Product configuration optimization using a multiobjective genetic algorithm

Product configuration is one of the key technologies in the environment of mass customization. Traditional product configuration technology focuses on constraints-based or knowledge-based application, which makes it very difficult to optimize design of product configuration. In this paper, an approach based on multiobjective genetic algorithm is proposed to solve the problem. Firstly, a configuration-oriented product model is discussed. A multiobjective optimization problem of product configuration according to the model is described and its mathematical formulation is designed. Secondly, a multiobjective genetic algorithm is designed for finding near Pareto or Pareto optimal set for the problem. A matrix method used to check constraint is proposed, and the coding and decoding representation of the solution are designed, then a new genetic evaluation and select mechanism is proposed. Finally, performance comparison of the proposed genetic algorithm with three other genetic algorithms is made. The result shows that the proposed genetic algorithm outperforms the other genetic algorithms in this problem.     

基于遗传算法的微小型谐振机器人结构拓扑优化方法研究

采用基于遗传算法的结构拓扑优化方法,设计了一种弯曲和扭转固有频率值相等的新型微小型机器人谐振足.采用可行解变换法和分层罚函数的方法解决了遗传算法中遗传、交叉、变异后产生不连通结构、棋盘格式的问题,并在有限元分析之前进行了结构连通性分析,提高了计算效率.     

Multi-objective optimization in single-row layout design using a genetic algorithm

This paper presents the development of a genetic algorithm for determining a common linear machine sequence for multi-products with different operation sequences and facilities with a limited number of duplicate machine types available for a job. This work aims to minimize the total flow distance traveled by products, reduce the number of machines arranged in the final linear sequence, and decrease the total investment cost of the machines used in the final sequence. We assume that product flow runs only in the forward direction, either via in-sequence or bypass movement. We demonstrate the effectiveness of the proposed algorithm by solving a typical layout design problem taken from literature, and several randomly generated problems. Results indicate that the proposed algorithm serves as a practical decision support tool for resolving layout problems in manufacturing facilities.     

基于遗传算法的轿车轮毂轴承数值优化

为了提高设计效率和质量,优化系统性能指标,在给定的装配空间条件下满足几何和性能约束,以获得最大的疲劳寿命为目的,运用遗传算法对双列角接触球轴承结构的轿车轮毂轴承进行数值优化设计。并基于一个实际例子编制了优化程序,给出了运算结果。结果表明,遗传算法能够有效地解决离散变量全局寻优问题,全部约束得到满足并且优化设计方案在系统疲劳寿命方面显著优于当前设计方案。     

遗传规划算法实现控制器的设计和优化

根据大型火电机组协调控制系统对象的特性,提出了采用遗传规划算法来实现控制器参数的寻优.该方法弥补了多变量控制器传统设计方法的不足,即在将多变量控制问题转化为多个单变量控制问题过程中对系统内部所存在的偶合性的削弱.仿真结果表明借助于该方法获得的控制器参数,能够使被控对象具有优秀的控制品质,并且通过优化PID控制器参数,也验证了遗传规划算法的全局搜索性.     

基于遗传算法的工步优化排序方法

针对数控加工中心上零件加工工步的排序问题,以辅助加工时间最短为优化目标,使用遗传算法对零件在一次装夹情况下的加工工步进行优化排序。提出了使用特征关系图和特征高度描述待加工特征之间加工的优先顺序、采用工步优先关系矩阵校验工步序列合理性的方法。论述了初始群体的生成、遗传算子以及工步优化排序的过程和算法。实际应用表明,该方法可有效提高工艺规划系统中工步的优化排序能力。     

遗传算法在机械优化设计中的应用

遗传算法是一种自适应启发式全局搜索算法,具有传统算法不可比拟的优点,更易于求解各种优化问题。在不断改进、不断成熟的过程中,遗传算法得到了广泛的应用,其中在机械优化设计领域得到了大量应用。通过遗传算法工具箱及针对基本遗传算法进行改进后的遗传算法两个方面,论述了遗传算法在机械优化设计中具有的优势,介绍了近年来在相关领域中成功应用的一些典型范例。并对遗传算法在机械优化设计中的发展及前景作了必要的讨论。     

类人机器人腕用六维加速度传感器的理论研究

类人机器人灵巧手抓取过程中的惯性力会影响到抓取的鲁棒性,为了消除惯性力的影响,需要检测出加在手上的空间加速度反馈给控制系统,以确保抓取的平稳性和快速性.采用并联机构作为弹性元件研究了一种六维加速度传感器,建立了静态数学模型,推导了加速度和灵敏度雅可比矩阵,并对传感器的静态特性进行了研究.根据定义的静态性能指标采用空间模型理论的图谱寻优法,优化设计了传感器的理论模型参数.为了验证设计参数的可靠性,给出了1种类人机器人腕用六维加速度传感器的算例,仿真实验获得的各弹性连接杆上应变的实验值与理论值的最大误差为1.617％.最后标定实验结果证明研究的理论和方法是有效和可靠的.     

仿人竖笛演奏机器人手设计

针对竖笛演奏需要,开发出两只具有相同结构的单关节3指机器人手,采用硬质铝合金作为手指材料,利用齿形带实现手指间接驱动,采用直流伺服电机(舵机)实现手指的节律运动.针对高、中、低音及长短音需要设计了一套气流控制系统,通过气压调节实现3种对应状态,通过调节每个分支管上节流阀的开度来得到所需要的气流,产生跟实际相符的音准,HK-Ⅱ机器人手的实际运行结果完全满足需要.     

Tolerance design optimization of machine elements using genetic algorithm

An important problem that faces design engineers is how to assign tolerance limits. In practical applications, tolerances are most often assigned as an informal compromise between functionality, quality and manufacturing cost. Frequently, the compromise is obtained iteratively by trial and error. A more scientific approach is often desirable for better performance. In this paper, a genetic algorithm (GA) is used for the design of tolerances of machine elements to obtain the global optimal solution. The objective is to design the optimum tolerances of the individual components to achieve the required assembly tolerance, zero percentage rejection of the components and minimum cost of manufacturing. The proposed procedure using GA is described in this paper for two tolerance design optimization problems: gear train and overrunning clutch assemblies. Results are compared with conventional techniques and the performances are analyzed.     

Welding parameters optimization for economic design using neural approximation and genetic algorithm

Welding is a basic manufacturing process for making components or assemblies. Recent welding economics research has focused on developing the reliable machinery database to ensure optimum production. It is an important issue, especially for the expensive equipment and the high quality preference in welding. An integrated approach is proposed to address the welding economic design problem. The integrated approach applies general regression neural network to approximate the relationship between welding parameters (welding current, electrode force, welding time, and sheet thickness) and the failure load. Analytical formula can be generated from the trained general regression neural network, and the mathematical model for the economic welding design can be constructed. An optimization method based on genetic algorithms is then applied to resolve the mathematical model and to select the optimum welding parameters. These parameters are used to obtain the preferred welding quality at the least possible cost.     

Multi-objective optimization for optimum tolerance synthesis with process and machine selection using a genetic algorithm

This paper presents a new approach to the tolerance synthesis of the component parts of assemblies by simultaneously optimizing three manufacturing parameters: manufacturing cost, including tolerance cost and quality loss cost; machining time; and machine overhead/idle time cost. A methodology has been developed using the genetic algorithm technique to solve this multi-objective optimization problem. The effectiveness of the proposed methodology has been demonstrated by solving a wheel mounting assembly problem consisting of five components, two subassemblies, two critical dimensions, two functional tolerances, and eight operations. Significant cost saving can be achieved by employing this methodology.     

Real-coded genetic algorithm for machining condition optimization

In this paper, we consider the machining condition optimization models presented in earlier studies. Finding the optimal combination of machining conditions within the constraints is a difficult task. Hence, in earlier studies standard optimization methods are used. The non-linear nature of the objective function, and the constraints that need to be satisfied makes it difficult to use the standard optimization methods for the solution. In this paper, we present a real coded genetic algorithm (RCGA), to find the optimal combination of machining conditions. We present various issues related to real coded genetic algorithm such as solution representation, crossover operators, and repair algorithm in detail. We also present the results obtained for these models using real coded genetic algorithm and discuss the advantages of using real coded genetic algorithm for these problems. From the results obtained, we conclude that real coded genetic algorithm is reliable and accurate for solving the machining condition optimization models.     

Cost optimization of submersible motors using a genetic algorithm and a finite element method

This paper presents an optimal design method to optimize cost of three-phase submersible motors. The optimally designed motor is compared with an industrial motor having the same ratings. The motor design procedure consists of a system of non-linear equations, which imposes induction motor characteristics, motor performance, magnetic stresses, and thermal limits. The genetic algorithm (GA) is used for cost optimization, and a software algorithm has been developed. As a result of the realized optimization, besides the improvements on the motor cost, motor torque improvements have also been acquired. The 2-D finite element method (FEM) is then used to confirm the validity of the optimal design. Computer simulation results are given to show the effectiveness of the proposed design process that can achieve a good prediction of the motor performance. Through the studies accomplished, it has been observed that submersible induction motors’ torques and efficiencies improve, their length reduces, and hence some material savings are obtained.     

类人足球机器人无侧摆动态步态规划研究

针对类人足球机器人提出了一种无侧摆的动态步态规划方法,从模型原理、数学描述、计算流程、求解方法等方面做了详细的阐述,并通过检验前向和侧向ZMP(零力矩点)位置证明机器人运动过程中无侧摆并保持了自身平衡。最后探讨了步态周期、步长、髋关节水平位置和竖直高度对于机器人行走稳定性的影响。     

仿人按摩机器人手臂结构设计及改进

仿人按摩机器人手臂是以传统中医按摩理论为基础,结合机器人定位精度高,按摩力量精确可控,动作可准确重复,不会产生疲劳等特点,构建了中医按摩机器人平台。机器人手臂是仿人按摩机器人的关键,在深入研究按摩机器人手臂的工作机理基础上,充分考虑手臂的材料、结构和刚度以及手臂反应的灵活性、稳定性、安全性、拟人化等要求,对大臂、肩关节、肘关节、小臂、手腕等关键环节进行了设计及改进。经过性能对比分析,设计的仿人按摩机器人手臂达到设计要求。     

遗传算法优化的线性二次型调节器控制下3-UPS并联机器人稳定性分析

通过对3-UPS并联机器人进行动力学建模与状态空间分析,基于遗传算法(GA)优化的线性二次型调节器(LQR)控制方法对其进行控制仿真,结合多体系统振动理论,并运用MATLAB软件和ADAMS软件联合仿真,分别对该并联机器人在动平台施加外部载荷、定平台施加随机振动,以及动、定平台施加复合干扰3种情况下机器人的末端稳定性进...     

New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm

Limited by techniques, the process of remanufacturing exists masses of uncertainties which have a great impact on the remanufactured parts quality, how to achieve a higher quality of mechanical products by using limited remanufactured parts precision, has become one of the key issues of remanufacturing industry. Firstly, with a target to reduce uncertainties and improve the quality of automatic products, a method of tolerance grading allocation for remanufactured parts is proposed based on the uncertainty analysis of the remanufacturing assembly. The dimensional tolerances of the mechanical parts are divided into positive and negative two groups. We use selective assembly method to reduce assembling deviation. Then, the method is proven by mathematical formulas that the remanufactured parts variance can be expanded to two times, and the tolerances can be liberalized 40 % through tolerance grading allocation method. It is also the theoretical basis for improving the reuse radio and quantitatively describing the tolerance liberalization in this paper. Finally, feasibility research on this method is studied from the angle of cost–benefit. Furthermore, a tolerance grading allocation example of remanufactured engine piston assembly in a power corporation shows the validity and practicality of the proposed method.     

Development of energy-saving optimization for the oval-edging oval pass design using genetic algorithm

In the multi-pass rod rolling systems, where the final products are round bars with different sizes, oval-edging oval passes are widely employed as intermediate passes. This study attempts to present a generalized strategy to support the optimal design of oval-edging oval passes. A design system based on this strategy is developed to minimize power consumption, to reduce trials and errors in industry, as well as to improve the design efficiency and flexibility. A series of equations are established for geometrical modeling and graphic plotting of oval-edging oval passes, which can realize the parametric transformation for roll pass design and optimization. A novel formula based on statistical analysis and data fitting is proposed for the calculation of power consumption. To verify this formula, results obtained are compared with those calculated from using the Smirnov’s formula. The comparison confirmed the accuracy and efficiency of this formula. Finally, the proposed approach is applied for the optimal design of oval-edging oval passes, which are widely employed for the intermediate passes in the rod rolling systems. Through the comparison between simulation results and experimental data from literature, it is concluded that the proposed method can reduce power consumption of these passes and is able to improve the efficiency and flexibility of intermediate pass design without compromising its accuracy.     

类人机器人腰部关节平衡作用研究与仿真

在双臂协调行为的研究中,对于类人机器人不仅涉及到双臂避碰、协调运动的轨迹规划等问题,还涉及到双臂动作时对身体ZMP的影响。当类人机器人搬动物体或执行其他工作时,由于身体质心的变化或者是质心加速度的变化对原来的运动平衡性造成影响。因此,我们在双臂协调的行为研究过程中,不得不考虑上体运动对身体ZMP的影响。这里通过调整腰部关节角度实现机器人在搬动物体时的静力学平衡,分析了通过腰部关节力矩调整实现机器人动平衡的方法,并给出了类机器人双臂搬运物体移动的仿真实例。