Fault pattern identification in multi-stage assembly processes with non-ideal sheet-metal parts based on reinforcement learning architecture

Abstract

A reinforcement learning-based architecture to address the fault detection on body in white assembly processes is introduced in this paper. During the research were addressed: (i) generation of a random defect pattern database using a multi-physics variation modeling for multi-stage assembly systems; (ii) design and implementation of a fault pattern identification reinforcement learning-based architecture, combining neural network, genetic algorithm and Q-learning algorithms; and (iii) validation based on non-ideal sheet-metal parts case study generated by the Variation Response Method toolkit. Finally, a comparative study between the different topologies is done, highlighting the influence of the Q-learning in the default identification process.