Self-Resilient Reconfigurable Assembly Systems with In-process Quality Improvement

全球化和不断变化的客户需求导致产品定制，品种和上市时间加剧了全球汽车和航空航天制造业的巨大竞争。制造商面临着巨大的压力，要在不牺牲质量的情况下快速、低成本地满足不断变化的客户需求。为了应对这些挑战，制造商提供了灵活且可重构的装配系统。然而，一个主要的挑战是如何在低投资的情况下获得产品系列的产量灵活性，并能够在允许快速生产的同时获得高产品质量和吞吐量。克服这些挑战涉及三个要求，这是本文的重点：(1)建模可重构装配系统架构。系统架构应该有目的地考虑由产品族组合和产品需求引发的未来不确定性。这将需要最小化系统的可变性，同时最大化系统的可重用性以降低成本；(2)针对给定的系统可变性要求，开发能够预测工艺能力和管理产品质量的新方法；(3)利用新兴技术并将其快速整合到现有的生产系统中，例如新的连接工艺（远程激光焊接）和新材料。该项目将通过开发一种自适应可重构装配系统来解决这些问题，该系统具有过程质量改进能力，能够从(1)6西格玛质量故障中自我恢复；（ii）设计和制造的变化。在此过程中，它将在以下方面超越最先进的技术和实践：(1)由于当前的系统架构在响应不断变化的需求方面面临重大挑战，该计划将通过集成不确定性模型将涉及必要变更的成本、时间和风险纳入其中；所需变更的决策模型；系统变化建模；(2)目前的过程质量监控系统使用基于点的测量，有限的6西格玛失效根本原因识别。它们很少能快速纠正操作缺陷，也不能提供深入的信息来理解和模拟与零件和组件变形相关的制造缺陷。通常，现有的基于表面的扫描仪用于零件检查，而不是过程中的质量控制。该项目将集成基于地面的在线测量与自动根本原因分析、前馈/反馈过程调整和控制，以增强系统对故障或质量/生产力下降的响应。针对多扇区应用的可重构装配系统进行研究。它将涉及系统的可变性/适应性和过程中的质量改进：(i)汽车门组件，用于实施新兴的连接技术，例如远程激光焊接（RLW），用于精确的闭环表面质量控制；（ii）用于预测工艺能力的机身装配，也用于精确的闭环表面质量控制。结果将为英国的高附加值制造业带来重大利益。它将通过加速引进新兴的生态友好工艺，例如RLW，进一步加强该部门。它将促进跨一系列学科的跨学科合作，如数据挖掘和过程挖掘、先进计量、制造和复杂性科学等。可重构装配系统（RAS）与过程质量改进（IPQI）的集成是一个新兴领域，这一倡议将有助于使其发展成为一个国际重要的研究领域。研究结果将为工程课程的组成部分提供信息，特别是这些课程与培养未来的工程师以领导高价值制造业和数字经济有关。

Globalization and ever-changing customer demands resulting in product customization, variety and time to market have intensified enormous competition in automotive and aerospace, manufacturing worldwide. Manufacturers are under tremendous pressures to meet changing customer needs quickly and cost effectively without sacrificing quality. Responding to these challenges manufacturers have offered flexible and reconfigurable assembly systems. However, a major challenge is how to obtain production volume flexibility for a product family with low investment and capability to yield high product quality and throughput while allowing quick production ramp-up. Overcoming these challenges involves three requirements which are the focus of this proposal: (1) Model reconfigurable assembly system architecture. The system architecture should purposefully take into account future uncertainties triggered by product family mix and product demands. This will require minimizing system changeability while maximizing system reusability to keep cost down; (2) Develop novel methodologies that can predict process capability and manage product quality for given system changeability requirements; and (3) Take advantage of emerging technologies & rapidly integrate them into existing production system, for e.g., new joining processes (Remote Laser Welding) and new materials.This project will address these factors by developing a self-resilient reconfigurable assembly system with in-process quality improvement that is able to self-recover from (i) 6-sigma quality faults; and (ii) changes in design and manufacturing. In doing so, it will go beyond state-of-the-art and practice in following ways: (1) Since current system architectures face significant challenges in responding to changing requirements, this initiative will incorporate cost, time and risks involving necessary changes by integrating uncertainty models; decision models for needed changes; and system change modelling; and (2) Current in-process quality monitoring systems use point-based measurements with limited 6-sigma failure root cause identification. They seldom correct operational defects quickly and do not provide in-depth information to understand and model manufacturing defects related to part and subassembly deformation. Usually, existing surface-based scanners are used for parts inspection not in-process quality control. This project will integrate in-line surface-based measurement with automatic Root Cause Analysis, feedforward/feedback process adjustment and control to enhance system response to fault or quality/productivity degradation. The research will be conducted for reconfigurable assembly system with multi-sector applications. It will involve system changeability/adaptation and in-process quality improvement for: (i) Automotive door assembly for implementing an emerging joining technology, e.g. Remote Laser Welding (RLW), for precise closed-loop surface quality control; and (ii) Airframe assembly for predicting process capability also for precise closed-loop surface quality control. Results will yield significant benefits to the UK's high value manufacturing sector. It will further enhance the sector by accelerating introduction of new emerging eco-friendly processes, e.g., RLW. It will foster interdisciplinary collaboration across a range of disciplines such as data mining and process mining, advanced metrology, manufacturing, and complexity sciences, etc. The integration of reconfigurable assembly systems (RAS) with in-process quality improvement (IPQI) is an emerging field and this initiative will help to engender the development into an internationally important area of research. The results of the research will inform engineering curriculum components especially as these relate to training future engineers to lead the high value manufacturing sector and digital economy.