云制造环境下加工制造资源虚拟化关键技术研究

发布时间：2018-05-09 19:25

本文选题：云制造 + 制造资源　；参考：《哈尔滨理工大学》2015年博士论文

【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。
[Abstract]:Cloud manufacturing is a new service-oriented networked manufacturing model, which provides a new way of thinking for the service transformation of Chinese manufacturing industry, but it has not reached the real level of realization. As a model of networked manufacturing, cloud manufacturing emphasizes on acquiring resources on demand, which is exactly in line with the demand for comprehensive sharing of resources in the manufacturing enterprises in the Internet era. Logical resource extraction and service are the basis of cloud manufacturing. This paper takes the manufacturing phase of manufacturing process in the cloud manufacturing environment as the research object, builds the virtual framework of manufacturing resources, in order to solve the problem of how to describe the resources in the architecture. The problems of how to extract virtual resources and how to find and select resources are studied in this paper from three aspects: modeling method of manufacturing resources mapping method of manufacturing resources selection and optimization of manufacturing resources. According to the characteristics of manufacturing resources, a virtual resource extraction model is established. A two-layer model structure of machining manufacturing resources and its virtual resources based on metamodel is established, and the logical resource extraction strategy of processing manufacturing resources is studied. Based on the analysis of ontology concept and the characteristics of machining manufacturing resources, the formal definition of meta-concept and meta-attribute of machining manufacturing resources and their virtual resources is proposed. Under the constraints of semantics and syntax of metamodel, the model layer of machining manufacturing resources and virtual resources is established by using ontology modeling tool prot 茅 g 茅 and semantic Web ontology description language owl, and verified by cutting machining resource virtualization prototype system. On this basis, a resource composition model and a composite service model are proposed. According to the characteristics of manufacturing resources, the virtual mapping model is established, and the basic rules and implementation flow of mapping are analyzed. The resource clustering method based on ai Net artificial immune network is studied. In order to solve the problem that ai Net network presents irregular dynamic change, the optimization objective function is defined, and an improved ai Net immune learning algorithm is proposed The time complexity and rationality of the algorithm are evaluated and verified by an example. The semantic ontology mapping method of processing manufacturing resources-virtual resources is studied, and the principle of mapping based on semantic equality is analyzed. According to the object characteristics of manufacturing tasks and the characteristics of cloud manufacturing, based on the resource cloud pool, the process of resource discovery in manufacturing process is studied, the formalized model of manufacturing tasks is established, and the principle of task decomposition is expounded. In order to ensure the subtask set to obtain the optimal resource set, the optimal selection method of the resource candidate set is studied, and the evaluation index system of the processing manufacturing resource optimal selection is established. The weight of the evaluation index is evaluated by the fuzzy uniform comparison matrix of the fuzzy analytic hierarchy process, and the weight evaluation vector is given. In the cloud manufacturing environment, the processing manufacturing task chain coexists in parallel, and the processing manufacturing resources are shared in different places. Therefore, the time and cost of logistics and storage are introduced into the evaluation factors of time and cost. According to the evaluation index system, the multi-objective function of the optimal selection of resources is established, and the design of the optimal selection algorithm of resources is realized according to the global search ability of genetic algorithm and the strategy of elite reservation, and the corresponding algorithm flow is given. Finally, the above theory is verified by the virtual prototype system of machining resources and the example of housing component machining.
【学位授予单位】：哈尔滨理工大学
【学位级别】：博士
【学位授予年份】：2015
【分类号】：TH164

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