Automated experience-based learning for plug and produce assembly systems

Abstract

This paper presents a self-learning technique for adapting modular automated assembly systems. The technique consists of automatically analysing sensor data and acquiring experience on the changes made on an assembly system to cope with new production requirements or to recover from disruptions. Experience is generalised into operational knowledge that is used to aid engineers in future adaptations by guiding them throughout the process. At each step, applicable changes are presented and ranked based on: 1) similarity between the current context and those in the experience base; 2) estimate of the impact on system performance. The experience model and the self-learning technique reflect the modular structure of the assembly machine and are particularly suitable for plug and produce systems, which are designed to offer high levels of self-organisation and adaptability. Adaptations can be performed and evaluated at different levels: from the smallest pluggable unit to the whole assembly system. Knowledge on individual modules can be reused when modules are plugged into other systems. An experimental evaluation has been conducted on an industrial case study and the results show that, with experience-based learning, adaptations of plug and produce systems can be performed in a shorter time.

Design of a self-learning multi-agent framework for the adaptation of modular production systems

“…Research on the computational aspects includes experience-based learning techniques, using classification algorithms, for the adaptation of assembly systems [5]. Adaptation of plug and produce systems is discussed by [6]. In plug and produce systems, identification and configuration of new devices is performed with minimal human intervention.…”