基于强度Pareto进化的注塑机注射性能多目标优化

为实现大型注塑机注射性能的优化设计,构建了注射压力、注射速率和注射功率优化模型,应用多目标进化算法,系统分析了影响注射性能的各方面因素.改进强度Pareto进化算法,引入模糊C均值聚类,加快外部种群的聚类过程.采用约束Pareto支配和浮点数、二进制混合染色体编码策略,一次运行就能求得分布均匀的Pareto最优解集,并使用基于集合理论的方法选择一个最优解.试验分析表明:结合了强度Pareto进化算法与模糊C均值聚类方法的混合算法在提高注射综合性能的同时,能够获得比线性加权法分布性更好的Pareto前沿;且与强度Pareto进化算法相比,显著缩短了运算时间,具有较高的效率与鲁棒性.     

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions.     

结合性能评价的多目标经营过程资源配置优化

经营过程的质量，既取决于经营过程链的结构，又取决于资源和组织的配置。经营过程资源配置质量由基于相似度排序技术的平均的理想资源配置贴近度和基于信息熵的资源配置均衡性所组成。这种质量可理解成经营过程运作的事前预估质量。建立了时间、成本和质量的多目标经营过程资源配置优化模型，采用混合型非受支配排序遗传算法求解这类决策变量较多的多目标优化问题，大致给出了进化过程所产生的Pareto全集的聚类中心。以劳动力密集型的船舶并行建造为例，说明了经营资源配置质量的解析描述和混合型非受支配排序遗传算法，对于动态资源配置是有效的。     

基于BP-NSGA的注塑参数多目标智能优化设计

为获得成型性能最优的注塑参数设计方案,提出了基于BP神经网络和非支配排序遗传算法的注塑参数多目标优化方法。将注塑模结构尺寸参数和注塑工艺参数作为待优化的设计变量,建立了以高质量、低成本、高效率为优化目标的注塑参数优化设计模型。基于非支配排序遗传算法获取给定参数范围内的所有Pareto最优解,并通过建立多输入和多输出的BP神经网络来快速获得非支配排序遗传算法优化进程中所有个体的适应度值。开发了基于BP神经网络与非支配排序遗传算法集成的注塑参数智能优化设计系统,并通过鼠标注塑参数设计实例,验证了其适用性和有效性。     

智能注射成形中工艺参数的多目标自学习优化

注射成形工艺参数是保障产品质量的关键因素.传统试错法严重依赖工艺人员的试模经验,随着注射成形工艺广泛应用于电子、航空航天等国家战略领域,产品的高端化对工艺参数智能化设置水平提出更高的要求.由于成形产品存在多方面的质量要求,且不同质量指标间可能相互制约,因此亟需一种工艺参数多目标智能优化方法,以获得不同优化目标间的帕累托...     

Multiobjective optimization of injection molding parameters based on soft computing and variable complexity method

The objective of this study is to propose an intelligent methodology for efficiently optimizing the injection molding parameters when multiple constraints and multiple objectives are involved. Multiple objective functions reflecting the product quality, manufacturing cost and molding efficiency were constructed for the optimization model of injection molding parameters while multiple constraint functions reflecting the requirements of clients and the restrictions in the capacity of injection molding machines were established as well. A novel methodology integrating variable complexity methods (VCMs), constrained non-dominated sorted genetic algorithm (CNSGA), back propagation neural networks (BPNNs) and Moldflow analyses was put forward to locate the Pareto optimal solutions to the constrained multiobjective optimization problem. The VCMs enabled both the knowledge-based simplification of the optimization model and the variable-precision flow analyses of different injection molding parameter schemes. The Moldflow analyses were applied to collect the precise sample data for developing BPNNs and to fine-tune the Pareto-optimal solutions after the CNSGA-based optimization while the approximate BPNNs were utilized to efficiently compute the fitness of every individual during the evolution of CNSGA. The case study of optimizing the mold and process parameters for manufacturing mice with a compound-cavity mold demonstrated the feasibility and intelligence of proposed methodology.     

节能注塑机合模机构多目标优化设计研究

对节能注塑机合模机构进行了运动和动力学分析,以机构行程比较大、力放大比较大和机构尺寸较小为目标函数建立了多目标优化数学模型,使用Matlab优化工具箱对其进行了优化设计,通过实例给出了优化结果并进行比对,验证了优化模型的正确性,为注塑机合模机构的优化设计提供了一定的参考。     

基于改进的非支配排序遗传算法的模块化产品多目标配置优化研究

在产品族模块化设计的基础上,应用模糊数学评价理论与最小二乘法,构建了以产品性能、成本及出货期为目标函数的配置优化数学模型,并采用基于改进的非支配排序遗传算法对三者进行并行优化.由此获得一系列基于Pareto最优集的配置方案来满足不同客户对产品性能、成本及出货期的要求,解决了客户需求侧重点对产品设计结果的适应性处理.最后,结合项目实施,给出该方法在机床制造业中的典型应用实例,验证了文中提出方法的有效性和适应性.     

数控机床主轴的多目标优化设计

对数控机床主轴的机构参数优化问题进行了研究。提出了一种以主轴重量、主轴伸出端的扰度为目标的结构参数多目标优化方法。研究了利用多学科优化设计软件iSIGHT对数控机床主轴进行多目标优化设计的方法步骤,并对一个简化的机床主轴模型进行了多目标优化设计,得到Pareto最优解集。研究表明,基于Pareto最优解的方法更适合于数控机床主轴的优化设计;利用iSIGHT进行优化设计,可以简化求解过程,具有一定的工程实用价值。     

基于有效工作寿命的多变量动态均衡设计及其度量方法研究

针对机械产品各关键部件设计寿命差异较大,设计寿命较短的关键零部件容易发生故障,从而导致停机维修甚至停产报废的问题,定义机械产品关键零部件有效工作寿命,给出机械产品关键零部件有效工作寿命的度量方法,建立基于有效工作寿命的机械产品多变量动态均衡数学模型,提出机械产品多变量动态均衡设计方法。通过求解获得关键零部件的预防性维护措施优化方案,在故障发生之前对关键零部件实施预防性维护措施,消除或减少关键零部件故障的发生,均衡各关键部件的有效工作寿命,同时满足机械产品维护费用成本指标与整机可靠性指标约束。应用该方法对某型号空分设备透平膨胀机主机进行多变量动态均衡设计,证明该方法在工程应用中的正确性与高效性。     

孔群加工路径规划问题的进化求解

孔群加工路径规划对于提高多孔类零件的加工效率和质量具有重要意义。建立了两个孔群加工路径规划问题的数学模型,分别归纳为单目标和多目标组合优化问题,并引入进化蚁群系统算法和人工免疫算法求解单目标组合优化问题。这两种算法均能有效防止解空间的“组合爆炸”问题,计算复杂度的阶次低于Hopfield神经网络算法,且性能优于Hopfield算法。采用多目标解的快速排序技术分别对进化蚁群系统算法和人工免疫算法加以改进,开发出多目标进化蚁群系统算法和多目标人工免疫算法。分析表明,改进算法不增加原算法的计算复杂度,能直接用于求解多目标组合优化问题而无需事先给出目标权值向量,并能一次运行求得问题的多个Pareto最优解。     

基于NSGA-II的多目标设备动态布局方法

对多个计划期内需求可预测的车间动态设备布局问题进行了研究.针对这一多目标、多约束的问题,以物流搬运和重布局费用之和、非物流关系以及面积利用率作为优化目标,将动态布局问题转化为重布局过程和多个子计划期的静态布局问题,构建了针对不等面积设备的动态多期布局问题的连续型多目标优化模型.采用带精英策略的非支配遗传算法(Non-dominated Sorting Genetic Algorithm II,NSGA-II)进行求解,克服了传统加权法求解多目标问题时加权系数难以确定和无法保证多目标同时优化的缺点,求解得到Pareto解集,供决策者根据企业实际情况优中选优.通过实例验证了本方法的有效性.     

Multi-objective simulation optimization for selection and determination of order quantity in supplier selection problem under uncertainty and quality criteria

Simulation optimization is providing solutions to practical stochastic problems. Supplier selection is one of the most important decisions that determine the survival of an organization. In this paper, a novel multi-objective simulation optimization method to make decisions on selecting the suppliers and determining the order quantities is proposed. Regarding the fact that a real supply chain is multi-objective with uncertain parameters and includes both quantitative and qualitative variables, the proposed method considers these points and is applicable to real-world problems. This method also considers supplier selection and order quantity allocation to each supplier, which are totally related, as an integrated model. The proposed method consists of four basic modules: Cuckoo Optimization Algorithm (COA), Discrete Event Simulation (DES), Supply Chain Model (SCM), and Generalized Data Envelopment Analysis (GDEA). Unlike many multi-objective methods, the proposed method is not limited to the number of objective functions and this is one of its main benefits. It also pays attention to the efficiency of the organization and, at the same time, finding inputs which result in best output amounts. This method, in addition to the convergence criterion, pays special attention to the dispersion of the Pareto frontier as the second criterion for choosing the good solutions. For implementation of the proposed method, the numerical results for the problem of supplier selection in multi-product, multi-customer modes, and uncertain and qualitative variables are discussed and the Pareto frontiers are presented. The proposed method in this paper is compared with a similar method, and the results show the efficiency of the proposed method.     

Study on temperature field for invar alloy during TIG welding

In order to solve the complex multi-objective optimal performance design of large-scale injection molding machines, NSGA-II is used to find a much better spread of design solutions and better convergence near the true Pareto-optimal front. The combination of the design method and the injection molding machine is discussed. Screw diameter performance, stick inside distance performance, mold moving route performance and mold-locked force performance are chosen as the four main performance evaluation indexes. Some related parameters are associated to get a performance indication. And performance optimization design parameter constraints are listed to make the design solutions to have practical significance. The mathematical models of two objectives and the mathematical models of three objectives are analyzed. Finally, the instance of HTF180X1N large-scale injection molding machine is taken as an example to demonstrated that such method is effective and practical.     

基于MATLAB的节能注塑机合模机构多目标寻优研究

基于节能注塑机合模机构的结构特点及工作特性,通过对机构进行运动和动力分析,并建立优化设计模型。以多目标的优化设计理论为基础,综合考虑了机构的力放大比、行程比、总体尺寸等因素及其相互关系,建立了合理的统一目标函数和约束条件,在此基础上通过Matlab优化工具箱进行了优化编程,得出最优解。最后结合具体实例得出了更为合理的合模机构设计参数,从而验证了优化模型及优化方法的正确性,为注塑机合模机构的优化设计提供了理论依据。     

基于多属性决策的气动隐身多目标优化

针对多目标优化结果排序与选择的多属性决策(Multi-attribute decision making,MADM)问题,将多目标优化与MADM相结合,提出基于MADM的多目标优化方法,并将该方法应用于跨声速前掠翼(Forward-swept wing,FSW)气动隐身多目标优化中,优化结果提高了跨声速FSW的气动和隐身性能。采用类别形状函数变换法(Class-shape function transformation,CST)方法对翼型几何外形进行描述,实现FSW气动和隐身多学科优化设计模型的参数化描述。建立基于N-S方程的计算流体力学方法的FSW气动分析模型和基于矩量法的计算电磁学方法的FSW隐身分析模型。将Pareto多目标遗传算法得到的Pareto非劣解集构成MADM矩阵,采用基于模糊熵权的改进的逼近理想解的排序法(Modified technique for order preference by similarity to ideal solution,M-TOPSIS)方案评价方法进行Pareto非劣解排序,最终确定最佳的Pareto非劣解。研究结果验证了所提出方法的有效性,为多目标优化问题提供了一种新的解决途径。     

Optimal design of medium channels for water-assisted rapid thermal cycle mold using multi-objective evolutionary algorithm and multi-attribute decision-making method

Rapid heat cycle molding technology developed recently is a novel polymer injection molding process. In this study, a new water-assisted rapid heat cycle molding (WRHCM) mold used for producing a large-size air-conditioning plastic panel was investigated. Aiming at improving heating efficiency and temperature distribution uniformity of the mold cavity surface, a two-stage optimization approach was proposed to determine the optimal design parameters of medium channels for the WRHCM mold. First of all, the non-dominated sorting genetic algorithm-II (NSGA-II) combined with surrogate models was employed to search the Pareto-optimal solutions. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution was adopted as a multi-attribute decision-making method to determine the best compromise solution from the Pareto set. Then, the layout of the medium channels for this air-conditioning panel WRHCM mold was optimized based on the developed optimization method. It was indicated that the heating efficiency and temperature distribution uniformity on the mold cavity surface were greatly improved by using the optimal design results. Furthermore, the effectiveness of the optimization method proposed in this study was validated by an industrial application.     

基于数字孪生的铣削参数动态多目标优化策略

为解决机床性能动态变化过程中的铣削参数动态多目标优化问题,提出一种基于数字孪生的铣削参数动态多目标优化策略.首先采用梯度提升回归树算法构建加工参数与加工结果间的非线性映射关系;然后基于动态非支配排序遗传算法进行铣削参数动态寻优;最后在Pareto最优解的基础上,结合层次分析法和理想解相似度顺序偏好法建立决策分析模型并进...     

Multi-objective optimization of cycloidal gear based on segmental modification of pressure angle

In order to get the optimal profile of cycloid gear after sectional modification, a multi-objective optimization design method is proposed that considers both the modification parameters and macro-parameters of the cycloid gear. An algorithm of meshing force, meshing efficiency and anti-gluing ability between the cycloid gear and the pin gear was derived, and the related independent parameters were extracted as optimization variables. Taking high efficiency, high strength and light weight as the objective, the mathematical models of double-objective and three-objective optimization were established, and the influence of key design variable on the objective function was analyzed, and NSGA-II multi-objective genetic algorithm was used to solve the Pareto optimal solution of the optimization mathematical model. Results show that the optimized parameters can significantly improve the meshing efficiency, reduce the volume and meet the design requirements of high strength, high efficiency and lightweight on the premise of ensuring the strength of cycloid gear surface.

     

Multi-objective optimization of MIMO plastic injection molding process conditions based on particle swarm optimization

Determining optimal process parameter settings critically influences productivity, quality, and cost of production in the plastic injection molding industry. Selecting the proper process conditions for the injection molding process is treated as a multi-objective optimization problem, where different objectives, such as minimizing product weight, volumetric shrinkage, or flash present trade-off behaviors. As such, various optima may exist in the objective space. This paper presents the development of an experiment-based optimization system for the process parameter optimization of multiple-input multiple-output plastic injection molding process. The development integrates Taguchi’s parameter design method, neural networks based on PSO (PSONN model), multi-objective particle swarm optimization algorithm, engineering optimization concepts, and automatically search for the Pareto-optimal solutions for different objectives. According to the illustrative applications, the research results indicate that the proposed approach can effectively help engineers identify optimal process conditions and achieve competitive advantages of product quality and costs.