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Closed Loop PMI Driven Dimensional Quality Lifecycle Management Approach for Smart Manufacturing System

机译:智能制造系统的闭环PMI驱动的尺寸质量生命周期管理方法

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In order to devise, build and control a self-organizing smart manufacturing system for certain modular product architecture to support mass personalization, it is essential to accurately predict quality and performance of the manufacturing processes among others. Dimensional quality issues have been widely studied to understand causes of variations of product manufacturing qualities and various point solutions such as Variation Simulation Analysis (VSA), Dimensional Planning and Validation (DPV) and Coordinate Measurement Machine (CMM) have been adopted. However, dimensional quality issues will never be sufficiently addressed unless all major causes during lifecycle are identified and managed. This research provides a holistic approach to build a closed loop Plan-Do-Check-Act (PDCA) lifecycle from dimensional design, planning and inspections via a unique data repository which can accurately, effectively and smartly carry on and reuse Product and Manufacturing Information (PMI) during product lifecycle time. The new approach integrates universal semantic model using Feature Associated Geometric Dimensioning and Tolerancing (GD&T) Information Reuse, consistently stated Dimensional Quality Lifecycle and it captures, identifies and reuses 3D geometry characteristics and their associated GD&T information of the semantic model. The approach also establishes a closed loop quality lifecycle management system incorporating GD&T Design and Validation, Inspection Planning and Validation, Measurement Data Analysis and Reporting, and GD&T Design and Validation. The new approach has been applied by a functional failure study for an industrial product platform. It proves that the holistic approach can ensure single version of truth, enforce reusable technical specifications, eliminate silos among different process steps, and enable design for manufacturing to achieve optimal cost/performance balance at very early stage. Furthermore, the paper suggests future research directions of dimensional quality management system advancement based on smart PMI technologies augmented by Internet of Things, VR/AR and Cyber Physical Systems.
机译:为了设计,构建和控制用于某些模块化产品体系结构的自组织智能制造系统以支持大规模个性化,准确预测制造过程的质量和性能至关重要。尺寸质量问题已得到广泛研究,以了解产品制造质量变化的原因,并且采用了各种点解决方案,例如差异模拟分析(VSA),尺寸规划和验证(DPV)和坐标测量机(CMM)。但是,除非确定并管理生命周期中的所有主要原因,否则就永远无法充分解决尺寸质量问题。这项研究提供了一种整体方法,可通过尺寸设计,规划和检查,通过一个独特的数据存储库从尺寸设计,计划和检查建立一个闭环的Plan-Do-Check-Act(PDCA)生命周期,该数据存储库可以准确,有效和智能地进行和重复使用产品和制造信息( PMI)。新方法使用功能关联的几何尺寸和公差(GD&T)信息重用,一致表示的尺寸质量生命周期集成了通用语义模型,并且捕获,标识和重用了3D几何特征及其语义模型的关联GD&T信息。该方法还建立了一个包含GD&T设计和验证,检验计划和验证,测量数据分析和报告以及GD&T设计和验证的闭环质量生命周期管理系统。新方法已通过功能故障研究应用于工业产品平台。证明了整体方法可以确保真相的单一版本,强制执行可重复使用的技术规范,消除不同工艺步骤之间的孤岛,并使制造设计能够在很早的阶段达到最佳的成本/性能平衡。此外,本文提出了基于物联网,VR / AR和网络物理系统增强的智能PMI技术的尺寸质量管理系统发展的未来研究方向。

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