EAGER: A Self-Healing Approach for Smart Assembly Systems

该基金提供资金，用于在制造系统的质量改进方面进行新范式的初步研究。这种模式基于一种智能自我修复系统，该系统通过在装配过程中自动纠正故障或应用补偿操作来提高产品质量。智能自我修复系统将利用人工智能复制生物的关键特征，如感知、适应、冗余和去中心化。通过开发可控装配试验台，探索生物机理在控制系统设计中的适用性。拟议的试验台将集成最先进的传感器-执行器网络，以复制符合要求的或灵活的部件的组装。所提出的算法将整合三个数据来源：系统预测模型、过程数据和人类知识。该项目专注于汽车、重型设备、家电和航空航天工业中复杂装配系统的一种新的质量方法。自愈机制将在三个不同的层次上层次化地攻击故障：组件、工位和系统。开发一种范式转换的自愈方法来提高质量，将提供更快、更准确的故障预防、检测、识别和纠正。通过减少制造停机时间，将减少材料浪费和缺陷零件的数量，提高组装产品的质量，提高效率和生产能力。1)复杂非线性系统的装配变化预测模型；2)基于信息融合的装配系统的监测-检测-诊断方法；3)基于仿生方法的自修复装配系统的数学框架。

ABSTRACTThis grant provides funding to conduct preliminary research in a new paradigm in quality improvement for manufacturing systems. This paradigm is based on a smart self-healing system that increases production quality by autonomously correcting faults or applying compensation actions during the assembly process. The smart self-healing systems will replicate key characteristics of bio-organisms such as awareness, adaptation, redundancy, and decentralization by using artificial intelligence. The applicability of biological mechanisms in designing a control system will be explored by developing a controllable assembly testbed. The proposed testbed will integrate state-of-the-art sensor-actuator networks to replicate the assembly of compliant or flexible parts. The proposed algorithms will integrate three sources of data: system predictive models, process data, and human knowledge. This project focuses on a new quality method for complex assembly systems found in the automotive, heavy equipment, appliance, and aerospace industries. The self-healing mechanisms will hierarchically attack faults at three different levels: component, station, and system.The development of a paradigm-shifting self-healing approach to quality improvement will provide faster and more precise fault prevention, detection, identification, and correction. Material waste and the number of defective parts will be reduced, the quality of assembled products will be improved, and efficiency and production capacity will be increased through diminished manufacturing downtime. The work aims to advance the current knowledge in three areas: 1) predictive assembly variation models for complex nonlinear systems; 2) monitoring-detection-diagnosis methods for information fusion-based assembly systems; and 3) mathematical framework for a self-healing assembly system using a bio-inspired approach.