## 4 Results
### Summary  
Regarding comparisons with various algorithms, considering the actual needs and feasibility of the current situation, we will expand this section in future research to gain a deeper understanding of the advantages of various algorithms. First, this study used traditional BP for analysis, followed by comparison with BP-PSO to enhance the model's accuracy. The accuracy comparison of BP-PSO is shown in Figure 12. The findings indicate that the BP-PSO model is more suitable for predicting printing quality. This research indicates that the PSO-BP model for thermal barcode printing demonstrates significant advantages in parameter application within specific domains, as well as the practical viability of the algorithms in real-world scenarios. In terms of parameter application in specialized fields, the PSO-BP model exhibits the ability to adapt to dynamic thermal fluctuations in real time and is compatible with hardware suited for existing production environments. Furthermore, it shows robust convergence even with limited training data, a common characteristic in industrial settings. The model's capabilities minimize the response time, reduce the carbon footprint of production, and facilitate interpretable parameter adjustments, such as production debugging. This will enable the rapid establishment of an optimized printing environment. In terms of algorithmic feasibility, the PSO approach requires only an objective function (fractional values that reflect printing quality) and does not depend on precise mathematical models. This method facilitates the implementation of predictive conditions that are crucial for real-world industrial applications, especially in situations where accurately simulating thermal printing parameters is challenging. The penalty function of the model can directly incorporate maximum temperature and pressure limits, allowing for flexible constraint management without the need for reprocessing. Furthermore, the concurrent investigation of multiple parameter combinations by various particles can significantly reduce the time needed for experimental trials and lead to a faster convergence rate compared to sequential techniques, such as grid search. Following the training of the model and its subsequent validation in thermal printing, the PSO-BP model introduced in this study demonstrates significant applicability and practical importance. Consequently, this paper presents three key findings. 1. Rapid debugging and optimization settings. In the stage of production and commissioning for thermal printing, the PSO-BP model can efficiently determine the optimal combination of printing parameters. By inputting  
Figure 12: A comparison of the accuracy between BP and BP-PSO. various factors, such as printing speed, temperature, and concentration, the model can predict the resulting printing quality. This assists operators in quickly identifying parameter configurations that meet standards of quality, reducing debugging time, and enhancing overall production efficiency. Real-time observation and feedback. While thermal printers are in operation, the PSO-BP model can be employed to monitor print quality in real time. By consistently gathering data on printing parameters and print quality, the model can make real-time adjustments to these parameters to maintain high printing quality. Additionally, the model provides immediate feedback on printing quality, assisting operators in identifying and addressing potential quality issues. Quality control and assessment. The PSO-BP model is applicable in the quality control of thermal printers for assessing the quality of printed barcodes. By inputting data on various influencing factors, it can effectively predict the printing quality of the barcode and assign an appropriate quality rating. This enables production staff to quickly identify and resolve quality issues, thereby ensuring product reliability and consistency while enhancing customer satisfaction. Dynamic Parameter Optimization.