Research On Key Technologies Of Dynamic Service For Machine Tools In Cloud Manufacturing Environment

Widely distributed massive machine tool resources are the core resources for the production of discrete manufacturing enterprises.Nowdays,global manufacturing is undergoing major changes in manufacturing models,manufacturing processes,manufacturing methods and manufacturing ecosystems,and Cloud Manufacturing has become one of the most important methods and trends in the service-oriented and intelligent transformation of manufacturing enterprises.Furthering the application of Cloud Manufacturing service model in the workshop layer of discrete manufacturing enterprises to realize the sharing and coordination of idle machine tool resources,have an important strategic significance for the transformation and development of China’s vast discrete manufacturing enterprises.However,due to the diversity of service quality requirements of machine tool resources,the complexity of the service capacity components,and the complex and varied workshop environment,dynamic machine tool resource supply and demand matching and stable operation of machine tool equipment resources have always been critical issues that restrict the Cloud Manufacturing model’s application into the discrete manufacturing enterprise workshop layer.Based on relevant research results at home and abroad,this paper combines the theoretical basis of network topology analysis method and complex network to explore the construction mechanism and evolution law of machine tool resource’s dynamic service network in Cloud Manufacturing environment.Main works are as follows:（1）Aiming at the situation that multiple machine tool resources cooperate to complete single cloud service task in Cloud Manufacturing environment,the paper analyzes the decomposition of machine tool cloud service tasks,the optimal capability-task matching of machine tool resource,and the construction mechanism of single dynamic service network（SDN）.A seven-dimensional machine tool resource cloud service capability model including service time（T）,service quality（Q）,service cost（C）,technical capability（P）,service reliability（R）,service safety（S）,and service flexibility（F）,are proposed,Based on which,an SDN optimization matching model for machine tool resources under multivariate quality constraints and transfer distance is established considering proprieties of geographical dispersion,variety,heterogeneity,combination and dynamics in machine tool cloud resources.The SDN optimization matching algorithm based on improved ant colony algorithm is researched and a case study is given to validate the effectiveness of SDN optimization matching model.（2）In the case of multiple cloud service tasks sharing single or multiple machine tool resources in Cloud Manufacturing environment,the SDN of machine tool resources is analyzed to establish a complex dynamic network（CDN）.From the perspective of service revenue and service risk,the cloud service task selection evaluation system is established considering direct task revenue₀（6）（）,cooperation sustainability_{（8）（）,impact enhancement49}1))（）,quality requirements risk_{（6）（）,time requirement risk48})（）,business management level₀)（）,service reliability₀7))（）,and evaluation satisfaction₍（6）（）as indicators.A cloud machine service task optimization selection method for single machine tool resource servicer in Cloud Manufacturing environment considering time window and multi-objective constraints is proposed,and an example is given to verify the effectiveness of the proposed method.（3）Considering the dynamics,openness,non-linearity,self-organization and uncertainty of CDN’s machine tool equipment resources in Cloud Manufacturing,an evolution model of machine tool equipment resource dynamic service network based on node degree is proposed based on the evolution path and evolution mechanism of CDN.Through the derivation of change degrees for each node degree and the analysis of practical cases,the scale-free characteristic and critical mass during CDN’s evolution is explained and verified,which provides the theoretic foundation for further research in robustness and optimization decision method of CDN under random disturbances.（4）Aiming at the appearance of random disturbances such as equipment failure,worker exception and transportation obstruction during machine tool resources’service process in Cloud Manufacturing environment,the random disturbance factors and simulation strategies are analyzed and summarized.Based on the complex network theory,the evaluation indicators of machine tool resource CDN’s robustness are constructed and simulated.Combined with the results of simulation analysis,an optimization decision model for machine tool resources under stochastic disturbances and its solution method using Bayesian Network are researched.A case study is given to prove the effectiveness of the model and model’s solve method.Finally,the theory and method proposed in this paper are applied and verified in the achievement of the Chinese National Scientific and Technological Project（863Project）–“machine tool equipment resource optimization configuration cloud service platform”,which obtain a relatively satisfied result.