【作者】 姜云霞；

【导师】 郑敏利；

【作者基本信息】 哈尔滨理工大学 ， 机械制造及其自动化， 2015， 博士

【摘要】 云制造是一种面向服务的网络化制造新模式,它为中国制造业的服务化转型提供一种新的思路,但其还没有达到真正的落地实现的程度。作为网络化制造的一种模式,云制造强调按需获取资源,这一点正好符合互联网时代的制造企业对资源全面共享的诉求。逻辑资源的抽取和服务化是云制造的基础,本文以云制造环境下制造过程中加工制造阶段作为研究对象,搭建加工制造资源虚拟化框架,为了解决架构中资源如何描述、虚拟资源如何抽取和资源发现与优选的问题,从加工制造资源建模方法、加工制造资源的映射方法、制造资源选择与优化方法三个方面进行了深入研究。根据加工制造资源的特点,构建底层的资源虚拟化架构,建立虚拟资源抽取模型。建立基于元模型的加工制造资源及其虚拟资源的两层模型结构,研究加工制造资源的逻辑资源抽取策略。在分析本体概念的基础上,结合加工制造资源的特点,提出加工制造资源及其虚拟资源的元概念和元属性的形式化定义。在元模型的语义和语法的约束下,采用本体建模工具protégé和语义Web本体描述语言OWL,建立加工制造资源及其虚拟资源的模型层,通过切削加工资源虚拟化原型系统进行验证。在此基础上,提出资源组合模型和组合服务模型。依据加工制造资源的特性,建立其虚拟化映射模型,分析映射的基本规则和实现流程。研究了基于ai Net人工免疫网络的资源聚类方法;为解决ai Net网络呈现无规律的动态变化的问题,定义优化目标函数,提出改进的ai Net免疫学习算法。对算法的时间复杂度和合理性进行了评价,并对其进行了实例验证。研究加工制造资源—虚拟资源的语义本体映射方法,分析了基于语义相等的映射实现原理。依据加工制造任务的对象特性和云制造的特点,在资源云池的基础上,研究加工制造过程中资源发现的流程,建立加工制造任务的形式化模型,阐述了任务分解的原则。为保证子任务集获得最优的资源集,研究资源候选集的优化选择方法,建立加工制造资源优选的评价指标体系。利用模糊层次分析法的模糊一致比较矩阵对评价指标的权重进行评价,给出权重评价向量。在云制造环境下,加工制造任务链是串并行共存、加工制造资源异地共享,为此,时间和成本两个评价因素中引入物流和仓储的时间和成本。依据评价指标体系,建立资源优选多目标函数;依据遗传算法全局搜索性与精英保留策略,实现资源优选算法的设计,给出了相应的算法流程。最后通过切削加工类资源的虚拟化原型系统和轴承座组件加工实例对上述理论进行了验证。更多还原

【Abstract】 Cloud manufacturing is a new service-oriented and networked manufacturing mode, it provides a new train of thought for transformation of service for China’s manufacturing industry, but it has not reached the degree of real landing realization. As a mode of networked manufacturing, cloud manufacturing emphasizes on-demand access to the resource, and this point is very good in line with the demands of the manufacturing enterprises to comprehensive sharing of resources in the Internet age. Logical resource extraction and service is the foundation of cloud manufacturing, this thesis takes manufacturing stage of the manufacturing process in the cloud manufacturing environment as the research object. In view of the problems existing in the virtualization and service process of manufacturing resources, the thesis has conducted the thorough research from the overall research of virtualization system、manufacturing resource modeling、manufacturing resources mapping method and manufacturing resource selection and implementation.According to the characteristic of manufacturing resources, the underlying virtual architecture is built, establishing the extraction model of virtual resources, establishing the model of manufacturing resources and virtual resources by the metamodel, researching the virtual resource extraction strategy of manufacturing resources. Based on the analysis on the concept of ontology, combined with the characteristics of manufacturing resources, the thesis puts forward the formalization definition of meta-concepts and meta-properties of manufacturing resources and virtual resources, and builds the ontology construction model of manufacturing resources on the basis of OWL. It is proved to be effective through the prototype system. On this basis, the resource combination model and the composite service model are put forward.According to the features of manufacturing resources, the thesis establishes the virtualization mapping model, and analyzes the basic mapping rules and the process realization, and researches premise of the mapping, namely, the clustering method of resources. In order to solve the problem of irregular dynamic change of aiNet network, the optimization objective function is defined to put forward the improved algorithm of aiNet immune learning network. The instance validation is done, the time complexity and rationality of algorithm is evaluated. At same time, the thesis researches the semantic ontology mapping method of manufacturing resources and virtual resources, and analyzes the mapping realization principle on basis of the semantic equivalent.According to the features of manufacturing task and cloud manufacturing, based on cloud pool of resources, the thesis researches the resource discovery process in the process of manufacturing, establishing the formalized model of manufacturing task, expounding the decomposition principle of task. In order to ensure the sub task set to obtain the optimal set of resources, to focus on the research of optimal of selection method of manufacturing resource, the optimization evaluation index system is established. Using the fuzzy consistent comparison matrix of hierarchy process to evaluate the weight of evaluation index, the evaluation vector is given. Under the cloud manufacturing environment, the serial and parallel processing exists in the chain of manufacturing tasks, and manufacturing resources are shared in the whole of country. So, the time and cost of logistics and warehousing are added into the evaluation index. On the basis of the evaluation index system, setting up the optimization objective function of resource with the constraint. Based on the search capability of global and the concept of Pareto set of genetic algorithm, realizing the design of resource optimization algorithm, the algorithm process is given.Finally through the virtual prototype system of cutting manufacturing resource and bearing components processing example, the above theory is verified.更多还原