.png){kind=icon}
Return
A Coordinated Optimization Framework for Placement Machine Programming in Multiorder PCB Assembly
.png){kind=icon}
PRE
AI
DOI：10.1109/tii.2026.3737408.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
High-mix and low-volume printed circuit board (PCB) assembly requires frequent production switching among different PCB orders on the same placement machine. Conventional separate-order programming optimizes each order independently, which may lead to repeated feeder reconfiguration and nozzle change when consecutive orders are executed. To address this issue, this article proposes a coordinated nozzle assignment and feeder arrangement (CNAFA) framework that integrates cross-order resource configuration with order-level execution planning, rather than treating nozzle assignment, feeder arrangement, and pick-and-place (PAP) optimization as isolated decisions. A multiorder production-time formulation is established to jointly characterize order-level execution time and transition-related setup time, enabling these coupled decisions to be optimized under a unified objective. Within this framework, nozzle configurations are coordinated across orders, while the shared feeder arrangement is optimized by an embedded evolutionary search with a picking-based surrogate fitness. The PAP solver further generates executable cycle-level head assignments and picking and placement sequences under the resulting fixed resource configurations. Experiments on industrial PCB orders using machine-recorded production times show average production-time reductions of 17.9% over Panasonic DGS and 13.9% over UCH across 32 feasible two-order combinations, with similar advantages observed for larger order batches. These results demonstrate the effectiveness and practical value of CNAFA for multiorder PCB assembly.
Keywords:
Evolutionary search
feeder arrangement
multiorder printed circuit board (PCB) assembly
nozzle configuration
placement machine
surface mount technology (SMT)
Journal
IF:
9.9
Papers:
8.6K
Citations:
6.0W
Organization
View more organizations
Cited Papers
No cited papers available