Abstract
The fast-changing market and increasing demand for customised products have imposed manufacturers to improve the flexibility and robustness of their manufacturing execution systems. The ability to recover from exceptional events is fundamental to autonomous manufacturing systems in the era of smart manufacturing. Currently, the various types of manufacturing exceptions remain unclear. This research investigated the typical exceptions and proposed a general framework that supports the autonomous exception-handling and resource discovery in dynamic environments. A multi-layer peer-to-peer network is used to model the resources, services, and events in decentralised manufacturing systems. The exception-handling mechanism is designed that incorporates rule-based reactions, matching of features, recording of behaviour patterns etc. The feasibility of the proposed methods is also discussed, which shows many exceptions that were ignored in previous scheduling models can be timely identified and easily handled. This research explores the complex relations among manufacturing resources and provides an intelligent overall framework for self-organising manufacturing with self-diagnose capabilities.
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