Multi-objective Optimization Analysis Research Articles

Chemical treatment in 3D dental model production for clear aligners via additive manufacturing: a comprehensive evaluation

Purpose Stereolithography (SLA) additive manufacturing (AM) technique has enabled the production of inconspicuous and aesthetically pleasing orthodontics that are also hygienic. However, the staircase effect poses a challenge to the application of invisible orthodontics in the dental industry. The purpose of this study is to implement chemical postprocessing technique by using isopropyl alcohol as a solvent to overcome this challenge. Design/methodology/approach Fifteen experiments were conducted using a D-optimal design to investigate the effect of different concentrations and postprocessing times on the surface roughness, material removal rate (MRR), hardness and cost of SLA dental parts required for creating a clear customized aligner, and a container was constructed for chemical treatment of these parts made from photocurable resin. Findings The study revealed that the chemical postprocessing technique can significantly improve the surface roughness of dental SLA parts, but improper selection of concentration and time can lead to poor surface roughness. The optimal surface roughness was achieved with a concentration of 90 and a time of 37.5. Moreover, the dental part with the lowest concentration and time (60% and 15 min, respectively) had the lowest MRR and the highest hardness. The part with the highest concentration and time required the greatest budget allocation. Finally, the results of the multiobjective optimization analysis aligned with the experimental data. Originality/value This paper sheds light on a previously underestimated aspect, which is the pivotal role of chemical postprocessing in mitigating the adverse impact of stair case effect. This nuanced perspective contributes to the broader discourse on AM methodologies, establishing a novel pathway for advancing the capabilities of SLA in dental application.

Unveiling the potential of machine learning in cost-effective degradation of pharmaceutically active compounds: A stirred photo-reactor study

In this study, neural networks and support vector regression (SVR) were employed to predict the degradation over three pharmaceutically active compounds (PhACs): Ibuprofen (IBP), diclofenac (DCF), and caffeine (CAF) within a stirred reactor featuring a flotation cell with two non-concentric ultraviolet lamps. A total of 438 datapoints were collected from published works and distributed into 70% training and 30% test datasets while cross-validation was utilized to assess the training reliability. The models incorporated 15 input variables concerning reaction kinetics, molecular properties, hydrodynamic information, presence of radiation, and catalytic properties. It was observed that the Support Vector Regression (SVR) presented a poor performance as the ε hyperparameter ignored large error over low concentration levels. Meanwhile, the Artificial Neural Networks (ANN) model was able to provide rough estimations on the expected degradation of the pollutants without requiring information regarding reaction rate constants. The multi-objective optimization analysis suggested a leading role due to ozone kinetic for a rapid degradation of the contaminants and most of the results required intensification with hydrogen peroxide and Fenton process. Although both models were affected by accuracy limitations, this work provided a lightweight model to evaluate different Advanced Oxidation Processes (AOPs) by providing general information regarding the process operational conditions as well as know molecular and catalytic properties.

Multi-objective optimization analysis of air-cooled heat dissipation coupled with thermoelectric cooling of battery pack based on orthogonal design

As the energy supply core of electric vehicles (EVs), the battery's performance is closely related to its temperature. Therefore, the battery thermal management system (BTMS) plays a crucial role in ensuring the vehicle's driving safety and power performance. This paper proposes the air cooling as the primary heat dissipation method, combined with a semiconductor refrigeration sheet (SRS) to improve heat transfer and reduce local high temperature. Firstly, determine the optimum air volume with the U-type air-cooled structure. Additionally, orthogonal analysis is used to investigate the inlet and outlet locations, the front deflector position and length of the shunt chamber, and the rear deflector position on the air-cooling effect. Then, through multi-objective optimization, the optimal air-cooled structure is selected based on the air supply pressure drop. The chosen structure is the same-side dual-exit ventilation with a front deflector located 150 mm from the near-wind end wall and a length of 30 mm, and a rear deflector located 100 mm from the far-wind end wall. Finally, SRS coupled air cooling is used to dissipate the heat. Four SRSs are used to analyze the effect of mounting position and different currents on the battery pack. The findings suggest that the optimal placement for SRSs is parallel to the lower end of the battery module at the far-wind end. When SRSs pass a current of up to 0.4A at 37 ℃, the entire battery module can complete 7200 s discharge with 0.5C. This leads to a decrease in the maximum temperature (Tmax) to 46.09 ℃ and a drop in the temperature uniformity index (δT) to 1.36.

Multi-objective optimization and uncertainty quantification for inductors based on neural network

Purpose The prolonged training time of the neural network (NN) has sparked considerable debate regarding their application in the field of optimization. The purpose of this paper is to explore the beneficial assistance of NN-based alternative models in inductance design, with a particular focus on multi-objective optimization and uncertainty analysis processes. Design/methodology/approach Under Gaussian-distributed manufacturing errors, this study predicts error intervals for Pareto points and select robust solutions with minimal error margins. Furthermore, this study establishes correlations between manufacturing errors and inductance value discrepancies, offering a practical means of determining permissible manufacturing errors tailored to varying accuracy requirements. Findings The NN-assisted methods are demonstrated to offer a substantial time advantage in multi-objective optimization compared to conventional approaches, particularly in scenarios where the trained NN is repeatedly used. Also, NN models allow for extensive data-driven uncertainty quantification, which is challenging for traditional methods. Originality/value Three objectives including saturation current are considered in the multi-optimization, and the time advantages of the NN are thoroughly discussed by comparing scenarios involving single optimization, multiple optimizations, bi-objective optimization and tri-objective optimization. This study proposes direct error interval prediction on the Pareto front, using extensive data to predict the response of the Pareto front to random errors following a Gaussian distribution. This approach circumvents the compromises inherent in constrained robust optimization for inductance design and allows for a direct assessment of robustness that can be applied to account for manufacturing errors with complex distributions.

Parametric Optimization of Friction Stir Welding of AA6061-T6 Samples Using the Copper Donor Stir-Assisted Material Method

This study presents an optimization of the process parameters for the effect of copper (Cu) donor material percentage on the friction stir welding (FSW) of AA6061-T6 alloy. Extensive factorial experiments were conducted to determine the significance of the rotational speed (ω), the transverse speed (v), the interface coefficient of friction (μ), and the Cu donor material percentage in the plunge, left, right, and downstream zones. Design Expert 13 software was used to identify the number of simulation experiments to be conducted using the Abaqus simulation software. From Design Expert 13, which is a thorough multi-objective optimization analysis software, we were able to identify ideal welding parameters such as a rotational speed of 1222 rpm, transverse speed of 1.1 mm/s, the coefficient of friction of 0.9, and a 19% donor material percentage for the plunge zone. Significant findings demonstrate that increasing the Cu donor material substantially reduced the temperature from 502 °C to 134 °C when the Cu content is increased from 0% to 50%. This integrated modeling and optimization approach provides a practical procedure to identify the best experimental parameters for the process and a new understanding to guide advances for high-quality FSW of aluminum alloys. This work offers a methodology for optimizing the FSW parameters aligned with multifaceted thermomechanical physics.

Energy absorption characteristics and multi-objective optimization of 3D bionic negative Poisson’s ratio honeycomb

This paper introduces a new bionic cell element with a negative Poisson’s ratio, which is derived from the traditional re-entrant hexagonal cell element. By incorporating rotation into the two-dimensional (2D) cell element, a three-dimensional (3D) cell element is formed, which exhibits enhanced mechanical properties. The 3D honeycomb is obtained by arraying the 3D cell elements and is enhanced by adding longitudinal ribs to the honeycomb. The present study employs the ABAQUS/EXPLICIT software to investigate the correlation between various compression velocities and the crashworthiness and energy absorption capacity of a given system. The effects of different compression velocities on the 3D honeycomb deformation pattern and energy absorption performance are analyzed. The simulation results show that the new bionic negative Poisson’s ratio honeycomb has better crashworthiness and energy absorption capacity than the traditional re-entrant hexagonal honeycomb. The nominal stress-strain curves of different honeycombs and the energy absorbed per unit mass of honeycomb are compared and analyzed, and the NSGA-II genetic algorithm is used to perform a multi-objective optimization analysis of the peak stress and energy absorbed per unit mass of honeycomb. The results show that the optimized honeycomb can increase the absorbed energy per unit mass by 7.5 % when the peak stress is the same as the initial honeycomb, while the peak stress can be reduced by 9.6 % when the absorbed energy per unit mass is the same.

Research on tribological performance of textured thrust bearing using gray relational degree and improved multi-objective water circulation algorithm

To enhance the loading capacity of the oil film, reduce the lubricating oil temperature, and minimize the deformation of the friction pairs, three types of concave textures were proposed. The tribological properties of the thrust bearing underwent multi-objective optimization analysis using the two different methods. By comparing the two methods, it was found that the improved algorithm (MOWCA) demonstrated better optimization performance. Under this algorithm, when the texture shape was triangular, the number of textures was 18n, the size was approximately 0.135 mm, and the spacing between textures was approximately 4.6 mm, the loading capacity of the oil film increased by approximately 26.8%, and the temperature decreased by approximately 0.46%.

Multi-objective Optimization and Performance Analysis of Dual-Rotor Hub Motor Based on Comprehensive Sensitivity Stratification

Multi-objective Optimization and Performance Analysis of Dual-Rotor Hub Motor Based on Comprehensive Sensitivity Stratification

Interpretable GBDT model-based multi-objective optimization analysis for the lateral inlet/outlet design in pumped-storage power stations

Abstract The uneven velocity distribution formed at the lateral inlet/outlet poses a significant risk of damaging the trash racks. Reasonable design of the inlet/outlet structures requires the consideration of two major aspects: the average velocity (Vm) and the coefficient of unevenness (Uc). This paper developed an optimization framework that combines an interpretable Gradient Boosting Decision Tree (SOBOL-GBDT) with a Non-dominated Sorting Genetic Algorithm (NSGA-II). 125 conditions are simulated by performing CFD simulations to generate the dataset, followed by GBDT implemented to establish a nonlinear mapping between the input parameters including vertical (α) and horizontal (β) diffusion angles, diffusion segment length (LD), channel area (CA), and the objectives Uc and Vm. The SOBOL analysis reveals that in Uc prediction, CA and α play more significant roles in the model development compared to β and LD. Besides, GBDT is observed to better capture interactive effects of the input parameters compared with other machine learning models. Subsequently, a multi-objective optimization framework using GBDT-NSGA-II is developed. The framework calculates the optimal Pareto front and determines the best solution using a pseudo-weight method. The results demonstrate that this framework leads to significant improvements in flow separation reduction in the diffusion segment and the normalized velocity distribution. The SOBOL-GBDT-NSGA-II framework facilitates a rational and effective design of the inlet/outlet.

Multi-objective size optimization and economic analysis of a hydrogen-based standalone hybrid energy system for a health care center

The main contribution of this study is the multi-objective size optimization of a standalone hybrid energy system (HES) to supply the electricity demand of a health care center in Kerman province, south of Iran, in which fuel cell, photovoltaic, and diesel generator produces electricity, while the combination of electrolyzer and hydrogen tank is the storage system. The non-dominated sorting genetic algorithm II (NSGA-II) is applied to find the Pareto front of optimal solutions, where the loss of power supply probability (LPSP) and total net present cost (TNPC) are the objective functions. The results show that considering operating reserve and uncertainty, the TNPC increases 30.50% compared to the base load. Moreover, the fuel cell has a smaller share of energy production, which is due to the high cost of the storage system, while about 50% of the demand load is provided by DG in the cold seasons. The sensitivity analysis reveals that TNPC decreases by 8.93% for a 50% reduction of the storage system cost, while increases by 24.55% for a 50% increase in the fuel cost. Moreover, AHP/TOPSIS-based multi-criteria decision-making is implemented to find the best solution according to the opinions of three different managers/experts, which shows that the HES with LPSP = 0.2503% and TNPC = 2.1038 × 106 $ is the best solution in which uncertainties and operating reserve are also considered.

Multi-objective optimization and theoretical analysis of re-entrant structure with enhanced mechanical properties

In response to the challenge of low stiffness and energy absorption capacity, various designs of negative Poisson's ratio (NPR) structures have emerged. However, these designs often lack a comprehensive analysis of other mechanical properties, leading to subpar mechanical performance. Previous studies have explored the mechanical properties response of enhanced re-entrant honeycombs (ERH) under impact conditions, revealing limitations in optimizing the structure's performance with a single objective. Therefore, this study aims to enhance ERH structural parameters to achieve superior mechanical performance through theoretical derivations and geometric optimizations. The results demonstrate that the proposed theoretical model is consistent with finite element analysis and response surface (RS) predictions. Expressions for Young's modulus, specific energy absorption, and compressive strength are proposed, facilitating the identification of structural parameters that meet specific requirements during reverse design. Furthermore, a multi-objective optimization approach optimizes the geometric parameters based on maximum energy absorption and compressive strength. The mechanical behavior of the optimized ERH is investigated using the finite element method, revealing the energy absorption capacity of 13.78 J/g while maintaining Poisson's ratio at -1.06. Additionally, the deformation mode of the optimized structure showcases enhanced stability compared to traditional honeycomb structures. The theoretical model and RS method were used to guide the design of ERH and promote the application of NPR structures.

Design and Optimization of 3D-Printed Variable Cross-Section I-Beams Reinforced with Continuous and Short Fibers.

By integrating fiber-reinforced composites (FRCs) with Three-dimensional (3D) printing, the flexibility of lightweight structures was promoted while eliminating the mold's limitations. The design of the I-beam configuration was performed according to the equal-strength philosophy. Then, a multi-objective optimization analysis was conducted based on the NSGA-II algorithm. 3D printing was utilized to fabricate I-beams in three kinds of configurations and seven distinct materials. The flexural properties of the primitive (P-type), the designed (D-type), and the optimized (O-type) configurations were verified via three-point bending testing at a speed of 2 mm/min. Further, by combining different reinforcements, including continuous carbon fibers (CCFs), short carbon fibers (SCFs), and short glass fibers (SGFs) and distinct matrices, including polyamides (PAs), and polylactides (PLAs), the 3D-printed I-beams were studied experimentally. The results indicate that designed and optimized I-beams exhibit a 14.46% and 30.05% increase in the stiffness-to-mass ratio and a 7.83% and 40.59% increment in the load-to-mass ratio, respectively. The CCFs and SCFs result in an outstanding accretion in the flexural properties of 3D-printed I-beams, while the accretion is 2926% and 1070% in the stiffness-to-mass ratio and 656.7% and 344.4% in the load-to-mass ratio, respectively. For the matrix, PAs are a superior choice compared to PLAs for enhancing the positive impact of reinforcements.

Multi-objective optimization of twisted tri-lobed tube geometry for enhanced heat transfer and reduced resistance in turbulent flow

This study presents a detailed multi-objective optimization analysis of turbulent flow and heat transfer within a twisted tri-lobed tube, employing an integrated approach that combines Central Composite Design (CCD), Response Surface Methodology (RSM), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto frontier analysis, Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The investigation focuses on optimizing key design parameters: Reynolds number (Re), twist pitch (P), and minor circle radius (r), targeting Nusselt number (Nu), Poiseuille number (fRe), and Performance Evaluation Criterion (PEC) as optimization objectives. Our major findings reveal that the developed second-order regression model precisely predicts the influences of these parameters on Nu, fRe, and PEC, with sensitivity analysis showing Re's strong positive impact on Nu and fRe, P's negative effect on fRe, and r's minimal impact. The study notably highlights the significant Nu enhancement with Re increase. Utilizing multi-objective optimization, we identified a Pareto optimal set, and through LINMAP and TOPSIS methods, derived compromise solutions that optimally balance heat transfer efficiency, flow resistance, and overall performance. These outcomes underscore the critical importance of parameter selection to meet specific engineering needs, offering a strategic framework for enhancing twisted tri-lobed tube designs.

Multi-objective structure optimization and performance analysis of catalytic micro-reactor channel designed by an improved topology optimization model

Previous micro-reactor channel studies mainly focus on the limited design space or structure layout, rarely exploits design forms in which the procedural topology structure evolves freely. Therefore, a multi-objective topology optimization model are proposed for the inverse free evolution of micro-reactor channels, which can synergistically balance the effects among three objectives of reaction rate, flow loss and heat transfer to achieve optimal comprehensive performance. The topology optimization problem is formulated based on the material density-based design variable. The fluid dynamics response of design variables at each optimization iteration is updated by solving multi-physics governing equations, objective functions and variable constraints. Adjoint-based sensitivity analysis and GCMMA algorithm are used to obtain the gradient information of objective functions and update design variables, respectively. Additionally, the strict volume constraints and proposed pseudo-design-domain concept are introduced into the multi-objective function. In numerical examples, the influence of different Re conditions and different multi-objective weight-ratios on optimal designs is investigated, and their regular evolution mechanisms and the trade-off between performance optimization and structure evolution are analyzed. Then, the optimization principles for topology structure and performance parameters under the control of multi-objective functions are revealed. Finally, the comprehensive performance of micro-reactors under topology optimization method is compared with that of traditional micro-reactors to demonstrate the effectiveness of the improved method and reveal the physical mechanisms of multi-objective performance enhancement. The comparison results show that topology optimization designs can achieve 86.1% growth in reaction rate and 47.1% reduction in flow loss, and comprehensive performance coefficients have also been significantly optimized.

Exergetic performance optimization and thermoeconomic analysis of a variable compression ratio diesel engine fueled with distilled plastic oil and diesel doped with nanographene

Due to the fast depletion of fossil fuels, enormous concerns about environmental pollution, and advocacy for waste-to-energy drives from the global perspective, compression ignition engines need a sustainable alternative fuel source. Enormous plastic wastes were generated in health sectors, particularly during post-pandemic. In this context, the study intends to introduce a reasonable solution for such waste plastics recycling by converting them into liquid oil by pyrolysis followed by the distillation process. Distilled waste plastic oil (DPO) extracted from medical plastic waste is a potential alternative diesel source. The performance of the engine significantly increases when nanographene is added with DPO/diesel blends, which act as a combustion improviser. The energy efficiency (η1), exergy efficiency (η2), and brake-specific fuel consumption (BSFC), which are regarded as key performance indicators, exhibited promising results when operated with 20% DPO +100 ppm nanographene (20DPO100G) emulsified fuel mixture as compared to normal diesel. When compared to diesel and other fuel combinations, the energy efficiency (η1) and exergy efficiency (η2) for 20DPO100G fuel mixture were found enhanced by 5.78% and 10.9%, respectively, and lowest by 14.7% for BSFC in comparison to diesel. The optimum energy efficiency, exergy efficiency, and minimum BSFC were obtained for the test engine from response surface methodology multi-objective optimization analysis as 31.44%, 22.12%, and 0.32 kg/kW-hr, respectively, for the composite desirability, D of 0.974. The 100 ppm nanographene emulsified distilled waste plastic pyrolysis oil and diesel blend has the lowest relative cost variation of −14.583.

Efficacy analysis of NSGAII and multi-objective particle swarm optimization (MOPSO) in agent based weapon target assignment (WTA) model

Efficacy analysis of NSGAII and multi-objective particle swarm optimization (MOPSO) in agent based weapon target assignment (WTA) model

Analysis of a novel high-speed homogenizer technique for methyl ester production using waste cooking oil: Multi-objective optimization and energy analysis

While fossil fuels continue to be the predominant energy source, the use of renewable energy is consistently growing. Biodiesel, with its various advantages over traditional fossil fuels, stands out as a promising alternative fuel that is produced from a sustainable and abundant waste cooking oil (WCO) via a transesterification reaction (due to the low acid value of WCO) with alcohol (MeOH) and homogeneous catalyst (KOH) in a high-speed homogenizer. Biodiesel is produced in the industry using conventional mechanical stirring techniques, which are time-consuming and less efficient. It has been recently reported that high-speed homogenizers have the scale-up potential as per industrial requirements and can significantly reduce the transesterification/esterification reaction time. In the present investigation, the Taguchi technique has been applied for the design of experiments and multi-response process optimization using a composite desirability approach. The optimized values observed for yield, yield efficiency, and pour point were 99.18 ± 0.2%, 5.4 × 10–3 g/J, and − 14.9 ± 0.5 °C, respectively, at alcohol to oil molar proportion of 7:1, KOH amount of 0.5 wt%, rotor speed of 14000 rpm and 10 min of process time. The physicochemical properties of produced biodiesel were found within EN 14214 and ASTM D6751 standards limits.

Design and test of tangential and longitudinal-axial threshing and separating unit for wheat

To achieve low loss and efficient wheat harvest in the Huanghuaihai region of China, this paper designed a tangential and longitudinal-axial threshing and separating unit (TLTSU). Key components such as the tangential threshing rotor (TTR), longitudinal-axial threshing rotor (LTR), and tangential concave were designed. Dynamic balance simulation of the threshing rotors was done to obtain the reaction forces at supporting ends of the threshing rotors and the amount of unbalance, and the amount of unbalance of the LTR was corrected to meet the standard requirement. Response surface optimization experiments of wheat threshing were conducted using throughput, TTR speed, and LTR speed as influencing factors and entrainment loss rate and impurity rate as test indicators. The regression models for the threshing device's entrainment loss rate and impurity rates were established. Through multi-objective parameter optimization analysis, the optimal threshing parameter combination for TLTSU was determined to be throughput of 8 kg s−1, TTR speed of 557 r·min−1, and LTR speed of 900 r·min−1. A validation experiment was conducted on this parameter combination, and the results showed that the entrainment loss rate was 0.27 % and the impurity rate was 28.23 %. The relative errors between the evaluation indicators and the predicted values of their regression models were 3.85 % and 1.4 %, respectively, which were less than 5 %, which proved the correctness of the regression model and provided a reference for the design of threshing and separating unit of wheat combine harvester.

Multi-objective optimization analysis of combined heating and cooling transcritical CO2 system integrated with mechanical subcooling utilizing hydrocarbon mixture based on machine learning

To meet the simultaneous needs of high temperature disinfection and freezing in the field of food processing, a new concept of combined heating and cooling transcritical CO2 system integrated with dedicated mechanical subcooling utilizing hydrocarbon mixture is proposed. The system performance in terms of thermodynamics, economy and environment is studied and compared with the baseline combined heating and cooling transcritical CO2 system and four traditional combined heating and cooling solutions, considering the influence of temperature glide and the heat transfer deterioration. The new proposed system is further optimized by using the machine learning method of artificial neural network and non-dominated sorting genetic algorithm. Multi-objective optimization is conducted considering the objective function of energy efficiency, initial capital cost and life cycle carbon emissions of the new system, to obtain the optimum components and concentration ratio of the hydrocarbon mixture. The results indicate the thermodynamic performance and environmental benefits of subcooling subsystem with hydrocarbon mixture are better than those of the pure system. In contrast to that using pure R290 and R601, the coefficient of performance is enhanced by 8.20 % and 8.13 % and the life cycle carbon emission is reduced by 8.54 % and 9.31 %, respectively, when R290/R601 (50/50) is used. However, the initial capital cost is 9.25 % and 10.23 % higher than that of pure R290 and R601, respectively. Finally, the hydrocarbon mixture corresponding to the optimal design point is R1270/R601a (53/47), the corresponding discharge pressure is 12.86 MPa, and the subcooling degree is 37.50 °C. This study can provide a theoretical reference for the application of CO2 refrigeration and heat pump technology.

A multi-objective optimization and multi-attribute decision-making analysis for technical-thermodynamic-economic evaluation considering the rock damage on production performance of hot dry rock geothermal resources

In the long-term mining of geothermal resources in hot dry rock (HDR), the change of thermal stress and pore pressure will increase fracture conductivity evolution, further improving production performance. The optimization and decision-making of the development scheme based on the impact of damage from fractures have yet to be reported. The damage to fractures is essential in designing and adjusting geothermal resource development schemes, particularly in selecting optimal schemes. Therefore, the production performances of HDR resources under different parameters are analyzed to establish a database. Then, minimizing flow resistance, maximizing net power, and maximizing economic benefits are set as optimization goals. Various injection-mining parameters and fracture characteristics are treated as decision variables. Multi-objective optimization and multi-attribute decision analysis is conducted to obtain optimal schemes. Finally, optimal schemes are evaluated and compared, considering damage and non-damage scenarios. Results show that the NSGA-II algorithm is more suitable for optimizing geothermal development questions. Net power and economic benefits of the optimal scheme considering damage increase by 45.84 % and 21.35 % compared to the control scheme with damage. For the non-damage scenario, the above values increased by 31.55 % and 5.15 %, respectively. Compared to not considering the damage, higher mass flow and well spacing of optimal scheme can be selected for the case when damaged. Moreover, the parametric design of the optimal scheme becomes more conservative as the production cycle increases.