Two-Stage Optimization of PCBA Placement Route Schedule Based on Deep Reinforcement Learning

In printed circuit board assembly (PCBA), placement route schedule (PRS) significantly affects assembly efficiency of the beam head placement machine. The PRS is typically solved by decomposing it into placement point assignment problem (PPAP) and beam heads sequencing problem (BHSP). This article first proposes a deep reinforcement learning framework to tackle PPAP, which is a key determinant of overall process quality. Then, to mitigate the impact of placement position and angle on assembly efficiency, a dynamic programming-based beam head sequencing algorithm is introduced to solve BHSP. Since component types and placement point assignment states vary across different pick-and-place cycles, a dynamic combinatorial mask encoding method is proposed to effectively extract feature information between placement points. Inspired by the beam head placement process, a decoder that combines gated recurrent units and an attention mechanism is finally introduced, which fully utilizes historical node information to predict the next node. Experimental results demonstrate that the proposed method reduces PCBA routing distance by an average of 4.62%, outperforming other State-of-the-Art approaches.