Multi-objective Parameter Optimization Research Articles

Multi-objective optimization of machining parameters based on an improved Hopfield neural network for STEP-NC manufacturing

Aiming at solving the existing problems of machining parameters optimization for STEP-NC manufacturing, a method for multi-objective optimization of machining parameters based on an improved Hopfield neural network (IHNN) for STEP-NC manufacturing is proposed. In this method, a multi-objective optimization mathematical model of machining parameters compliant with STEP-NC is firstly established taking machining energy, machining time and machining cost as optimization objectives. Next, the IHNN for multi-objective optimization of STEP-NC machining parameters combining with Pareto theory, improved immune algorithm and non-monotone activation function is designed. Based on it, the optimal Pareto solutions of STEP-NC machining parameters are obtained, which intelligently realizes the multi-objective optimization of STEP-NC machining parameters and provides a decision support for the decision-maker. Finally, performance comparisons among the IHNN, classic non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO) are done by classic test functions with three objectives and multiple constraints which correspond to the mathematical model established in this paper, and its effectiveness and feasibility is verified by case study.

Multi-objective parameter optimization of the Z-type air-cooling system based on artificial neural network

The performance of electric vehicles is significantly influenced by the battery thermal management system (BTMS). In this article, the Z-type air cooling BTMS is studied, and a method combining an artificial neural network (ANN) model as a surrogate model with non-dominated sorting genetic algorithm II (NSGA-II) for multi-objective optimization is proposed to enhance the heat dissipation performance. The ANN model is trained by using 1529 sets of numerical simulation data of Z-type air cooling system, and the fast and high-precision prediction of heat dissipation performance is realized, which effectively avoids the repeated calculation of CFD. The design variables studied include geometric structure parameters and inlet wind speed (vinlet), and the target variables are maximum temperature (Tmax), temperature difference (ΔTmax) and pressure loss (ΔP). From the new perspective of genetic evolution process of NSGA-II, it is proved that the design of tapered inlet and outlet is better than that of flat and expansion outlet, the wide channel near the inlet and narrow channel near the outlet are more conducive to the improvement of heat dissipation performance. Further analysis shows that there is a linear relationship among vinlet, Tmax and ΔP in the Pareto solution set of Z-type air-cooling system, which simplifies the Pareto model effectively, and engineers can predict the optimal performance of BTMS by this relationship. In addition, through sensitivity analysis, it is found that in the Z-type BTMS, the operating parameter vinlet has the greatest influence on the heat dissipation performance and pressure loss, while the geometric parameters have less influence. Compared with the benchmark case, the optimized design scheme reduces Tmax, ΔTmax and ΔP by 1.97 K, 6.32 K and 2.82 Pa, respectively, which significantly improves the heat dissipation performance.

Multi-objective optimization of machining parameters in complete peripheral milling process with variable curvature workpieces

The mechanism of peripheral milling with variable curvature has yet to be revealed. There is an urgent need to develop a composite optimization approach for the full milling process, including rough and fine machining. The influence of curvature on the energy consumption mechanism in peripheral milling is investigated in this paper. Firstly, the geometric parameters of peripheral milling are analyzed, and the material removal rate calculation method with variable curvature is proposed. The effects of workpiece curvature, machining parameters, and tool wear on energy consumption and surface quality are revealed. Secondly, prediction models for machine tool specific energy and surface roughness are developed in accordance with the examined material removal mechanism. Then, the carbon emission calculation method for the complete peripheral milling process is put forward based on the developed energy consumption mechanism. Finally, a genetic algorithm with elite strategy is used to optimize the multi-dimensional and multi-objective machining parameters. The objective is to achieve the maximum machining efficiency, minimize carbon emissions, and attain excellent surface quality during the complete milling process. The research aims to concurrently optimize machining parameters for rough and fine machining processes, thereby enabling effective and environmentally friendly peripheral milling while maintaining machining quality. The determination coefficient R2 for the proposed model exceeds 0.97, while the average accuracy of predictions surpasses 96 %. The research holds particular importance in providing guidance for peripheral milling operations involving workpieces with variable curvature.

Multi-objective optimization and machine learning assisted design and synthesis of magnesium based novel non-equiatomic medium entropy alloy

Non equiatomic compositionally complex alloys have gained significant attention due to their unique combinations of properties including high strength, good ductility, excellent thermal stability and corrosion resistance. The non equiatomic compositions provide flexibility in tailoring the mechanical, thermal, magnetic, biomedical and other functional properties to meet the specific application requirements. In this study, a novel design approach was proposed to identify quaternary non equiatomic medium entropy alloy for biomedical application. The design methodology employs multi-objective optimization of empirical phase formation parameters and ML algorithm to design solid solution containing non equiatomic medium entropy alloy. The suggested approach scans over a large composition space to find possible non-equiatomic compositionally complex alloy compositions (CCA) with desired phases. The proposed methodology is an alternative to trial-and-error alloy fabrication and CCA design approach reported in the literature. This step significantly reduced the time and resources utilized in CCA design by decreasing the number of experimental runs. Based on the design strategy, Mg60Ti24.24Zn9.66Nb6.1 alloy was designed and selected for fabrication through powder metallurgy route by employing microwave sintering. The Mg rich alloy exhibited the presence of two solid solution phases viz HCP and BCC in both prealloyed powder and final sintered specimen. The developed alloy showed a high microhardness value of 153.9 ± 0.85HV with a measured density of 2.82 ± 0.03 g/cm3. The mechanical properties such as yield strength, ultimate compressive strength, elastic modulus and fracture strain of the developed alloy were found to be superior to that of pure Mg and AZ31 fabricated through the same manufacturing route. Mechanical properties of local phases present within the alloy were assessed through nanoindentation technique. Elastic modulus (E) and hardness (H) values for the Ti rich BCC phase were 118.9±7.2 GPa and 4.65±1.1 GPa, respectively, whereas the Mg rich HCP phase showed values of 65.2±7 GPa and 1.6±0.43 GPa, respectively. Furthermore, with minor changes to the constraint equations and correct ML dataset selection, the suggested design technique can be applied to any multi-principal element alloy system to design non-equiatomic compositions.

Research on multi-objective parameter optimization of split flow dual flash thermodynamic cycle

In addition to considering the intermediate pressure variables, the split flow double flash thermodynamic cycle also needs to consider the operating conditions and split flow coefficient of the cycle. Traditional algorithms require too much computation and have a longer response time. Faced with sudden operating conditions or changes in production conditions, it is difficult to quickly re-optimize the intermediate parameters of the split flow double flash thermal cycle and carry out corresponding system control. This article uses intelligent optimization algorithms instead of global search methods to optimize multi-objective parameters in a split flow double flash thermodynamic cycle. By summarizing and analyzing the characteristics of algorithms such as SIO, EA, and ANN, it is believed that the GWO algorithm in the SIO class is the most suitable for multi-objective parameter optimization of the split flow double flash thermal cycle. It is combined with the SA optimization algorithm to propose the SA-GWO algorithm with faster optimization speed and higher optimization accuracy, taking the initial position of the wolf pack as the optimization objective. Combined with the cycle thermodynamic model, a multi-objective parameter optimization model for the split flow double flash thermal cycle is established.

Optimizing the fuzzy PI controller for quadruple tank system using multi-objective Jaya algorithm

This article proposes the Jaya optimization algorithm for the multi-objective optimization of parameters for a Fuzzy Proportional-Integral (Fuzzy PI) controller, referred to as MOJaya. The purpose is to control the water level of a quadruple-tank system (QTS). The controlled plant is a nonlinear and uncertain Multiple-Input Multiple-Output (MIMO) system, which is affected by various factors such as parameter uncertainties, external disturbances, and significant time delays. These factors have a considerable impact on the stability of the control system. The parameters of the fuzzy controller and the normalization coefficients of the Fuzzy PI controller are optimized offline using the multi-objective Jaya optimization (MOJaya) algorithm. To demonstrate the effectiveness of the proposed algorithm, it was compared with the Fuzzy PI and PI-MOJaya algorithms. The simulation results indicate that the proposed algorithm outperforms the other algorithms in terms of control quality.

Multiobjective optimization of process parameters of AZ91D/AgNPs/TiO2 composite fabricated by friction stir processing using response surface methodology and desirability

PurposeThis paper aims to determine the optimum parametric settings for yielding superior mechanical properties, namely, ultimate tensile strength (UTS), yield strength (YS) and percentage elongation (EL) of AZ91D/AgNPs/TiO2 hybrid composite fabricated by friction stir processing.Design/methodology/approachAn empirical model has been developed to govern crucial influencing parameters, namely, rotation speed (RS), tool transverse speed (TS), number of passes (NPS) and reinforcement fraction (RF) or weight percentage. Box Behnken design (BBD) with four input parameters and three levels of each parameter was used to design the experimental work, and analysis of variance (ANOVA) was used to check the acceptability of the developed model. Desirability function analysis (DFA) for a multiresponse optimization approach is integrated with response surface methodology (RSM). The individual desirability index (IDI) was calculated for each response, and a composite desirability index (CDI) was obtained. The optimal parametric settings were determined based on maximum CDI values. A confirmation test is also performed to compare the actual and predicted values of responses.FindingsThe relationship between input parameters and output responses (UTS, YS, and EL) was investigated using the Box-Behnken design (BBD). Silver nanoparticles (AgNPs) and nano-sized titanium dioxide (TiO2) enhanced the ultimate tensile strength and yield strength. It was observed that the inclusion of AgNPs led to an increase in ductility, while the increase in the weight fraction of TiO2 resulted in a decrease in ductility.Practical implicationsAZ91D/AgNPs/TiO2 hybrid composite finds enormous applications in biomedical implants, aerospace, sports and aerospace industries, especially where lightweight materials with high strength are critical.Originality/valueIn terms of optimum value through desirability, the experimental trials yield the following results: maximum value of UTS (318.369 MPa), maximum value of YS (200.120 MPa) and EL (7.610) at 1,021 rpm of RS, 70 mm/min of TS, 4 NPS and level 3 of RF.

Performance enhancements of semi-active vehicle air ISD suspension

To explore an innovative approach for enhancing the vibration isolation performance of vehicle air suspension systems, this study introduces the inerter element into the vehicle air suspension and investigates the dynamic behavior of the semi-active vehicle air ISD (inerter-spring-damper) suspension. Initially, a dynamic model of the quarter semi-active vehicle air ISD suspension is established, followed by conducting multi-objective optimization of the core parameters using the genetic algorithm. Subsequently, the dynamic performance of the semi-active vehicle air ISD suspension is thoroughly examined through simulations and analyses in both the time and frequency domains. The results demonstrate notable improvements in various aspects: the root-mean-square (RMS) value of the vehicle body acceleration in the semi-active vehicle air ISD suspension is reduced by 12.8%, the RMS value of the suspension working space is decreased by 37.3%, and the RMS value of the dynamic tire load experiences an 8.9% reduction. The findings of this paper indicate that the proposed semi-active vehicle air ISD suspension outperforms both the passive vehicle air suspension and the semi-active vehicle air suspension without an inerter, significantly enhancing the vehicle’s ride comfort, handling stability, and driving safety.

Design and experiment study of a novel dual-channel independent-coil magnetorheological grease damper

In order to address the issue of reduced damping force dynamic range in magnetorheological (MR) damper caused by the high zero-field viscosity of MR grease, known for its sedimentation stability, this paper introduces a novel dual-channel independent-coil MR damper (DCICMRD). Firstly, the dual-channel configuration and the magnetic circuit structure of independent coils were meticulously designed, and a genetic algorithm was employed to conduct multi-objective optimization of the structural parameters for DCICMRD. The optimization results indicate that all performance metrics of the damper post-optimization exhibit improvements exceeding 15%. Then, the theocratical model of the damping force for DCICMRD under three operational modes are established, and the output damping force of various input currents for different operating mode is obtained. Finally, the DCICMRD was manufactured and subjected to dynamic performance testing. The results revealed that the damping force dynamic range in Mode III, i.e. dual-channel structure, can achieve approximately 15 times, whereas Mode I, i.e. traditional single-channel structure, only attains approximately 9 times. The aforementioned research holds significant implications for expanding the further engineering applications of MR dampers.

Multi-objective optimization of emission parameters of a diesel engine using oxygenated fuel and pilot injection strategy based on RSM-NSGA III

In this work, a diesel engine fueled with PODE3/n-butanol/diesel blends at high altitudes was optimized using the RSM-NSGA III hybrid optimization methodology to achieve a reduction in emissions. The target parameters of the optimization were soot, NOx, and HC emissions, and the decision variables were the mass ratio of pilot injection (MRPI), pilot injection timing (PIT), mass ratio of PODE3 (MRP) and mass ratio of n-butanol (MRB). Firstly, a three-dimensional simulation model of the CI engine was built in CONVERGE, and the model was verified through experiments. Secondly, the effects of the pilot injection strategy, also known as the pre-injection strategy, on the combustion characteristics of the CI engine were analyzed. Then, an experimental design based on Box-Behnken Design (BBD) was conducted and a mathematical model of the target parameters was obtained using RSM. Finally, the mathematical model of RSM was optimized using NSGA III to obtain the Pareto frontier, and the optimal solution was selected from the Pareto frontier using TOPSIS. The results showed that a suitable pilot injection strategy helped alleviate the problem of low cylinder pressure (CP) and high soot and HC emissions from the CI engine at high altitudes. The soot and HC emissions exhibited a decreasing and then increasing trend as the PIT advanced. As MRPI increased, both soot and HC emissions decreased. The NOx emissions showed the opposite trend. Compared with the non-optimized results, at an altitude of 2000 m, the optimized engine exhibited a 65.29% decrease in soot emissions, a 24.7% increase in NOx emissions, and a 74.94% decrease in HC emissions. Compared with the non-optimized results, at an altitude of 4000 m, the optimized engine demonstrated a 54.70% decrease in soot emissions, a 15.71% increase in NOx emissions, and a 65.78% decrease in HC emissions. This research will contribute to the application of oxygenated fuels in plateau diesel engines and the optimization of plateau diesel engines.

Multi-objective optimization of laser polishing parameters for enhanced mechanical properties, sustainability, and surface finish of 3D-Printed industrial ABS polymers using response surface methodology (RSM)

In this investigation, the asperities on the print surface have been reduced using a laser polishing technique. The improvement in mechanical properties (tensile and flexural strength), roughness of printed surface, and scan time of three-dimensional (3D)-printed new acrylonitrile-butadiene-styrene (ABS) polymers are among the industrial issues that the projected laser polishing process is explicitly intended to address. Response surface approach was used in a series of tests to determine how altering the laser's parameters influenced the test materials' top finish and the mechanical properties. The pre-polishing workpiece was compared in terms of mechanical characteristics and surface examination using a scanning electron microscope. According to the results, surface roughness was decreased by more than 18.85% for ABS utilizing laser polishing, from 8.01 to 6.50 μm. The sample's tensile strength improved by 8.1%, from 35.8 to 38.7 MPa. The samples' laser scan time was 0.143 min and the flexural strength increased by 2.58% from 58.01 to 59.51 MPa. These results demonstrate that a 3D-printed ABS polymer material's mechanical and surface qualities are enhanced by the novel post-processing laser polishing technique. In order to produce sustainable items, this innovative technique is quite helpful in the 3D printing sector.

Multi-objective optimization of laser welding process parameters of steel/Al based on BO-RF and MOJS

Multi-objective optimization of laser welding process parameters of steel/Al based on BO-RF and MOJS

Multi-objective optimization of intake parameters based on response surface methodology

Multi-objective optimization of intake parameters based on response surface methodology

Design, analysis and optimization of a novel redundant (6+1)-degree-of-freedom parallel mechanism with configurable platform

In this paper, a concept of adaptive capture for Autonomous Underwater Vehicles(AUVs) using parallel mechanism with configurable platform is proposed. By leveraging the deformation capabilities of the configurable platform, the mechanism can capture AUVs of various sizes and orientations while also expanding its workspace, thereby increasing the success rate of grasping. Firstly, the mechanism design is conducted, a novel parallel mechanism consisting of three identical chains with a three-fold symmetrical Bricard platform is proposed. Secondly, the kinematics are obtained, based on which the workspace analysis is performed, and numerical verification is conducted. Thirdly, the Jacobian matrix is derived and verified, and the singularity analysis is carried out. Finally, the concept of a high-quality workspace tailored for the AUVs recovery scenarios is proposed. To achieve a maximum high-quality workspace while maintaining a compact mechanism structure, two indicators are proposed to evaluate the volume and shape of the workspace, and a multi-objective parameter optimization of the mechanism is conducted. The optimized mechanism can be effortlessly scaled to cover the target workspace while preserving minimal dimensions and compact structure.

Multi-objective Parameter Optimization of Four-Stroke Diesel Engine with Waste Cooking Oil Biodiesel and Diesel Blend using RSM-NSGA-II

Multi-objective Parameter Optimization of Four-Stroke Diesel Engine with Waste Cooking Oil Biodiesel and Diesel Blend using RSM-NSGA-II

Multi-objective optimization of process parameters for laser metal deposition of NiTi shape memory alloy based on neural network and genetic algorithm

Multi-objective optimization of process parameters for laser metal deposition of NiTi shape memory alloy based on neural network and genetic algorithm

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.

Bayesian multi-objective optimization of process design parameters in constrained settings with noise: an engineering design application

Bayesian multi-objective optimization of process design parameters in constrained settings with noise: an engineering design application

NSGA-II Algorithm-Based Structural Parameters of Electric Pulse Rock-Breaking Electrode Bit Multi-Objective Optimization

Abstract High-voltage electric pulse (HVEP) electrode bit has a considerable influence on the drilling and rock-breaking (RB) efficiency. HVEP electrode bit was systematically studied to optimize the structural parameters in order to improve RB efficiency. This paper analyzed the impact of main structural parameters on electric field strength (EFS) and depth of penetration (DOP) during high-voltage electric pulse drilling. A structural optimization method integrating back propagation (BP) neural network and genetic algorithm for HVEP electrode bit was proposed. The method mapped the complex nonlinear relationships among electrode distance, electrode cone angle, electrode grounding span, etc., and EFS and DOP by establishing a BP neural network model, and adopted the non-dominated sorting genetic algorithm-II (NSGA-II) to optimize the main structural parameters. The simulation data showed that the combined BP neural network/non-dominated sorting genetic algorithm-II (BP-NSGA-II) was an effective tool for optimizing the injection molding process. The multi-objective optimization of the structural parameters of the HVEP electrode bit based on the NSGA-II algorithm was crucial to direct the choice of the process parameters of the HVEP electrode bit, boost the RB efficiency, and lower the energy loss during drilling.

Multi-objective optimization of concave radial forging process parameters based on response surface methodology and genetic algorithm

Multi-objective optimization of concave radial forging process parameters based on response surface methodology and genetic algorithm