This 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.
