Multi-objective Parameter Optimization Research Articles

Optimization of Extraction Parameters of Ethanol Extracts of Propolis Samples Using Artificial Neural Network and Moth-Flame Optimization Algorithm

Abstract In this study, the optimum values of propolis ethanol extracts parameters were determined with the use of single and multi-objective optimization procedures. The euclidean distance approach was used in the multi-objective optimization process. Firstly, propolis was extracted using water with ethanol contents 40, 50, 60, 70 and 80% for 8, 10, 12, 16, 20 and 24 h. Then, total phenolic content (TPC) and ferric reducing antioxidant power (FRAP) activities of all extracts were determined. With the obtained data a prediction model was produced with the use of artificial neural networks (ANN), and optimization was performed using a moth-flame (MFO) algorithm. The best prediction models for the TPC and FRAP were observed in 2-5-1 and 2-5-1 network architecture with the mean absolute percentage error (MAPE) values, 5.126 and 2.451%, respectively. For maximum TPC, the extraction parameters were determined as ethanol content 57.5% and extraction time 13.56 h. To maximize FRAP, the optimized extraction parameters were ethanol content 72.03% and extraction time 18.04 h. The optimum extraction conditions for both maximum values of the studied assays were ethanol content 70.03% and extraction time 16.93 h. The study concluded that the integrated ANN and MFO algorithm system can be used in single and multi-objective optimization of extraction parameters. The established optimization model can save time, money, labor and energy.

Multi-objective optimization of machining parameters in hybrid powder-mixed EDM process by response surface methodology and normalized fuzzy logic algorithm

Multi-objective optimization of machining parameters in hybrid powder-mixed EDM process by response surface methodology and normalized fuzzy logic algorithm

A new grey relational analysis application in analytical chemistry: Natural deep eutectic solvent as a green extractant for HPLC determination of lamotrigine in plasma

Developing new, eco-friendly solvents which would meet technological and economic demands is perhaps the most popular aspects of Green Chemistry. Natural deep eutectic solvents (NADES) fully represent green chemistry principles. In this sense a new green vortex-assisted NADES-based microextraction (VA-NADES-ME) was established, for the first time, for the extraction of lamotrigine from human plasma samples using NADES, as a green extractant alternative to hazard organic solvents. Then extract was determined by high performance liquid chromatography with ultraviolet detection (HPLC–UV). The mixture of (choline chloride/ethylene glycol) as NADES was found to be the best candidate for extracting lamotrigine. Grey relational analysis-based Taguchi method was utilized as a chemometric protocol for multiobjective optimization of different HPLC performance parameters. The optimum mobile phase was a mixture of water with 0.3 % triethylamine in 0.02 M H3PO4 (adjusted to pH 5.5) and acetonitrile (70:30 v/v) with an overall pH 6. Finally, the confirmation test was performed to validate the predicted optimal results and established an improvement of 32.84 % from the initial design. The mobile phase was eluted in an isocratic mode with a flow rate of 0.8 mL/min and UV detection at 310 nm. The retention times of lamotrigine and tinidazole (internal standard) were 4.7 and 3.4 min, respectively. Proposed method was linear over the concentration range of 0.1–15.0 μg mL−1. The greenness profile was assessed using Analytical GREEnness metric approach which assured the greenness of the proposed method compared with other reported methods.

Electric vehicles body frame structure design method: An approach to design electric vehicle body structure based on battery arrangement

Although the electric vehicles (EVs) have become the main development of the automotive industry, there is still very limited knowledge about the design method of EVs’ body structure. This paper substantially increases this limited knowledge by developing an EV’s aluminum alloy body frame structure design (EVFD) method based on battery arrangements. The main concept of the EVFD approach is the development of the parametric knowledge base to facilitate performance driven design of body structure in concept design stage, including the battery sub-libraries designed by vehicle power performances and the modular body frame structure sub-libraries and cross section of profile sub-libraries designed by structure performances. The layout of vehicle is firstly determined based on battery volume which calculated on the basis of power performance of EVs. And then, the body frame structures are designed by multi-load topology optimization and multi-objective parametric optimization based on the body structure performance, considering the battery layout and its mass distribution. The developed body structure knowledge base is screened through comparing their structural mechanical properties. The feasibility and accuracy of selected EV’s aluminum alloy body frame structure is verified by experiment. In this paper, the case study demonstrates that the proposed EVFD method based on battery arrangement can be effectively used to design aluminum alloy body frame structure of EVs, which provides a reference to other EV’s body structure design. Due to the performance driven design concept employed, the designed body structure for EV meets the requirements of lightweight, dynamic performance, and structural mechanical properties.

Multi-objective optimization of process parameters in TIG-MIG welded AISI 1008 steel for improved structural integrity

This study investigates a parametric multi-objective optimization of the Tungsten Inert Gas-Metal Inert Gas (TIG-MIG) hybrid welding of AISI 1008 mild steel joints. A combined grey relational system theory and the Taguchi method was used for process optimization towards achieving a set of process parameter that maximizes both ultimate tensile strength and 0.2% yield strength for structural applications. An L-9 orthogonal array based on the Taguchi method was adopted for the experimental design matrix. Grey relational grading system was used to establish a single grade for the responses. Mathematical models for first- and second-order regressions were developed and optimum process parameter combination that optimizes the response was obtained. From the results, the gas flow rate had the most significant influence on the responses with a percentage contribution of 39.77%. Also, the second-order regression models had a higher coefficient of determination (R2) compared to the first-order regression for the two responses and, thus, represents the best fit for the process. The grey relational grade was improved by 0.0489 through process optimization. The interactive effects of process parameters and their effects on the responses are also illustrated by response surface plots. This study shows the effectiveness of the grey relational grading system in achieving a multi-objective optimization for the TIG-MIG welding process.

Multi-objective optimization of machining parameters during green machining of aerospace grade titanium alloy using Grey–Taguchi approach

This paper presents multi-objective optimization of aerospace-grade titanium alloy using the Gray–Taguchi approach. Experimentation has been conducted with varying cutting conditions according to Taguchi's L9 orthogonal array. Tool nose wear, tool flank wear, surface roughness and material removal rate are employed as process characteristics. The experimental outcomes are statistically analyzed using the Grey–Taguchi approach. Statistical and sensitivity analysis are also performed. Studies revealed that cutting speed and feed rate are having a significant influence on process performances. Micro analysis of cutting tool is also performed to study the kind of wear. Furthermore, chips were also analysed using scanning electron microscope to study its features and morphology to obtain a better insight into the process.

Shape optimization of piezoelectric energy harvesters of variable thickness

Recently, a model of a piezoelectric energy harvester (PEH), based on bimorph Kirchhoff–Love plate of variable shape and constant thickness was developed and subsequently discretized using isogeometric analysis (IGA). The model describes a wide class of non-conventional PEH configurations and can be paired with an optimization algorithm to identify the design shapes with the best outcomes in terms of the produced voltage and power. However, recent studies suggest that devices with variable thickness, in particular, devices of inverse trapezoidal shape, might produce higher power output. In this work, we investigate such possibility by extending the IGA PEH model to plates of variable thickness and performing a detailed parametric study for two families of through-thickness shapes: linear and quadratic. We explore the behavior of the first natural frequency, voltage Frequency Response Function (FRF), power FRF, and stress FRF, with their corresponding relation to the electrical resistance and the change in volume of the PEH. A multi-objective Kriging optimization of the thickness parameters is then performed to maximize the peak amplitude of the FRFs and minimize the volume of the substructure.

Dual ensemble online modeling for dynamic estimation of hot metal silicon content in blast furnace system

Hot metal silicon content (HMSC) is usually utilized to measure the quality of hot metal and reflect the thermal status of blast furnace (BF) system. However, most state-of-the-arts ignore the time-varying behavior of BF ironmaking process, which are impractical. Accordingly, a novel dual ensemble online sequential extreme learning machine (DE-OS-ELM) is proposed to establish the online estimation model of HMSC, which can update the data-driven model with the latest operation data. Specifically, an online learning method with recursive modification is first proposed based on OS-ELM (referred to as RM-OS-ELM) to address the modeling with uncertainty. To heel, a dynamic forgetting factor is presented for the dynamic tracking capability enhancement and convergence acceleration. Furthermore, a final updating rule for sequential implementation is constructed by combining the output weights of OS-ELM and RM-OS-ELM based on their corresponding contributions on modeling. Considering the modeling accuracy and curve trend consistency, multiobjective parameter optimization model is also implemented to achieve the satisfactory performance. By taking the proposed DE-OS-ELM, the estimation model of HMSC is established using industrial data. Comprehensive experiments demonstrate that DE-OS-ELM-based HMSC estimation model is more feasible and practical.

4E analysis and multiobjective optimization of a PEMFC-based CCHP system with dehumidification

This paper proposes a novel PEMFC-based combined cooling, heating and power (CCHP) system with independent control of refrigeration and dehumidification, which is used in data centers with strict requirements on temperature and humidity. The effects of operating parameters of PEMFC (current density, hydrogen and oxygen pressure) and refrigeration system (hot water distribution ratio, mass flow of cooling water and chilled water) on the 4E (energy, exergy, economy and environment) are investigated. Meanwhile, multiobjective optimization of the above parameters is carried out. The results show that a high inlet pressure is beneficial to improve system efficiency and reduce annual greenhouse gas (GHG) emissions. An increase in the chilled water mass flow increases system efficiency and reduces annual GHG emissions, while an increase in the cooling water mass flow yields the opposite results. Moreover, an increase in the proportion of hot water entering the adsorption chiller causes system efficiency and the annual GHG reduction to initially decrease and then increase. Compared with the unoptimized system, the energy efficiency, exergy efficiency, system cost, GHG emission reduction, electric power, cooling power, heating power and dehumidification rate of the system increase by 5.5%, 0.77%, 10%, 21.27%, 12.87%, 5.37%, 45.73% and 1.53%, respectively.

Multiple-objective optimization in green fuel production via catalytic deoxygenation reaction with NiO-dolomite catalyst

This study investigates the multi-objective optimization of reaction parameters with response surface methodology (RSM) with central composite design (CCD) for the deoxygenation of waste cooking oil (WCO) over low cost-modified local carbonate mineral catalyst (NiO-Malaysian dolomite) into green fuel in the range of gasoline, kerosene and diesel. RSM was performed to study the effect of four operating parameters: temperature (390–430 °C), time (30–120 min), catalyst loading (1–10 wt%) and nitrogen flow rate (50–300 cm3/min). The results indicate that for maximum WCO conversion, deoxygenated oil and product yield, the optimum parameters of the deoxygenation reaction were at 410 °C, 60 min, 5.50 wt% of catalyst loading, and 175 cm3/min of N2. The green fuel properties testing (density, kinematic viscosity, flash point, cloud point, pour point, sulfur, carbon residue, cetane index, oxidation stability, acid value, iodine value and calorific value) and GC–MS analysis show that the product oil meets almost all the requirements of green diesel fuel and hydrocarbon biofuel standards for fuel application while the quadratic model proposed agreed with the experimental data (95% confidence) which indicates that the RSM can adequately predict the reaction products.

RESEARCH ON THE EFFECTS OF ROLLER-SPREADING PARAMETERS FOR NYLON POWDER SPREADABILITY IN ADDITIVE MANUFACTURING

The powder spreading process is one of the key processes in the powder-bed-based additive manufacturing (AM) technology. The roller-spreading parameters include the powder spreading layer thickness H, roller’s diameter D, roller’s rotational speed ω and translational velocity V, which have a major impact on the powder spreadability in AM processes. In this paper, the nylon powder was taken as the research object, and the discrete element method (DEM) was deployed to simulate the nylon powder spreading process by a roller. The three powder spreadability indicators including the deposition fraction, percent coverage and deposition rate were established. The central composite design (CCD) model was used to generate 30 groups of simulation cases. The regression models of three powder spreadability indicators were fitted by the response surface method (RSM). The analysis of variance was used to prove the accuracy and predicting effectiveness of regression models. In addition, the effect of process parameters on powder spreadability indicators was analyzed in detail. The results showed that the powder spreading layer thickness H was a leading influencing factor. The roller’s translational velocity V was a less important influencing factor. The roller’s diameter D and rotational speed ω had a slight influence on powder spreadability indicators. Both the H and D with V were determined as the main interactive factors on powder spreadability indicators. The three powder spreadability indicators were taken as the optimization goal, and the multi-objective optimization of roller-spreading parameters was carried out by the expectation method. The predicted optimal combination of powder spreading parameters and powder spreadability indicators were obtained. Moreover, the optimal results were verified through the experiments. The results showed that the predicted results of powder spreadability indicators were in good agreement with experimental results. The research results in this paper can provide guidance for the optimization of roller-spreading parameters in AM.

Multi-objective Optimization of High Speed Milling Parameters Based on Genetic Algorithm

In order to optimize the high-speed milling parameters and reduce the experimental work in the actual processing, a multi-objective optimization model is established. The improved genetic algorithm NSGA-II combined with penalty function method is used to solve the mathematical model, and the optimal cutting parameters are obtained when the high-speed milling conditions are known, which provides guidance for the process decision-making of high-speed milling.

A hybrid approach for prediction of machining performances of glass fiber reinforced plastic (Epoxy) composites

In recent years, Glass Fiber Reinforced Plastic (GFRP) composites are more potential materials for usage in various structural applications in aerospace and automobile industries, owing to its favorable properties, such as light weight, specific strength, high elastic modulus and excellent corrosion resistance. This paper presents a new hybrid approach for multi-objective optimization of machining parameters on GFRP composite using Taguchi coupled grey relational (GR) and desirability function (DF) approach. The end milling of GFRP composite was performed as per Taguchi’s L16 orthogonal array by considering three input parameters: viz cutting speed (V), feed rate (F) and depth of cut (D), each at four levels. The material removal rate (MRR), surface roughness (SR) and tool wear (TW) were chosen as output responses. The main effect plot was used to determine the optimum level of machining parameters on the multiple responses. An experimental result revealed that, the optimum level of parameters obtained by GR analysis is V2F3D4. Similarly, the optimum level of parameters obtained by DF approach is V2F4D4. Moreover, analysis of variance (ANOVA) was applied to examine the significance of the parameters. Based on the ANOVA results, it has been proved that the depth of cut was the most significant parameter, trailed by feed rate and cutting speed, respectively. It was also confirmed that the P-values of depth of cut for both analyses are 0.017 and 0.039, which is less than 0.05. Finally, the confirmation tests exhibited that the DA approach was employed better in terms of determining the optimum level of machining parameters as compared to GR analysis.

On the Lightweight Truss Structure for the Trash Can-Handling Robot

With the rapid development of cities, the automated and intelligent garbage transportation has become an important direction for technological innovation of sanitation vehicles. In this paper, a vehicle-mounted trash can-handling robot is proposed. In order to reduce the cost of the robot and increase the loading capacity of the intelligent sanitation vehicles, a lightweight design method is proposed for the truss structure of the robot. Firstly, the parameters of the robot that are related to the load are optimized by multi-objective parameter optimization based on particle swarm optimization. Then, the material distribution of the truss structure is optimized by topology optimization under multiple load cases. Finally, the thickness of the truss structure parts is optimized by discrete optimization under multiple load cases. The optimization results show that the mass of the truss structure is reduced by 8.72%, the inherent frequency is increased by 61.08%, and the maximum stress is reduced by 10.98%. The optimization results achieve the goal of performance optimization of the intelligent sanitation vehicle, and prove the feasibility of the proposed lightweight design method.

Multi-Objective Parameter Optimization of Flexible Support System of Optical Mirror

During the processing of an optical mirror, the performance parameters of the bottom support system would affect the surface forming accuracy of the mirror. The traditional bottom support system has a large unadjustable support stiffness, which increases the difficulty of unloading the impact force generated by the grinding disc. In response to this scenario, a flexible support system (FSS) consisting of 36 support cylinders with beryllium bronze reeds (BBRs) and rolling diaphragms (RDs) as key components is designed. It is necessary to analyze the key components of the support cylinder to reduce its axial movement resistance, ensure a consistent force output of each support point. First, the internal resistance model of a flexible support cylinder is established, and the main factors of internal resistance are then analyzed. Thereafter, the multi-objective structural parameters of the BBR and RD are simulated in ANSYS using the control variable method. The optimal structural parameters of BBR and RD are determined by simulation. Finally, experiments are performed on the RD ultimate pressure, internal resistance of the support cylinder, and consistency of the force output of the FSS. The experimental results show that the support cylinder with the optimized design has good force output consistency, which provides a theoretical basis for the application of FSS in optical mirror processing.

Experimental study and multi-objective optimization of process parameters during turning of 100Cr6 using C-type advanced coated tools

The drive of this research is to examine the machinability of 100Cr6 bearing steel using advanced C-type cutting tools. Experimental studies investigated the effects of machining variables on the surface quality, chip reduction coefficient and cutting force. Seven advanced coated tools were checked for characterization by micro hardness (VHN), adhesion quality, X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDXS). The experimental trials were planned by Taguchi’s L18 orthogonal array using a mixed-level design. Two numerical machining variables feed rate and cutting speed, and one categorical machining variable tool type was taken into consideration while a constant depth of cut was kept for all trails. A combined Taguchi-Satisfaction function distance measure approach was implemented for multi-response optimization. The most promising machining parameter setting for minimization of surface roughness, cutting force, and chip reduction coefficient was identified. The most important process parameter was found to be tool-type. Ceramics tools are found to be best trailed by WC coated tools under most of the conditions. Lower tool wear was observed in the CBN tool as compared to others.

Multi-objective optimization of process parameters in stamping based on an improved RBM–BPNN network and MOPSO algorithm

Multi-objective optimization of process parameters in stamping based on an improved RBM–BPNN network and MOPSO algorithm

Comprehensive performance analysis and optimal design of smart light pole for cooperative vehicle infrastructure system

Abstract On the basis of the finite element theory, in this study, a theoretical design and simulation practice to carry out steady-state static analysis, structural dynamics analysis, and electromagnetic interferencecharacteristics analysis on smart light poles, is combined. On this basis, a design of modular weight reduction and cost reduction was proposed, which realized the multi-objective parameter optimization of the smart light pole. The simulation results show that the mass of the optimized light pole can be reduced by 14.2%, and the material cost can be reduced by 14.7%. At the same time, the comprehensive performance of the optimized smart light pole can still meet the design requirements of the industry standards. The research results provide a reference for the lightweight design and the comprehensive analysis of the smart light pole in the future.

Dynamic analysis and multi-objective parameter optimization in multi-receiver wireless power transfer systems

Dynamic analysis and multi-objective parameter optimization in multi-receiver wireless power transfer systems

Multi-Objective Optimization of MIG Welding and Preheat Parameters for 6061-T6 Al Alloy T-Joints Using Artificial Neural Networks Based on FEM

To control the welding residual stress and deformation of metal inert gas (MIG) welding, the influence of welding process parameters and preheat parameters (welding speed, heat input, preheat temperature, and preheat area) is discussed, and a prediction model is established to select the optimal combination of process parameters. Thermomechanical numerical analysis was performed to obtain the residual welding deformation and stress according to a 100 × 150 × 50 × 4 mm aluminum alloy 6061-T6 T-joint. Owing to the complexity of the welding process, an optimal Latin hypercube sampling (OLHS) method was adopted for sampling with uniformity and stratification. Analysis of variance (ANOVA) was used to find the influence degree of welding speed (7.5–9 mm/s), heat input (1500–1700 W), preheat temperature (80–125 °C), and preheat area (12–36 mm). The range of research parameters are according to the material, welding method, thickness of the welding plate, and welding procedure specification. Artificial neural network (ANN) and multi-objective particle swarm optimization (MOPSO) was combined to find the effective parameters to minimize welding deformation and stress. The results showed that preheat temperature and welding speed had the greatest effect on the minimization of welding residual deformation and stress, followed by the preheat area, respectively. The Pareto front was obtained by using the MOPSO algorithm with ε-dominance. The welding residual deformation and stress are the minimum at the same time, when the welding parameters are selected as preheating temperature 85 °C and preheating area 12 mm, welding speed is 8.8 mm/s and heat input is 1535 W, respectively. The optimization results were validated by the finite element (FE) method. The error between the FE results and the Pareto optimal compromise solutions is less than 12.5%. The optimum solutions in the Pareto front can be chosen by designers according to actual demand.