Multi-objective optimization of process parametersduring low-pressure die casting of AZ91Dmagnesium alloy wheel castings

Multi-objective optimization has been increasingly applied in engineering where optimal decisions need to be made in the presence of trade-offs between two or more objectives. Minimizing the volume of shrinkage porosity, while reducing the secondary dendritic arm spacing of a wheel casting during low-pressure die casting(LPDC) process, was taken as an example of such problem. A commercial simulation software Pro CASTTM was applied to simulate the filling and solidification processes. Additionally, a program for integrating the optimization algorithm with numerical simulation was developed based on SiPESC. By setting pouring temperature and filling pressure as design variables, shrinkage porosity and secondary dendritic arm spacing as objective variables, the multi-objective optimization of minimum volume of shrinkage porosity and secondary dendritic arm spacing was achieved. The optimal combination of AZ91 D wheel casting was: pouring temperature 689 °C and filling pressure 6.5 kPa. The predicted values decreased from 4.1% to 2.1% for shrinkage porosity, and 88.5 μm to 81.2 μm for the secondary dendritic arm spacing. The optimal results proved the feasibility of the developed program in multi-objective optimization.

Parameters matching and optimization of parallel hybrid electric vehicle powertrain

Aiming at the development of parallel hybrid electric vehicle （PHEV） powertrain, parameter matching and optimization are presented, According to the performance of PHEV, the optimization range of engine, motor, driveline gear ratio and battery parameters are determined. And then a two-level optimization problem is formulated based on analytical target cascading （ATC）. At the system level, the optimization of the whole vehicle fuel economy is carried out, while the tractive performance is defined as the constraints. The optimized parameters are cascaded to the subsystem as the optimization targets. At the subsystem level, the final drive and transmission design are optimized to make the ratios as close to the targets as possible. The optimization result shows that the fuel economy had improved significantly, while the tractive performance maintains the former level.

Effects of strength matching and crack depth on the fracture parameters for welded joints

Fracture parameters of welded joints with different strength matching and crack depth in weld metal are investigated by using the methods of elastoplastic finite element analysis and three point bend specimen test. The results show that for shallow crack, the plastic zone turns large in loading process, and the fracture toughness turns high. The extent of the plastic zone of overmatched joint is larger than that of undermatched joint because it will extends to parent metal from the weld metal in loading process for the same CTOD value. The plastic zone of undermatched joint is restricted within the weld, and the size of that is small. Overmatched joint shows the fracture behaviour of shallow crack may more easily than the undermatched joint, while the two sorts of joint specimens have the same crack depth. Therefore, the fracture-resistant capability of overmatched weld is better than that of undermatched weld. when the toughness of weld metals is similar for both overmatched and undermatched joints.

A Penalty Function Algorithm with Objective Parameters and Constraint Penalty Parameter for Multi-Objective Programming

In this paper, we present an algorithm to solve the inequality constrained multi-objective programming (MP) by using a penalty function with objective parameters and constraint penalty parameter. First, the penalty function with objective parameters and constraint penalty parameter for MP and the corresponding unconstraint penalty optimization problem (UPOP) is defined. Under some conditions, a Pareto efficient solution (or a weakly-efficient solution) to UPOP is proved to be a Pareto efficient solution (or a weakly-efficient solution) to MP. The penalty function is proved to be exact under a stable condition. Then, we design an algorithm to solve MP and prove its convergence. Finally, numerical examples show that the algorithm may help decision makers to find a satisfactory solution to MP.

A Modified Interactive Stability Algorithm for Solving Multi-Objective NLP Problems with Fuzzy Parameters in Its Objective Functions

This paper presents a modified method to solve multi-objective nonlinear programming problems with fuzzy parameters in its objective functions and these fuzzy parameters are characterized by fuzzy numbers. The modified method is based on normalized trade-off weights. The obtained stability set corresponding to α-Pareto optimal solution, using our method, is investigated. Moreover, an algorithm for obtaining any subset of the parametric space which has the same corresponding α-Pareto optimal solution is presented. Finally, a numerical example to illustrate our method is also given.

Matching and Simulation of Power Parameters of Electric Transporter based on Cruise Software

In view of the characteristics of warehouse or freight yard carrying out transfer and loading conditions,the performance parameter matching of the power system of an electric transfer vehicle was studied,and the driving motor and power battery of the key components of the vehicle were reasonably selected.Cruise software was used to simulate the loading process of the vehicle.The results show that the performance design of the power system of the electric transport vehicle and its key components is reasonable,meeting the requirements of maximum speed,climbing performance and starting driving performance,and providing a reference and credible basis for the design of the power system of the vehicle.

Multi-Objective Optimization Analysis of Auxiliary Flux Modulator Magnetic Gear with Unequal Magnetic Poles

To enhance the output torque and minimize the torque ripple of coaxial magnetic gear(CMG),a novel auxiliary flux modulator CMG with unequal magnetic poles is proposed.This design incorporates an inner rotor with an asymmetric sector and a trapezoidal combined N-S pole structure,featuring Halbach arrays for the arrangement of permanent magnets(PMs).The outer rotor PMs adopt a Spoke-type configuration.To optimize the CMG for high output torque and low torque ripple,a sensitivity analysis is conducted to identify key size parameters that significantly influence the optimization objectives.Based on the sensitivity hierarchy of these parameters,a multi-objective optimization analysis is performed using a genetic algorithm(GA)to determine the optimal structural parameter values of the CMG.In addition,a coaxial magnetic gear(CMG)topology with 4 inner and 17 outer pole pairs is adopted,and the parametric model is established.Finally,the electromagnetic properties of the CMG are evaluated using the finite element method.The results indicate a remarkable reduction in torque ripple,specifically by 46.15%.

Multi-Objective Rule System Based Control Model with Tunable Parameters for Swarm Robotic Control in Confined Environment

Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tunable parameters is a widely adopted approach.In this article,an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking(MO O-Flocking)is proposed.The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters.To achieve multi-objective parameter tuning,a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2)is designed.Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives.Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.

Parameter matching of hydraulic balancing circuit based on AMESim

In order to improve the stability of the hydraulic balancing circuit,taking a certain type of vehicular radar flip device as the research object,the influence of different wind load,counterbalance valve parameters and hydraulic pump flow pulsation on the system flip process are simulated and analyzed.Through the force analysis of the flipping device,the total load curve of the flip cylinder is obtained by using MATLAB simulation.The simulation model of balancing circuit and plane flip device are built by AMESim software,and the reasonable parameter matching relationship is obtained by specific simulation analysis.The simulation results show that when the wind load is to the left and decreases,the system runs more stably.When wind load works in the right direction,the load on the piston rod of the hydraulic cylinder decreases but the jitter amplitude increases.At the same time,the stability of the balancing circuit can be effectively improved by properly reducing the damping diameter of the counterbalance valve,setting the pilot ratio to 4∶1 and using the hydraulic pump with small pulsation.The optimized parameter matching relationship can not only meet the requirements of typical system conditions and responses,but also improve the performance of the circuit.

An Improved Parameter Dimensionality Reduction Approach Based on a Fast Marching Method for Automatic History Matching

History matching is a critical step in reservoir numerical simulation algorithms.It is typically hindered by difficulties associated with the high-dimensionality of the problem and the gradient calculation approach.Here,a multi-step solving method is proposed by which,first,a Fast marching method(FMM)is used to calculate the pressure propagation time and determine the single-well sensitive area.Second,a mathematical model for history matching is implemented using a Bayesian framework.Third,an effective decomposition strategy is adopted for parameter dimensionality reduction.Finally,a localization matrix is constructed based on the single-well sensitive area data to modify the gradient of the objective function.This method has been verified through a water drive conceptual example and a real field case.The results have shown that the proposed method can generate more accurate gradient information and predictions compared to the traditional analytical gradient methods and other gradient-free algorithms.

The Optimal Matching Parameter of Half Discrete Hilbert Type Multiple Integral Inequalities with Non-Homogeneous Kernels and Applications

By using the weight function method,the matching parameters of the half discrete Hilbert type multiple integral inequality with a non-homogeneous kernel K(n,||x||ρ,m)=G(nλ1||x||ρmλ,2)are discussed,some equivalent conditions of the optimal matching parameter are established,and the expression of the optimal constant factor is obtained.Finally,their applications in operator theory are considered.

加权3D-Matching的改进算法

Matching问题构成了一类重要的NP难问题.此类问题在诸多领域中有着重要的应用,如调度、代码优化等领域.对于加权3D-matching问题,通过深入分析问题的结构特性,可以转化成加权3D-matching augmentation问题进行求解,即从一个最大加权的k-matching着手构造权值最大的(k+1)-matching.从问题的特殊结构特性出发,给出了加权3D-matching augmentation问题特有的性质:k-matching中存在2列使得该2列至少有2k/3元素被包含在(k+1)-matching中所对应的2列中.基于给出的性质,通过运用color-coding和动态规划技术,给出了一个时间复杂度为O*(4.823k)的参数算法,最终求解加权3D-matching问题.该算法较目前文献中的最好结果O*(5.473k)有了极大的改进.

带权Matching和Packing问题的一种固定参数可解算法

带权的m-DMATCHING和m-SETPACKING问题(m≥3)以前是用近似算法来求解的.本文首先根据参数计算理论对这两个带权问题进行了参数化定义,然后运用最新的着色技术和动态规划技术对带权的m-SETPACKING问题设计了一个时间复杂度为O*(12.8mk)的固定参数可解算法,接着在此基础上利用问题本身的结构特点对带权的m-DMATCHING问题提出了一个时间复杂度为O*(12.8(m-1)k)的固定参数可解算法,表明带权的m-SETPACKING问题和带权的m-DMATCHING问题都是固定参数可解的.

A strength reduction method based on double reduction parameters and its application

In the traditional strength reduction method,the cohesion and the friction angle adopt the same reduction parameter,resulting in equivalent proportional reduction.This method does not consider the different effects of the cohesion and friction angle on the stability of the same slope and is defective to some extent.Regarding this defect,a strength reduction method based on double reduction parameters,which adopts different reduction parameters,is proposed.The core of the double-parameter reduction method is the matching reduction principle of the slope with different angles.This principle is represented by the ratio of the reduction parameter of the cohesion to that of the friction angle,described as η.With the increase in the slopeangle,ηincreases; in particular,when the slope angle is 45°,tηis 1.0.Through the matching reduction principle,different safety margin factors can be calculated for the cohesion and friction angle.In combination with these two safety margin factors,a formula for calculating the overall safety factor of the slope is proposed,reflecting the different contributions of the cohesion and friction angle to the slope stability.Finally,it is shown that the strength reduction method based on double reduction parameters acquires a larger safety factor than the classic limit equilibrium method,but the calculation results are very close to those obtained by the limit equilibrium method.

Optimization of Cutting Parameters in Helical Milling of Carbon Fiber Reinforced Polymer

To investigate cutting performance in the helical milling of carbon fiber reinforced polymer(CFRP),experiments were conducted with unidirectional laminates.The results show that the influence of cutting parameters is very significant in the helical milling process. The axial force increases with the increase of cutting speed, which is below 95 m/min; otherwise, the axial force decreases with the increase of cutting speed. The resultant force always increases when cutting speed increases; with the increase of tangential and axial feed rates, cutting forces increase gradually. In addition, damage rings can appear in certain regions of the entry edges; therefore, the relationship between machining performance(cutting forces and holemaking quality) and cutting parameters is established using the nonlinear fitting methodology. Thus, three cutting parameters in the helical milling of CFRP, under the steady state, are optimized based on the multi-objective genetic algorithm, including material removal rate and machining performance. Finally, experiments were carried out to prove the validity of optimized cutting parameters.

Optimization of Cutting Parameters for Trade-off Among Carbon Emissions, Surface Roughness, and Processing Time

As the manufacturing industry is facing increasingly serious environmental problems, because of which carbon tax policies are being implemented, choosing the optimum cutting parameters during the machining process is crucial for automobile panel dies in order to achieve synergistic minimization of the environment impact, product quality, and processing efficiency. This paper presents a processing task-based evaluation method to optimize the cutting parameters, considering the trade-off among carbon emissions, surface roughness, and processing time. Three objective models and their relationships with the cutting parameters were obtained through input–output, response surface, and theoretical analyses, respectively. Examples of cylindrical turning were applied to achieve a central composite design(CCD), and relative validation experiments were applied to evaluate the proposed method. The experiments were conducted on the CAK50135 di lathe cutting of AISI 1045 steel, and NSGA-Ⅱ was used to obtain the Pareto fronts of the three objectives. Based on the TOPSIS method, the Pareto solution set was ranked to find the optimal solution to evaluate and select the optimal cutting parameters. An S/N ratio analysis and contour plots were applied to analyze the influence of each decision variable on the optimization objective. Finally, the changing rules of a single factor for each objective were analyzed. The results demonstrate that the proposed method is effective in finding the trade-off among the three objectives and obtaining reasonable application ranges of the cutting parameters from Pareto fronts.

Multi-objective Optimization of Geothermal Extraction from the Enhanced Geothermal System in Qiabuqia Geothermal Field, Gonghe Basin

A geothermal demonstration exploitation area will be established in the Enhanced Geothermal System of the Qiabuqia field, Gonghe Basin, Qinghai–Xizang Plateau in China. Selection of operational parameters for geothermal field extraction is thus of great significance to realize the best production performance. A novel integrated method of finite element and multi-objective optimization has been employed to obtain the optimal scheme for thermal extraction from the Gonghe Basin. A thermal-hydraulic-mechanical coupling model(THM) is established to analyze the thermal performance. From this it has been found that there exists a contraction among different heat extraction indexes. Parametric study indicates that injection mass rate(Q_(in)) is the most sensitive parameter to the heat extraction, followed by well spacing(WS) and injection temperature(T_(in)). The least sensitive parameter is production pressure(p_(out)). The optimal combination of operational parameters acquired is such that(T_(in), p_(out), Q_(in), WS) equals(72.72°C, 30.56 MPa, 18.32 kg/s, 327.82 m). Results indicate that the maximum electrical power is 1.41 MW for the optimal case over 20 years. The thermal break has been relieved and the pressure difference reduced by 8 MPa compared with the base case. The optimal case would extract 50% more energy than that of a previous case and the outcome will provide a remarkable reference for the construction of Gonghe project.

Multi-objective quality control method for cold-rolled products oriented to customized requirements

To deal with the increasing demand for low-volume customization of the mechanical properties of cold-rolled products, a two-way control method based on mechanical property prediction and process parameter optimization(PPO) has become an effective solution. Aiming at the multi-objective quality control problem of a company's cold-rolled products, based on industrial production data, we proposed a process parameter design and optimization method that combined multi-objective quality prediction and PPO. This method used the multi-output support vector regression(MSVR) method to simultaneously predict multiple quality indices. The MSVR prediction model was used as the effect verification model of the PPO results. It performed multi-process parameter collaborative design and realized the optimization of production process parameters for customized multi-objective quality requirements. The experimental results showed that, compared with the traditional single-objective quality prediction model based on support vector regression(SVR), the multi-objective prediction model could better take into account the coupling effect between process parameters and quality index, the MSVR model prediction accuracy was higher than that of the SVR, and the optimized process parameters were more capable and reflected the influence of metallurgical mechanism on the quality index,which were more in line with actual production process requirements.

3D Motion Parameters Determination Based on Binocular Sequence Images

Exactly capturing three dimensional (3D) motion i nf ormation of an object is an essential and important task in computer vision, and is also one of the most difficult problems. In this paper, a binocular vision s ystem and a method for determining 3D motion parameters of an object from binocu lar sequence images are introduced. The main steps include camera calibration, t he matching of motion and stereo images, 3D feature point correspondences and re solving the motion parameters. Finally, the experimental results of acquiring th e motion parameters of the objects with uniform velocity and acceleration in the straight line based on the real binocular sequence images by the mentioned meth od are presented.

Time variant multi-objective linear fractional interval-valued transportation problem

This paper studies a time-variant multi-objective linear fractional transportation problem. In reality, transported goods should reach in destinations within a specific time. Considering the importance of time, a time-variant multi-objective linear fractional transportation problem is formulated here. We take into account the parameters as cost, supply and demand are interval valued that involved in the proposed model, so we treat the model as a multi-objective linear fractional interval transportation problem. To solve the formulated model, we first convert it into a deterministic form using a new transformation technique and then apply fuzzy programming to solve it. The applicability of our proposed method is shown by considering two numerical examples. At last, conclusions and future research directions regarding our study is included.