共查询到20条相似文献,搜索用时 156 毫秒
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针对STEP-NC制造特征的加工工艺路线生成与优化问题,提出了一种以机床、夹具及刀具更换率最低为目标函数的禁忌制造特征动态更新的工艺路线蚁群优化方法。首先,根据加工工艺对制造特征进行分解,将工艺路线优化问题转化为对制造特征的排序;然后根据制造特征在加工过程中存在的加工遮挡关系以及刚性工艺性约束,提出禁忌制造特征动态更新的工艺路线生成方法;最后将禁忌制造特征动态更新方法与Ant-Cycle模型的蚁群算法相结合,求解制造特征工艺路线的优化问题。实例应用表明,提出的方法能较好解决加工工艺路线优化过程中的刚性约束处理问题。 相似文献
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提出了一种基于遗传算法和约束矩阵的工艺路线优化方法.该方法利用约束矩阵来描述加工元间的优先关系,由系统自动生成约束矩阵,开发了保证加工元序列满足工艺约束关系的加工元序列有效性检验与调整算法.以总变换成本最小为优化目标,采用改进的遗传算法进行工艺路线的优化,以实现工艺过程的全局优化. 相似文献
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在重点分析零件特征典型加工方案及加工方案组合优化原则的基础上,基于STEP-NC应用遗传算法建立了零件加工方案组合优化的数学模型。并将该方法应用到具体零件的工艺排序决策过程中,通过编码、杂交、复制、变异等得到满足零件要求的最优或接近最优的工艺路线。验证了遗传算法在STEP-NC工艺路线排序中的有效性。 相似文献
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Kunlei Lian Chaoyong Zhang Xinyu Shao Liang Gao 《The International Journal of Advanced Manufacturing Technology》2012,59(5-8):815-828
In this paper, we investigate the optimization of process planning in which various flexibilities are considered. The objective is to minimize total weighted sum of manufacturing costs. Various flexibilities, including process flexibility, sequence flexibility, machine flexibility, tool flexibility, and tool access direction (TAD) flexibility, generally exist in process planning and consideration of these flexibilities is essential for improving production efficiency and system flexibility. However, process planning is strongly NP-hard due to the existence of various flexibilities as well as complex machining precedence constraints. To tackle this problem, an imperialist competitive algorithm (ICA) is employed to find promising solutions with reasonable computational cost. ICA is a novel socio-politically motivated metaheuristic algorithm inspired by imperialist competition. It starts with an initial population and proceeds through assimilation, position exchange, imperialistic competition, and elimination. Computational experiments on three sets of process planning problem taken from literature are carried out, and comparisons with some existing algorithms developed for process planning are presented. The results show that the algorithm performs significantly better than existing algorithms like genetic algorithm (GA), simulated annealing (SA), tabu search (TS), and particle swarm optimization (PSO). 相似文献
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具有柔性加工路径的作业车间智能优化调度 总被引:3,自引:0,他引:3
用遗传算法研究了具有柔性加工路径的作业车间的智能优化调度问题 ,提出了一种将遗传算法和分派规则相结合的调度算法 ,将加工计划与生产调度同时考虑 ,避免了加工计划和生产调度相脱节的弊端。最后给出了此调度算法的仿真结果 ,证明该算法是可行的 ,并获得优异的结果 相似文献
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Optimization of continuous-time production planning using hybrid genetic algorithms-simulated annealing 总被引:2,自引:0,他引:2
K. Ganesh M. Punniyamoorthy 《The International Journal of Advanced Manufacturing Technology》2005,26(1-2):148-154
Evolutionary algorithms are stochastic search methods that mimic the principles of natural biological evolution to produce better and better approximations to a solution and have been used widely for optimization problems. A general problem of continuous-time aggregate production planning for a given total number of changes in production rate over the total planning horizon is considered. It is very important to identify and solve the problem of continuous-time production planning horizon with varying production rates over the interval of the planning period horizon. Some of the researchers have proposed global search methods for the continuous-time aggregate production-planning problem. So far, less work is reported to solve the problem of continuous-time production planning using local search methods like genetic algorithms (GA) and simulated annealing (SA). So in this work, we propose a modified single objective evolutionary program approach, namely GA, SA, and hybrid genetic algorithms-simulated annealing (GA-SA) for continuous-time production plan problems. The results are compared with each other and it was found that the hybrid algorithm performs better. 相似文献
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Guofu Luo Xiaoyu Wen Hao Li Wuyi Ming Guizhong Xie 《The International Journal of Advanced Manufacturing Technology》2017,91(9-12):3145-3158
Process planning and scheduling are two major sub-systems in a modern manufacturing system. In traditional manufacturing system, they were regarded as the separate tasks to perform sequentially. However, considering their complementarity, integrating process planning and scheduling can further improve the performance of a manufacturing system. Meanwhile, the multiple objectives are needed to be considered during the realistic decision-making process in a manufacturing system. Based on the above requirements from the real manufacturing system, developing effective methods to deal with the multi-objective integrated process planning and scheduling (MOIPPS) problem becomes more and more important. Therefore, this research proposes a multi-objective genetic algorithm based on immune principle and external archive (MOGA-IE) to solve the MOIPPS problem. In MOGA-IE, the fast non-dominated sorting approach used in NSGA-II is utilized as the fitness assignment scheme and the immune principle is exploited to maintain the diversity of the population and prevent the premature condition. Moreover, the external archive is employed to store and maintain the Pareto solutions during the evolutionary process. Effective genetic operators are also designed for MOIPPS. To test the performance of the proposed algorithm, three different scale instances have been employed. And the proposed method is also compared with other previous algorithms in literature. The results show that the proposed algorithm has achieved good improvement and outperforms the other algorithms. 相似文献
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D. Kondayya A. Gopala Krishna 《The International Journal of Advanced Manufacturing Technology》2013,65(1-4):259-274
This paper presents a new integrated methodology based on evolutionary algorithms (EAs) to model and optimize the laser beam cutting process. The proposed study is divided into two parts. Firstly, genetic programming (GP) approach is used for empirical modelling of kerf width (Kw) and material removal rate (MRR) which are the important performance measures of the laser beam cutting process. GP, being an extension of the more familiar genetic algorithms, recently has evolved as a powerful optimization tool for nonlinear modelling resulting in credible and accurate models. Design of experiments is used to conduct the experiments. Four prominent variables such as pulse frequency, pulse width, cutting speed and pulse energy are taken into consideration. The developed models are used to study the effect of laser cutting parameters on the chosen process performances. As the output parameters Kw and MRR are mutually conflicting in nature, in the second part of the study, they are simultaneously optimized by using a multi-objective evolutionary algorithm called non-dominated sorting genetic algorithm II. The Pareto optimal solutions of parameter settings have been reported that provide the decision maker an elaborate picture for making the optimal decisions. The work presents a full-fledged evolutionary approach for optimization of the process. 相似文献
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基于改进粒子群算法的生产批量计划问题研究 总被引:12,自引:0,他引:12
为求解基于成组单元有能力约束的生产批量计划问题,提出了一种基于二进制粒子群算法和免疫记忆机制相结合的方法,并阐明了该方法的具体实现过程。在该方法中,采用罚函数法处理约束条件,每个粒子都代表一组可用于描述具体批量计划方案的规则组合。通过对其他文献中一个仿真实例的计算和结果比较,表明该算法在寻优能力、求解速度和稳定性等方面都明显优于文献中的遗传算法。 相似文献
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针对目前室内清洁机器人的路径规划算法理论研究较多,而难以应用于实践的问题,建立了清洁机器人相对定位的数学模型,通过使用清洁机器人的迂回式路径规划、回字形路径规划、包围式路径规划和启发式路径规划算法进行了研究,分析了路径规划算法的具体实现过程.利用微软机器人开发平台(MRDS),使用可视化编程语言(VPL),对4种路径规划算法进行仿真实验.对启发式路径规划中激光测距仪的返回数据进行了分析,将单位时间内各路径规划算法的转弯角度作为评价算法优劣的标准,比较了各路径规划算法的优缺点.研究结果表明,启发式路径规划中,清洁机器人能够根据当前的环境信息选择最佳路径,相同时间内所用转弯角度最少,该算法优于其他算法,具有一定的推广价值. 相似文献
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Assembly path planning is a crucial problem in assembly related design and manufacturing processes. Sampling based motion planning algorithms are used for computational assembly path planning. However, the performance of such algorithms may degrade much in environments with complex product structure, narrow passages or other challenging scenarios. A computational path planner for automatic assembly path planning in complex 3D environments is presented. The global planning process is divided into three phases based on the environment and specific algorithms are proposed and utilized in each phase to solve the challenging issues. A novel ray test based stochastic collision detection method is proposed to evaluate the intersection between two polyhedral objects. This method avoids fake collisions in conventional methods and degrades the geometric constraint when a part has to be removed with surface contact with other parts. A refined history based rapidly-exploring random tree (RRT) algorithm which bias the growth of the tree based on its planning history is proposed and employed in the planning phase where the path is simple but the space is highly constrained. A novel adaptive RRT algorithm is developed for the path planning problem with challenging scenarios and uncertain environment. With extending values assigned on each tree node and extending schemes applied, the tree can adapts its growth to explore complex environments more efficiently. Experiments on the key algorithms are carried out and comparisons are made between the conventional path planning algorithms and the presented ones. The comparing results show that based on the proposed algorithms, the path planner can compute assembly path in challenging complex environments more efficiently and with higher success. This research provides the references to the study of computational assembly path planning under complex environments. 相似文献