work deals the first study about DCF-IBU removal in aqueous solution by CWPO, as well as a continuous study using real wastewater that allow to extend the experimental results to a real scenario.

Optimization of production of the fermented milk beverage based on Tibetan kefir grains

In recent years we have seen interest in Tibetan kefir grains (TKG) for production of fermented milk beverages. The article presents the results of the work on expanding the range of fermented milk beverages based on TKG, studying the influence of fermentation conditions on quality characteristics of beverages and their shelf life. The possibility of their optimization is shown. Using the method of Central Composite Design (CCD), response functions were obtained in the form of 2nd-order polynomials, and the numerical values of the coefficient of these dependencies were calculated. The conditions of milk fermentation by Tibetan kefir grains were optimized. The numerical values of the influencing factors are: the dose of inoculate is (60 ±2) g per 100 ml of the milk, fermentation duration -(30±3) h; fermentation temperature - (35±4)°C. We also studied the effect of sea buckthorn, chokeberry and lingonberry juice additives on the quality of fermented milk drinks and their shelf life. Using the same method CCD we determined the functional dependencies of sensory indicators and shelf life of the beverage on the added amount of berry juice and sucrose. According to the results of the conducted studies, the recipe of a fermented milk beverage was modified. The optimal content of the components (per 100 ml of the drink) is: juice – (12±2) ml, sucrose - (10±1) g.

Numerical simulation of rock cutting using YADE

Laboratory rock cutting tests are commonly used for the study of the cutting process and the evaluation of the expected performance of excavation machines. The cutting process is dominated by a great number of parameters, most of them relating with the intrinsic mechanical characteristics of the rock. In this study the open software Yade is used for simulating laboratory cutting tests on sandstone samples using a drag pick as the cutting tool. The process is simulated based on a conventional formulation, combined with an enhancement of the sample microstructure through the manipulation of the interaction range coefficient, which provides for the sample a very realistic initial strength ratio UCS/UTS. On cylindrical samples, four different cuts in four different paths were carried out. The mean cutting force for each cut was calculated and was chosen to represent the macroscopic response of the numerical model. The optimum set of microparameters is obtained through an experimental design with the Placket-Burman and Central Composite Design methods, and then optimized, in regard to the microparameters’ values, so that the rock cutting simulation is in close accordance with the observations from the actual laboratory cutting tests.

Determination of sulfonamide residues in animal foodstuffs by magnetic dispersive solid-phase extraction using magnetic carbon nanocomposites coupled with ion pair-dispersive liquid\u2013liquid micro-extraction combined with HPLC-DAD

In this study, magnetic-dispersive solid-phase extraction coupled with ion pair-dispersive liquid–liquid micro-extraction (MSPE-i-DLLME) was used to the pre-concentration and extraction of five sulfonamides residues (sulfadiazine, sulfathiazole, sulfacetamide, sulfamethazine and sulfamethoxazole) in animal foodstuffs. The sulfonamides are extracted using magnetic carbon nanocomposite and then eluted with acetonitrile. In the DLLME step, the target analytes are collected in 1-octanol containing 10% Aliquat-336 (as extraction solvent). Finally, the compounds are quantified by HPLC with DAD detection. The extraction parameters optimized using the one at the time and central composite design methods. Under the optimized conditions: sample solution volume was 100 mL; initial pH: 12, amounts of MCNs: 30 mg; desorption solvent (ACN) volume, 1 mL; desorption condition, 10 min sonication at two step; extraction solvent (1-octanol + 10% aliquat) volume, 115 µL; pH of DLLME step, 3; salt effect, 24.6%, (w/v) NaCl; centrifuge, 5 min, 4000 rpm. In these extraction conditions, the proposed procedure represented good pre-concentration factors between 130 and 490; detection limits in the range from 0.01 to 5 μg kg−1 (at S/N = 3), and linear response in the 0.1–400 μg kg−1 concentration range. The method is successfully applied to the determination of sulfonamides residues in animal foodstuffs.

Optimization of operating parameters of seeding device in plot drill with seeding control system

Plot drill has a significant impact on field breeding by replacing manual seeding. However, the seeding performance of plot drill needs to be further improved as the mechanical transmission method cannot work under the optimal combination of operating parameters. In this study, a cone compartment tray of sowing device was evaluated under the laboratory conditions, using a self-designed seeding control system to adjust the operating parameters. Among them, a plot seeding control system was involved that could set operating parameters through an Android terminal, which effectively reduced the difficulty of parameters adjustment. The method of central composite experiment design was employed to conduct experiments and explore the effects of experiment factors such as lifting height of storage tube (LHST), rotation speed of cone compartment tray (RSCCT), and rotation speed seed distributor (RSSD) on the seeding uniformity coefficient of variation among rows (SUCVR) and the rate of damaged seeds (RDS). For SUCVR of wheat, the results showed that the importance order of main factors from high to low was RSSD, RSCCT and LHST. And for SUCVR of buckwheat, RSCCT had the largest influence, followed by RSSD, and LHST had the smallest influence. For RDS of wheat and buckwheat, only RSSD had a very significant effect among the main factors. The experiments data of wheat indicated that when operated at 27.2 mm for LHST, 4.5 r/min for RSCCT and 1169 r/min for RSSD, the sowing performance of wheat was optimal, with SUCVR and RDS values of 5.02% and 0.117%, respectively. The buckwheat seeding performance was found to be optimal when the LHST, RSCCT and RSSD were 26.5 mm, 3.9 r/min and 711 r/min, and SUCVR and RDS were 4.74% and 0.113%. The seeding control system realized the electromechanical control of the seeding equipment, was convenient for parameter setting and stepless adjustment, and could be operated under an optimized combination of operating parameters. The research results could provide a reference for the performance improvement and application of plot drill. Keywords: plot drill, seeding device, seeding control system, seeding performance, parameter optimization DOI: 10.25165/j.ijabe.20211403.6218 Citation: Cheng X P, Li H W, He J, Wang Q J, Lu C Y, Wang Y B, et al. Optimization of operating parameters of seeding device in plot drill with seeding control system. Int J Agric & Biol Eng, 2021; 14(3): 83–91.

Optimization of Operating Parameters of Diesel Engine Powered with Jatropha Oil Diesel Blend by Employing Response Surface Methodology

Biodiesel is promoted as an appropriate alternative fuel for use in compression ignition (CI) engines as it is non-toxic, biodegradable, and sulphur-free and does not require any change in current engines design. The key objective of this experimental study is to evaluate the best operational parameters of engine referring to performance and emissions of Jatropha biodiesel powered CI engine by employing response surface methodology (RSM). In order to achieve maximum brake power (BP), brake thermal efficiency (BTE) and to reduce nitrogen oxide (NOx) and unburnt hydrocarbon (HC) emissions the optimization model is used. Effects of different factors such as fuel injection pressure (FIP), engine compression ratio (CR), and load on thermal performance have been studied in a single cylinder diesel engine. Experiments design was based on L20 orthogonal array Central composite design (CCD) method. RSM was employed to test the suitability of biodiesel in diesel engines and models were developed by using experimental results. Based on the optimization, the optimum engine parameters found were 18 CR, 180 bar FIP and 8.11 kg engine load. Under these settings, the optimum responses were found as 2.21 kW, 28.24%, 25.3 ppm and 174.6 ppm for BP, BTE, HC, and NOx, respectively. Meanwhile, R 2 (coefficient of determination) values were found as 99.96%, 99.93%, 98.5%, 99.14%, and 99.78%, for BP, BTE, net heat release rate (NHRR), HC, and NOx, respectively.

Optimization of Blanching Process of hawthorn using central composite design and response surface method

Response surface method (RSM) was used to optimize hawthorn blanching process based on single-factor experiments. There were two numerical (time and temperature) and one categorical variable (POD enzyme activity) that was used for the evaluation of the hawthorn blanching quality. The changes in POD enzyme activity, procyanidin content, and total flavonoid content were also analyzed. The regression model (P< 0. 05) was obtained through Central-Composite experimental design, indicating that the model could evaluate the residual POD enzyme activity in the blanching process under different parameters. The results show that time 103s and temperature 92.5°C were the best blanching conditions. Under these optimized conditions, the residual POD enzyme activity of hawthorn was within a reasonable range, which was 0.7731 U⋅g-1⋅min-1. The calculated results were basically consistent with the model predicted values (0.7989 U⋅g-1⋅min-1).

Techno-economic Optimization of Combined Cooling, Heat and Power System Based on Response Surface Methodology

In the present work, the statistical analyses are presented to study the economic indexes of Net Present Value (NPV) and Simple Payback Period (SPB) as response functions for the Combined Cooling, Heating and Power (CCHP) system. The CCHP performance is simulated with the aid of thermodynamic modeling, and also economic equations are presented for economic simulation. An attempt is made to study the effect of some economic factors (interest ratio, fuel cost, lifetime, and electricity sell price) on the system’s responses. Based on the Design of Experiment analysis, regression models are presented to quantify the effects of these parameters on the Net Present Value and Simple Payback Periods. This novel approach is developed utilizing the response surface methodology (RSM) based on the central composite design (CCD) method. Sensitivity analysis of the economic parameters was also examined in this research. Optimal values of these parameters were obtained for the two economic indexes as response functions.

Impact of processing parameters on fatigue life of fused filament fabricated parts: application of central composite design and genetic algorithm tools

Generally, components tend to fail due to cyclic loading; therefore study of fatigue life of products is a critical issue. The purpose of this study is to develop a predictive model relating the process parameters with fatigue life of fused filament fabrication (FFF) technology-based 3D-printed parts. Test specimens are printed using a desktop FFF 3D printer to suit a laboratory-based fatigue testing machine. Response surface methodology-based central composite design method was used for the design of experiments to obtain a regression model for prediction of response. Lastly, genetic algorithm solver was used to optimise the predictive model results. Number of contours, layer thickness and raster width were found to have a significant effect while raster angle had very little effect on the fatigue life of FFF-based 3D printed parts. In addition, it was observed that there is a high significant interaction between layer thickness and raster width of the FFF specimens.

Optimization of ammonium adsorption from landfill leachate using montmorillonite/hematite nanocomposite: response surface method based on central composite design

In this study, the adsorption of ammonium ions from landfill leachate (LL) was examined using montmorillonite/hematite nanocomposite (M/HNC). Parameters affecting ammonium adsorption such as pH 3-11, adsorbent dosage 0.05-0.25 g/L, mixing speed 75-175 rpm, and contact time 25-125 min were assessed. The central composite design (CCD)-based response surface methodology was employed to design the experiments and find optimal conditions. M/HNC was synthesized under green conditions and structurally investigated using X-ray powder diffraction, Fourier-transform infrared spectroscopy, Brunauer-Emmett-Teller, and scanning electron microscopy techniques. M/HNC was synthesized with a particle size of about 26 nm, magnetic properties, and a high specific surface area. The efficiency of ammonium adsorption from LL was increased by enhancing parameters such as pH, adsorbent dosage, mixing speed, and contact time. The pH of 8.05, adsorbent dosage of 0.167 g/L, mixing speed of 117.42 rpm, and contact time of 81.99 min was presented as optimal conditions according to the results of the CCD method. In addition, the efficiency of ammonium adsorption from LL using M/HNC was 80% at optimal conditions. The data of ammonium adsorption on M/HNC fitted well with pseudo-second-order kinetic and Langmuir isotherm models. It should be noted that the interactive effects of the parameters had an increasing impact on the ammonium adsorption efficiency. Based on the results of the present study, M/HNC had an effective potential for the adsorption of ammonium from LL.

Improvement of biodiesel production from palm oil by co-immobilization of Thermomyces lanuginosa lipase and Candida antarctica lipase B: Optimization using response surface methodology

Candida antarctica lipase B (CALB) and Thermomyces lanuginose lipase (TLL) were co-immobilized on epoxy functionalized silica gel via an isocyanide-based multicomponent reaction. The immobilization process was carried out in water (pH 7) at 25 °C, rapidly (3 h) resulting in high immobilization yields (100%) with a loading of 10 mg enzyme/g support. The immobilized preparations were used to produce biodiesel by transesterification of palm oil. In an optimization study, response surface methodology (RSM) and central composite rotatable design (CCRD) methods were used to study the effect of five independent factors including temperature, methanol to oil ratio, t-butanol concentration and CALB:TLL ratio on the yield of biodiesel production. The optimum combinations for the reaction were CALB:TLL ratio (2.1:1), t-butanol (45 wt%), temperature (47 °C), methanol: oil ratio (2.3). This resulted in a FAME yield of 94%, very close to the predicted value of 98%.

Synthesis and Optimization of a Molecularly Imprinted Membrane as a Specific Absorbent to Assess the Occupational Exposure to the 5-Fluorouracil Drug

Objectives: Over the last decades, antineoplastic drugs have resulted in concerns about health care and pharmacy because of the increased incidence of neoplasm due to occupational exposure. Therefore, there is a need for accurate and valid methods to detect and evaluate the trace amount of these drugs in occupational assessment programs. In this study, for occupational exposure assessment of 5-fluorouracil (5-FU), molecularly imprinted membranes (MIMs) were synthesized and optimized as appropriate absorbents for selective determination of trace amount of this antineoplastic drug. Methods: To synthesize MIMs as Specific adsorbent for 5-FU, First, 5-FU molecularly imprinted polymers (MIPs) were prepared by the precipitation polymerization method, and then MIPs encapsulated into PET nanofibers, as the matrix polymer, by electrospinning. Finally, MIMs were fabricated from collection nanofibers. Optimization of electrospinning parameters (e.g., MIP amount, tip needle to the collector, electrospinning voltage and flow rate) was performed by the Central Composite Design (CCD) method using the experimental design software. The diameter of the fiber was analyzed using image analysis software. The applicability of the synthesized membranes for absorbing 5-FU drug was evaluated for assessing occupational exposure to the drug of interest. Results: In this study, MIP particles were successfully encapsulated into PET nanofibers. The optimization process showed that the molecularly imprinted nanofibers diameter of 276.38[Formula: see text]nm could be obtained in 57.4%[Formula: see text]w, 13.9[Formula: see text]cm, 25[Formula: see text]KV and 0.55[Formula: see text]ml/h. The extraction efficacy of 5-FU drugs by synthesized membranes was [Formula: see text]. Conclusion: The optimized MIMs presented in this study can be used as a specific absorbent for occupational and environmental monitoring of the 5-FU drug.

Copper extrusion 3D printing using metal injection moulding feedstock: Analysis of process parameters for green density and surface roughness optimization

In the present study, the extrusion-based 3D printing process was explored using metal injection moulding (MIM) copper feedstock to fabricate dense copper parts. The influence of process parameters of 3D printing, namely layer thickness, nozzle speed, extrusion multiplier and extrusion temperature on green density and surface roughness were studied. Based on the central composite design method, a set of experiments was chosen to study the individual and interaction effects of parameters. Analysis of variance was performed to identify the significant factors and statistical models were obtained by regression analysis. The green density was observed to increase with varying the layer thickness from 0.25 to 0.05 mm and nozzle speed from 100 to 20 mm/s. Similarly, surface roughness was improved by decreasing the layer thickness and the nozzle speed and by increasing the extrusion multiplier up to a specific value. The extrusion temperature gave the best results at 200 °C for both green density and surface roughness. The significant interactions between the parameters for both green density and surface roughness were also studied. A multi-objective optimization approach was used to maximize the green density and minimize the surface roughness within the range of the parameters. Micro-tomography scans were used to analyze the porosity and voids in samples printed with optimized and non-optimized parameters. Besides, sintering was performed on the optimized printed sample to fabricate a dense copper part and analyze linear shrinkage during sintering. Sintered copper parts with high density and low surface roughness were obtained with the optimized printing process parameters.

Using Entropy Weight and Over All Evaluation Criteria Optimize the Parameters in EDM of Al–20 Wt. % SICP MMC

The paper has been described one of the recent effective optimization technique i.e. Over all Evaluation Criteria (OEC) to controlling the process parameter during Electrical discharge machining (EDM) of Al – 20 wt.% SiCP metal matrix composite (MMC). The MMC is one of the advance material in compare to the conventional material. To exploit the industrial application of this material with EDM it needs to optimize the process parameter for reliable and economical production of this process. For that the experiment is carried out by Central composite design (CCD) method by considering the input process parameters like peak current, pulse on time and flushing pressure and the output are material removal rate, tool wear rate, radial over cut and surface roughness (Ra). In order to optimize the process parameter a combine effect of Entropy weight measurement method and Over all Evaluation Criteria (OEC) is used to get a single numerical index. Finally, the present work.is is carried out by confirmation test to analyse the desirable approach of the selected model.

Buckling Analysis of Innovative Corrugated Column by using Response Surface Methodology

Abstract Tubular steel structures with slender compression cross-sections are prone to local buckling. In this paper the axial compression is investigation of the innovative fabricated structural steel column consisting of sinusoidal corrugated pattern. When it comes to the design of compressive individuals, buckling is a crucial layout provision. The paper describes the effect of variation in the sectional properties like mean diameter, thickness, amplitude, and frequency of corrugation on the buckling axial load. A quadratic model was developed to correlate the independent variables for maximum buckling load by using central composite design (CCD) method. Subsequently ANOVA, a statistical tool is used to analyse and compare the different combinations and finding the most influential factor on buckling load. Response surface methodology is adopted to review the interaction models between the different combinations.

Optimization of dyeing parameters of cotton standardized samples for laundry test of dye transfer inhibition program

In order to prevent light-colored clothes from being stained by dyes released from dark clothes during the washing process, some new-type washing machines have developed the dye transfer inhibition washing program. However, there is no certified reference materials for the test of dye transfer inhibition function. To this end, cotton fabric and reactive dyes are used as experimental materials to prepare standardized samples to evaluate the dye transfer inhibition function of washing machines. Firstly, the single factor analysis method is used to analyze the significance of the dyeing parameters including dye dosage, dyeing temperature, sodium sulfate dosage and sodium carbonate dosage. Secondly, a 4-factor 5-level experimental design and theoretical prediction of the best dyeing parameters are successively carried out through central composite design and response surface method. Two evaluation indicators, the dye release amount of the standardized sample of dyed fabric and the color difference value of the standardized sample of white fabric after washing, are proposed as the response values for response surface analysis to search the optimal dying parameters in theory. The optimal dyeing parameters obtained through response surface analysis are that the dosage of dye is 5.63% (owf), the dyeing temperature is 60 °C, the dosage of sodium sulfate is 93.60 g/L, and the dosage of sodium carbonate is 15 g/L. Experimental results indicate that the standardized samples prepared with optimal dyeing parameters can effectively distinguish the dye transfer inhibition function of washing machines.

A calculation model of the normal coefficient of restitution based on multi-factor interaction experiments

As a key input parameter for simulating the moving trail of a rockfall, the magnitude of the normal coefficient of restitution (Rn) directly affects the prediction accuracy. However, few comprehensive and accurate calculation models are available for reference, owing to the fact that Rn is jointly controlled by multiple factors with the influence mechanisms being complex. Considering the interactive effects of influencing factors and the non-linear relationships between some influencing factors and Rn, the combined effects of the seven influencing factors on Rn were first investigated in this study by the response surface methodology–central composite design (RSM–CCD) method. The effects of the seven main factors on Rn were all significant; the results of the regression and variance analysis indicate that for non-angular blocks, the degree of influence of these factors is given by the following sequence: shape factor (η) > Schmidt hardness of the block (SHV1) > impact angle (θ) > rotational speed (ω) > Schmidt hardness of the slope surface (SHV2) > incident velocity (V) > block size (d). However, for angular blocks, the sequence is θ > SHV1 > ω > η > V > SHV2 > d. There are also many interaction parameters that had significant effects on the Rn of the test blocks (for non-angular blocks: η–θ > d–η > η–ω > V–d > SHV1–η > SHV1–θ; for angular blocks, d–θ > SHV1–d > V–η > SHV1–ω > d–η > η–ω > SHV1–η), the effects of which were examined via contour and three-dimensional surface plots. Based on the conclusions of these experiments (the determined significant influence parameters of Rn) and the previous single-factor experiments, the SPSS19.0 software was used to perform multivariate non-linear regression on the test results. Consequently, two Rn value calculation models of both non-angular and angular blocks were established. Through a contrastive analysis of the measured values of field tests and the predicted values of various models, the prediction ability of models “a” of the two types of blocks closely approached the measured values, with average deviations of only 3.4% and 9.8%; thus, these models can basically be used to achieve an accurate prediction of Rn values under various conditions. The models obtained in this study consider the comprehensive influences of various factors, which include not only the effects of all the main controlling factors, but also those of the interactions between them. Thus, these models can more accurately reflect the energy loss in the process of rockfall impact, which is helpful to improve the prediction accuracy of rockfall motion paths and is capable of providing a more reliable reference for the prevention and control of rockfall disasters.

Tough dual-network GAMAAX hydrogel for the efficient removal of cadmium and nickle ions in wastewater treatment applications

A dual network Poly(Acrylamide-co-Acrylic Acid)/Xanthan Gum (XG) hydrogel incorporated with graphene oxide (GAMAAX) was synthesized through free radical polymerization method for the removal of heavy metal ions. The GAMAAX hydrogel was characterized by Fourier transform infrared spectrum (FTIR), environmental scanning electron microscope (ESEM), energy dispersive X-ray spectroscopy (EDS) and rheology tests. The adsorption capacity and electrical conductivity of GAMAAX hydrogel was investigated for varying pH range and adsorption dosage by central composite design (CCD) method. The results indicated that the heavy metal ion adsorption followed the pseudo-second-order kinetic model, and conformed Freundlich adsorption model with substantial adsorption capacities of 312.15 mg/g and 185.0 mg/g for Cd2+ and Ni2+, respectively. In addition to a great single-ion affinity for Cd2+ and Ni2+ ions, it also proved a remarkable dual-ion adsorption capability with the adsorption of 259.25 mg/g and 80.75 mg/g Cd2+ and Ni2+, respectively. Moreover, less than 20% reduction in the adsorption capacity after four adsorption–desorption cycles, verified the reusability of the GAMAAX hydrogel for the practical and large-scale wastewater treatment applications.

Degradation of anthraquinone dye reactive blue 19 using persulfate activated with Fe/Mn modified biochar: Radical/non-radical mechanisms and fixed-bed reactor study

In this study, a heterogeneous activator was prepared via the Fe/Mn modification of sludge-derived biochar (Fe/MnBC) to achieve high-efficiency activation of persulfate (PS) for reactive blue 19 (RB19) degradation. The morphologies and chemical states of Fe/MnBC were examined by various characterizations. A comprehensive assessment was conducted to reveal the effects of biochar preparation conditions and system reaction conditions. According to the results of scavenger quenching experiments and electron paramagnetic resonance (EPR) testing, the mechanisms of Fe/MnBC combined PS system on RB19 degradation were proposed, including radical and non-radical mechanisms. The formation and involvement of sulfate radical (SO4·−), hydroxyl radical (OH·), and singlet oxygen (1O2) were proved in this system, and Fe(IV)/Mn(VII) was also speculated to participate in the non-radical degradation process. These findings give a new insight into the mechanisms of PS activated by metal-biochar composite. Besides, fixed-bed reactor (FBR) experiments indicated that the Fe/MnBC has considerable PS activation potential for dyes removal. The degradation process was further modeled by the central composite design (CCD-RSM) and artificial neural networks (ANN) methods. The statistical metrics and prediction indicated that the prediction results of ANN model were better than CCD-RSM model, and the ANN model could perfectly predict the reaction process of Fe/MnBC FBR for engineering applications.

Natural Frequency Prediction Method for 6R Machining Industrial Robot

The industrial robot machining performance is highly dependent on dynamic behavior of the robot, especially the natural frequency. This paper aims at introducing a method to predict the natural frequency of a 6R industrial robot at random configuration, for improving dynamic performance during robot machining. A prediction model of natural frequency which expresses the mathematical relation between natural frequency and configuration is constructed for a 6R robot. Joint angles are used as input variables to represent the configurations in the model. The quantity and range of variables are limited for efficiency and practicability. Then sample configurations are selected by central composite design method due to its capacity of disposing nonlinear effects, and natural frequency data is acquired through experimental modal test. The model, which is in form of regression equation, is fitted and optimized with sample data through partial least square (PLS) method. The proposed model is verified with random configurations and compared with the original model and a model fitted by least square method. Prediction results indicate that the model fitted and optimized by PLS method has the best prediction ability. The universality of the proposed method is validated through implementation onto a similar 6R robot.