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。     

多目标一维下料决策方法研究

研究目标为最小化原料浪费、最小化下料方式数和最小化可用余料返回的多目标优化下料问题。运用多目标优化和多属性决策相结合的方法设计下料决策方法,即先用改进的非支配排序启发式进化算法求出问题的Pareto最优解集,再采用综合主客观赋权法计算各优化目标的权重,最后运用多属性决策方法选出一个满意解作为下料方案。实验结果证实所提方法对多目标下料决策是有效的。     

机载相控阵雷达探测参数优化

相控阵雷达(phased array radar,PAR)可通过合理配置工作参数优化其性能.针对机载相控阵雷达目标探测过程中的射频隐身需求,提出了一种雷达工作参数优化方法.首先建立了以评估发现目标能力的检测概率和评估射频隐身能力的截获概率为指标,以脉冲峰值功率和驻留时间为优化参数的雷达探测多目标优化模型;然后根据多目标优化理论,采用改进非支配遗传算法(improved non-dominated sorting genetic algorithm Ⅱ,NSGA-Ⅱ)求解Pareto最优解集,并由这些Pareto解构成决策矩阵;最后,利用熵权法对各个目标进行客观赋权,再运用逼近理想解的排序法(techninque for order preference by similarity to an ideal solution,TOPSIS)进行多属性决策(multi-attribute decision making,MADM),得到参数控制方案.结合具体实例进行仿真计算,结果表明,运用多目标优化理论可以有效地配置雷达工作参数,优化雷达工作性能.     

高速干切滚齿工艺参数的多目标优化与决策方法

为降低高速干切滚齿加工能耗、提高滚齿加工质量,提出一种基于改进多目标灰狼优化（MOGWO）算法和逼近理想解排序法（TOPSIS）的高速干切滚齿工艺参数多目标优化与决策方法。分析了滚齿工艺参数,将切削参数和滚刀参数作为优化变量,构建了面向最小加工能耗和最优加工质量的多目标优化模型,采用改进MOGWO算法对所建的模型进行迭代寻优,利用TOPSIS对优化的工艺参数解进行多属性决策。实验结果验证了所提方法的有效性。      

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行可视化分析排序。该策略能够针对机床整个运行时段提供符合当前机床特性的最优铣削参数取值方案,从而保证加工质量和加工效率。     

数控加工工艺参数的多目标优化与决策算法研究

为了提高汽油机活塞数控车削加工的质量和生产效率,提出了基于非支配排序差分进化算法的多目标优化方法与基于加权相对距离的决策方法。以加工件的表面粗糙度和材料去除速率为优化参数,建立了多目标优化模型。鉴于表面粗糙度的经验公式计算精度有限,且不具有生产设备和生产过程差异适应性,给出了学习因子自适应神经网络的拟合方法。使用非支配排序差分进化算法对多目标优化模型进行求解,得到了Pareto前沿解集。提出了基于加权相对距离的决策方法,得到了最优生产方案。经验证,与工厂现用生产方案比,优化后的工件表面粗糙度减小了一倍以上,材料去除速率提高了57.98%,以上数据充分证明了优化方案的有效性。     

基于NSGA-Ⅱ的复摆式破碎机多目标优化设计

为提高复摆式颚式破碎机工作能力,以破碎生产率和动颚行程特性值为优化目标,对颚式破碎机进行多目标优化设计。以PE250×400型颚式破碎机为优化对象,以各构件尺寸为优化设计变量,建立机构参数、腔形参数及工作参数等约束条件,构建多目标优化设计模型,利用带精英策略的非支配排序遗传算法(NSGA-II)处理多目标优化模型,得到帕累托(Pareto)最优解集。通过对比两目标的最优解分布,确定出待优化目标间的相互影响规律,并从最优解集中选择出合理参数作为最终设计结果。分析结果表明:文中提出的优化设计方法在获得更大生产率的同时,有效减小了动颚磨损,实现了颚式破碎机的多目标优化设计。     

基于多目标优化的生产调度与设备维护集成研究

为了解决生产实际中工件调度与维修计划的相互影响问题,提出基于多目标遗传算法的联合优化方案,以单机系统为研究对象,设备失效函数服从威布尔分布,考虑机器和工件的堕化效应,综合决策工件加工顺序和预防性维护时间。以工件流程时间最短化和维修成本最小化为联合优化目标,基于非支配排序遗传算法框架,提出一种新的选择机制以及去除重复个体的方法以提高种群多样性,设计改进的多目标遗传算法以求解Pareto最优解。通过不同设置下的数据实验验证了基于多目标优化的联合决策比独立决策表现更优异。实现了生产与维修部双目标之间的权衡,使决策者可根据偏好选择不同的满意解,有效协调车间的生产调度与设备维护计划。     

自适应遗传算法在混流装配线排序中的应用

混流装配线排序对实现准时化生产、提高生产效率至关重要。以保持均匀的零/部件消耗速率为目标,运用自适应遗传算法(Adaptive Genetic Algorithm,AGA)对混流装配线排序进行优化分析。构建了以零/部件均匀消耗速率为目标的优化模型,应用MATLAB软件对自适应遗传算法和标准遗传算法(Standard Genetic Algorithm,SGA)进行了编程,并对一个混流装配线实例进行排序分析。结果表明,AGA求出的最优解目标函数值26 723优于SGA求出的最优解目标函数值26 759,说明自适应遗传算法对解决此类问题有更好的搜索能力和更快的收敛速度。     

基于多目标优化的柴油机EGR率决策方法研究

EGR率的选择是一个多目标优化的问题,以往对于EGR率的选择通常是基于经验和大量试验的基础上,针对不同的目的进行优化。然而这种方法需要丰富的经验支持,且依赖主观判断,缺乏客观性。多目标优化方法的应用可以解决这样的问题,本文采用多目标灰色局势决策方法和Pareto前沿分析方法分别对EGR率进行了优化,并对结果进行了比较。结果表明,受到EGR率对发动机NOx排放抑制效果的影响,多目标灰色局势决策的结果会更偏向对此性能的优化,而Pareto前沿分析可以根据非劣解灵敏比的偏向度,可以获得偏离各优化目标最小的解,分析结果也能较为符合决策目的。     

Parameter optimization of machining processes using teaching–learning-based optimization algorithm

The optimum selection of process parameters plays a significant role to ensure quality of product, to reduce the machining cost and to increase the productivity of any machining process. This paper presents the optimization aspects of process parameters of three machining processes including an advanced machining process known as abrasive water jet machining process and two important conventional machining processes namely grinding and milling. A recently developed advanced optimization algorithm, teaching–learning-based optimization (TLBO), is presented to find the optimal combination of process parameters of the considered machining processes. The results obtained by using TLBO algorithm are compared with those obtained by using other advanced optimization techniques such as genetic algorithm, simulated annealing, particle swarm optimization, harmony search, and artificial bee colony algorithm. The results show better performance of the TLBO algorithm.     

Development of an management information system as knowledge base model for machining process characterisation

The current paper intends to use the typical problems of parameter selection and optimization in machining processes to bring out the design ideas that form the foundation of n-tier management information systems (MIS). These ideas can be used for creating data-driven, scalable enterprise information systems. The current phase (phase I) of the implementation highlights the capabilities of these design ideas in improving three key functional parameters of MIS, i.e. multi-dimensionality, isolation and scalability. The problem area of machining process characteristics of non traditional machining has been chosen with two specific goals in mind: A typical machining process has multiple parameters and various corresponding relationships can be found among such parameters. This will be used to highlight the degree of multi-dimensionality that can be achieved from a simple design pattern. The phase II of this paper can actually shift focus from the design of a generic management information system to a specific problem area of machining. This incorporates the features of an expert system in providing the customer or end user with maximum encapsulation of technical data and much improved decision-making capabilities within the system. Some complex machining processes such as abrasive water jet machining (AWJM), wire electrical discharge machining (WEDM) and electro discharge machining (EDM) are characterized by a large number of parameters which are not available in a structured fashion, thus, all the data associated with AWJM, WEDM and EDM systems has been conglomerated and presented in a normalized fashion. The essential features of the current research include the extraction of the relevant data from a large pool of data, with a distinct architecture of MIS, having three scalable layers. The normalization of the available data has been dealt with management information system and presented in the form of a software named “machining expert”. The code for the software provided is easy to understand, being written in Visual Basic and Access server for implementation. The developed “machining expert” for handling non traditional machining (NTM) data can also be extended with suitable design extensions to handle business logic by interacting with the end user. The current paper is applicable to any research on the structure and design of MIS in manufacturing technology.     

Machining process parameters optimization for heavy-duty CNC machine tools in sustainable manufacturing

Machining process parameters (MPP) directly affect the machining quality and efficiency of heavy-duty CNC machine tools (HCMT). The selection of MPP is very important to effectively improve machining performance. Machining performance has been closely related to the HCMT running state. In order to maintain HCMT sustainably manufacturing with high accuracy and low consumption after machining performance degradation for a long time running, MPP should be re-optimized according to the current state of the machine tools. Thus, this paper proposed a MPP optimization method for running HCMT to obtain optimal MPP based on current running state. A multi-objective optimization model was built, considering both the linear factors such as machining time and machining cost and nonlinear factors such as chatter in machining process. The nonlinear factors were reflected by the nonlinear dynamic model of machining process. Furthermore, a grid optimization algorithm was introduced to search the optimal MPP from the multi-objective optimization model. Finally, a case study was implemented to verify the feasibility of the nonlinear dynamic model and the superiority of the multi-objective optimization method compared with single-objective optimization method.     

Optimization and sensitivity analysis of geometrical and process parameters to reduce vibration during end milling process

Selection of geometrical and machining parameters has great influence in machining performance. Predictive modeling can be used in optimization and control of process parameters. This study focuses on the optimization and sensitivity analysis of machining parameters, and fine-tuning requirements to obtain better machining performance. A statistical prediction model was developed in terms of tool geometrical parameters such as rake angle, nose radius, and machining parameters such as cutting speed, feed rate, and depth of cut. Central composite response surface methodology with five parameters and five levels was used to create a mathematical model, and the adequacy of the model was checked using analysis of variance. The experiments were conducted on aluminum Al 6351 with high-speed steel end mill cutter. Vibration in terms of acceleration amplitude during end milling was measured with two accelerometers—one in tool holder (channel I) and other in workpiece fixture (channel II) respectively. Optimizations of process parameters were performed using genetic algorithm. Sensitivity analysis was performed using developed equations to identify the parameter exerting most influence on vibration amplitude.     

Constrained grinding optimization for time,cost, and surface roughness using NSGA-II

Selection of parameters in machining process significantly affects quality, productivity, and cost of a component. This paper presents an optimization procedure to determine the optimal values of wheel speed, workpiece speed, and depth of cut in a grinding process considering certain grinding conditions. Experimental studies have been carried out to obtain optimum conditions. Mathematical models have also been developed for estimating the surface roughness based on experimental investigations. A non-dominated sorting genetic algorithm (NSGA II) is then used to solve this multi-objective optimization problem. The objectives under investigation in this study are surface finish, total grinding time, and production cost subjected to the constraints of production rate and wheel wear parameters. The Pareto-optimal fronts provide a wide range of trade-off operating conditions which an appropriate operating point can be selected by a decision maker. The results show the proposed algorithm demonstrates applicability of machining optimization considering conflicting objectives.     

Off-line optimization on NC machining based on virtual machining

Virtual machining, based-on the model of a machining system, aims to simulate, evaluate and optimize the actual machining process with high sense of reality. It provides digital off-line optimization tools for NC machining. Taking advantage of virtual machining used in machining process simulation, one can build the framework of optimization system on NC machining so that the processes of reliability verification, cutting parameter optimization and error compensation can be integrated into one system to improve machining processes comprehensively. The optimization is realized via modifying NC programs. Several key issues such as virtual machining, cutting parameters optimization, error prediction and compensation are also highlighted. Optimization systems based on virtual machining have been developed to demonstrate the effectiveness of off-line optimization for different purposes. The results show that the machining process is obviously improved.     

Optimization of micro milling of hardened steel with different grain sizes using multi-objective evolutionary algorithm

Modern manufacturing processes need high production rates, low costs, and high product quality. Generally, surface roughness is a good reference to determine the performance in machined products. The use of optimization systems can determine the optimum machining parameters in the machining process, especially in milling operations. The present study integrates the least square model based on feed rate, cutting speed, and grain size with a genetic optimization algorithm to provide the optimal process parameter. The NSGA II algorithm was applied due to its coverage and easily to optimize the micro milling of hardened steel. The responses were Fy Force and Mz Torque. The results show that the feed rate was the most significant factor for minimizing Fy force and Mz Torque.     

Selection of the optimal electrochemical machining process parameters using biogeography-based optimization algorithm

Electrochemical machining (ECM) has become one of the most potential and useful non-traditional machining processes because of its capability of machining complex and intricate shapes in high-strength and heat-resistant materials. For effective utilization of the ECM process, it is often required to set its different machining parameters at their optimal levels. Various mathematical techniques have already been proposed by past researchers to determine the optimal combinations of the different machining parameters of the ECM process. In this paper, the machining parameters of an ECM process and a wire electrochemical turning process are optimized using the biogeography-based optimization (BBO) algorithm. Both the single- and multi-response optimization models are considered. The optimization performance of the BBO algorithm is also compared with that of other population-based algorithms, e.g., genetic algorithm and artificial bee colony algorithm. It is observed that the BBO algorithm outperforms the others with respect to the optimal values of different process responses and computation time.     

基于STEP-NC加工工步的优化

研究了基于精英选择遗传算法的加工工步序列优化技术。传统的加工工艺路线是以工序为制造单元,而在基于STEP-NC的加工中,是以加工工步为制造单元,并且面向数控加工中心的一种加工过程,由加工工步变换所带来的零件转位、刀具更换等所消耗的辅助加工时间为最短作为优化目标,来进行加工工步序列的优化,并对加工工步序列的优化算法进行了验证。     

基于遗传算法的计算机辅助工艺计划工序决策优化

加工中心在加工复杂零件时,工序复杂,工步多,其顺序直接影响加工质量和工作效率.针对数控加工中心零件加工工步的排序问题,提出了特征排序.以辅助加工时间最短为优化目标,采用数学中的最优化原理,利用惩罚函数建立相关数学模型,并基于遗传算法来解决工艺加工过程的排序问题.将为具体的工程实践起到指导作用.