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Optimization of complex part-machining services based on feature decomposition in cloud manufacturing
International Journal of Computer Integrated Manufacturing ( IF 4.1 ) Pub Date : 2020-09-27 , DOI: 10.1080/0951192x.2020.1815845
Liang Guo 1 , Yang Xu 1 , Wei He 1 , Yunxi Cheng 1
Affiliation  

ABSTRACT Cloud manufacturing (CM) is a new service-oriented networked manufacturing mode. The optimal configuration of manufacturing services is one of most challenging topics in CM. Most research focuses on service composition optimization algorithms. However, for different manufacturing tasks, the configuration mode of the manufacturing services is different. For complex parts, effectively using the appropriate optimization strategy to solve the optimization of machining services is still rare in CM. To solve the above problem, a new machining task decomposition and service optimization strategy is proposed. Under this mode, the features of the complex part are defined as the basic task granularity. Four machining service optimization modes are constructed, and a mathematical model of machining service optimization under the four modes is established. Subsequently, a particle swarm optimization algorithm based on simulated annealing (PSOBSA) is designed by combining the particle swarm optimization (PSO) and simulated annealing (SA). Finally, three groups of simulation experiments are conducted to simulate the optimization mode of complex parts machining services based on feature decomposition. The simulation results demonstrate the feasibility of the service optimization mode and the effectiveness of the PSOBSA. The research results presented in this paper provide an machining service outsourcing method for complex parts.

中文翻译:

云制造中基于特征分解的复杂零件加工服务优化

摘要 云制造(CM)是一种新型的面向服务的网络化制造模式。制造服务的优化配置是CM中最具挑战性的话题之一。大多数研究集中在服务组合优化算法上。但是,对于不同的制造任务,制造服务的配置模式是不同的。对于复杂的零件,有效地使用合适的优化策略来解决加工服务的优化问题在CM中还是很少见的。针对上述问题,提出了一种新的加工任务分解和服务优化策略。在这种模式下,将复杂部分的特征定义为基本任务粒度。构建了四种加工服务优化模式,建立了四种模式下加工服务优化的数学模型。随后,将粒子群优化(PSO)和模拟退火(SA)相结合,设计了一种基于模拟退火的粒子群优化算法(PSOBSA)。最后,通过三组仿真实验来模拟基于特征分解的复杂零件加工服务优化模式。仿真结果证明了服务优化模式的可行性和PSOBSA的有效性。本文的研究成果为复杂零件的加工服务外包提供了一种方法。结合粒子群优化(PSO)和模拟退火(SA)设计了一种基于模拟退火的粒子群优化算法(PSOBSA)。最后,通过三组仿真实验来模拟基于特征分解的复杂零件加工服务优化模式。仿真结果证明了服务优化模式的可行性和PSOBSA的有效性。本文的研究成果为复杂零件的加工服务外包提供了一种方法。结合粒子群优化(PSO)和模拟退火(SA)设计了一种基于模拟退火的粒子群优化算法(PSOBSA)。最后,通过三组仿真实验来模拟基于特征分解的复杂零件加工服务优化模式。仿真结果证明了服务优化模式的可行性和PSOBSA的有效性。本文的研究成果为复杂零件的加工服务外包提供了一种方法。仿真结果证明了服务优化模式的可行性和PSOBSA的有效性。本文的研究成果为复杂零件的加工服务外包提供了一种方法。仿真结果证明了服务优化模式的可行性和PSOBSA的有效性。本文的研究成果为复杂零件的加工服务外包提供了一种方法。
更新日期:2020-09-27
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