串并联多工位制造系统误差传播分析与多变量过程质量控制

项目名称： 串并联多工位制造系统误差传播分析与多变量过程质量控制

项目编号： No.51275191

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 机械、仪表工业

项目作者： 朱海平

作者单位： 华中科技大学

项目金额： 80万元

中文摘要： 过程质量控制是制造系统运行优化的核心问题，过程一旦失控将大大增加质量风险。本项目在误差流（SoV）、误差传播分析、多变量统计过程控制（MSPC）等最新成果的基础上，通过创新手段对串并联多工位制造系统的过程质量控制问题进行研究：首先将SoV方法从串联推广到串并联过程，融合工程驱动SoV和数据驱动MSPC的优点，建立SoV/MSPC集成的"物理-统计"误差传播模型，揭示阶段误差相互影响、传播、累计的规律；然后，深入分析质量数据相关性和自相关性，提出经济和统计综合优化的控制图设计方法，基于误差传播分析实现过程失控自动判别和误差源定位；再后，定量分析"设备/工装/刀具"失效对质量特性的影响，建立制造系统可靠性和质量控制集成优化模型及经济性维修策略，实现过程异常波动的诊断和预防控制；最后，基于误差传播模型，通过生产线布局优化、面向质量的工艺路线评估、质量和生产性能指标集成优化等途径实现过程持续改进。

中文关键词： 误差传播分析；统计过程控制；维修决策；控制图；优化模型

英文摘要： Quality control of production process is one of the critical problems in the operational optimization of manufacturing system. Product quality risk will increase greatly when the production process is out of control. In the proposal, we focus on the process quality control problem of the serial-parallel multi-station manufacturing system (SP-MMS) by applying novel methods on the basis of up-to-date achievements such as Stream of Variation (SoV), variation propagation analysis and Multivariate Statistical Process Control (MSPC). Firstly, the SoV method is extended from the serial manufacturing processes to the serial-parallel manufacturing processes. By combining the advantages of engineering knowledge-driven SoV method and data-driven MSPC method, a SoV/MSPC integrated "physical-statistical" variation propagation model is set up, which can reveal the basic rules of interaction, propagation and accumulation of variations in all production stages. Secondly, by analyzing the inter-dependency and auto-correlation of quality characteristics in depth, an economic-statistical control chart design model is proposed, and then the out-of-control patterns of control chart are automatically recognized and the variation sources are identified through the variation propagation analysis. Thirdly, the impact of "equipment/fixtu

英文关键词： variation propagation analysis；statistical process control；maintenance decision-making；control chart；optimization model