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

机译:闭环PMI驱动尺寸尺寸质量生命周期管理方法,用于智能制造系统

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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,构建和控制一个用于某些模块化产品架构的自组织智能制造系统,以支持大规模个性化,必须准确地预测制造过程的质量和性能。已经普遍研究了尺寸质量问题,以了解产品制造品质的变化以及各种点解决方案,如变异仿真分析(VSA),尺寸规划和验证(DPV)和坐标测量机(CMM)。但是,除非识别和管理生命周期内的所有主要原因,否则从未充分解决维度质量问题。本研究提供了一种整体方法,可以通过唯一的数据存储库从尺寸设计,规划和检查中建立一个整体方法,可以通过独特的数据存储库准确,有效,有效地携带和重用产品和制造信息( PMI)在产品生命周期时间。新方法使用特征相关的几何尺寸和公差(GD&T)信息重用,始终如一的尺寸质量生命周期和它捕获,识别和重复使用语义模型的3D几何特征及其相关的GD&T信息来集成通用语义模型。该方法还建立了封闭的环路优质生命周期管理系统,包括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。新方法已通过工业产品平台的功能失败研究应用。它证明,整体方法可以确保单一版本的真理,强制执行可重复使用的技术规范,消除不同的过程步骤中的筒仓,并使制造业的设计能够在早期阶段实现最佳成本/性能平衡。此外,本文提出了基于智能PMI技术的维度质量管理系统进步的未来研究方向,VR / AR和网络物理系统增强。

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