云制造环境下产品制造质量控制理论与方法研究

In cloud manufacturing environment, manufacturing systems present the characteristics of large scale, dynamicity and complex. Issues of the product quality variation source identification, the networked evolution mechanism for multivariate product quality features, the control of the transmission path of product quality elements among different cloud resource nodes and the optimization of high dimensional targets for product quality elements prove to be fundamental and key scientific problems to be solved. The network of network (NoN) model of the cloud manufacturing system is established, and the product multivariate quality elements are mapped into the model. Networked evolution mechanism for multivariate product quality features is analyzed based on SIRS model and network percolation theory. Mathematical correlation analysis is performed through adopting multi-element copula function, and quality loss function combining with maximum likelihood method is employed to identify the product quality variation source, so as to analyze the coupling relationship and control the transmission path of product quality elements among different cloud resource nodes. Aiming at the optimization of high dimensional targets for product quality elements, network model based optimization rules are exacted on the foundation of the obtained global and local parameters of the network. The optimization rules are integrated into the designed ε-dominance evolutionary algorithm with an adaptive ε parameter for large-dimensional and multi-objective problems, so that product quality controlling and optimization solutions with large dimension, high searching speed and global optimization can be achieved. This research provides innovative views and approaches for product quality controlling and optimization of manufacturing systems in cloud environment, and can be further extended to product quality controlling and optimization for other modern manufacturing systems. Moreover, it is also a typical application case of complex network theories in real world.

云制造环境下制造系统呈现规模巨大、动态且复杂化等特点，其产品质量变异源识别、质量变异网络化演化机理、质量变异传递路径控制、质量特性优化等问题是根本性的又是有待解决的关键科学问题。从复杂网络理论视角建立云制造环境下制造系统网络中的网络模型，将产品多元质量特性映射到模型上。借鉴SIRS模型及渗流理论分析产品多元质量特性网络化演化机理；采用多元Copula函数对产品质量特性进行分析，利用质量损失函数和最大似然法识别质量变异源并对质量变异传递路径进行控制；针对质量特性高维目标优化问题，提取基于网络全局和局部参量的寻优规则，并融入自适应ε调整策略的ε-支配高维目标优化算法，以获取兼顾求解规模、求解速度以及全局质量特性优化性能。本研究为解决云制造环境下制造系统产品质量控制与优化问题提供新的角度和方法，能进一步深化现代制造系统质量控制与优化建模与求解理论和技术，同时也为复杂网络相关理论提供应用范例。

云制造环境下制造系统的制造资源节点具有广域分布、高度复杂、不可确定等特点，其产品质量控制与优化问题呈现出一定挑战性，是云制造环境下提供高品质产品所必须解决的关键问题。基于此，课题对云制造环境下产品制造质量控制理论与方法研究，具体为：以复杂网络理论为基础，以云制造环境下制造系统产品质量控制的理论和方法为研究对象，构建了云制造环境下制造系统的“网络中网络”模型，分析了云制造环境下的制造任务分解方法，探讨了其多重质量特征的网络演化机理，提出了一种识别和定位云制造环境下制造系统产品质量变异源、质量变异耦合传递和路径控制方法。依托此课题，共资助了2位青年教师参与共同研究，并支持了1位博士研究生和4位硕士研究生的论文研究工作。共发表论文14篇，其中SCI检索6篇，EI检索6篇。申报发明专利2项。研究工作对促进云制造理论的完善和我国制造业的转型升级具有重要意义。

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