Enhanced simulated annealing for laser cutting path optimization in photovoltaic cell manufacturing

Addressing the engineering challenges of path planning efficiency and precision in solar cell laser cutting, this study proposes an enhanced simulated annealing algorithm (QSA). By modifying the acceptance probability model of traditional simulated annealing through the introduction of quantum tunneling effects, the algorithm achieves coordinated optimization of total cutting length and idle travel distance in two-dimensional straight-line cutting scenarios. Experimental results demonstrate that, compared to conventional simulated annealing, the proposed algorithm reduces the objective function value by 8.3% and improves convergence speed by 40% in the scenario of segmenting 156mm×156mm cells into 4×4 subunits. The algorithm operates efficiently on standard computing hardware without requiring quantum hardware support, providing a low-cost optimization solution for automated solar cell production lines. This research focuses on the application of the enhanced simulated annealing algorithm in optimizing laser cutting paths for solar cells, detailing the algorithm's design principles and problem modeling process. Through comparative experiments with traditional simulated annealing, we validate its advantages in optimization effectiveness and engineering applications, aiming to provide the photovoltaic industry with an efficient, low-cost cutting path optimization solution that promotes the advancement of automated solar cell cutting technology.