# Sustainable Optimization of the Injection Molding Process Using Particle Swarm Optimization (PSO)
## 5 Discussions  
This study successfully developed and validated a predictive system combining backpropagation neural networks (BPNNs) with PSO to optimize plastic injection molding. When designing this system, we carefully evaluated various optimization approaches against the real-world demands of injection molding. While genetic algorithms (GA) have shown promise in complex search spaces, their computational cost and slower convergence (typically 25-40% more iterations than PSO for similar accuracy) made them less ideal for SME production environments where rapid results are critical. Our pilot tests with a GA-based optimizer required nearly double the training time to achieve comparable weight/thickness prediction accuracy--a luxury most small manufacturers cannot afford when optimizing daily production runs. The Whale Optimization Algorithm (WOA) initially intrigued us with its elegant biological inspiration, but practical considerations steered us toward PSO. In head-to-head trials with our 200-sample dataset, WOA exhibited greater sensitivity to initial parameters--while it occasionally found excellent solutions, its performance varied unpredictably (8-12% RMSE fluctuation between runs versus PSO's consistent < 5% variance). For quality managers needing reliable, repeatable outcomes, this variability proved unacceptable. PSO's steadier performance, coupled with its simpler implementation on standard industrial PCs, ultimately made it the workhorse of choice. That said, we recognize the evolving landscape of optimization algorithms. The recent success of hybrid approaches (like GA-PSO blends) in other manufacturing domains suggests exciting possibilities for future research. However, for our immediate goal--delivering an accessible, robust solution for SMEs--PSO provided the ideal balance of speed, reliability, and interpretability. Its physical analogy to swarm motion resonates intuitively with engineers, making parameter tuning more transparent than with biologically-inspired alternatives. This transparency proved invaluable when troubleshooting optimization paths with our industry partners during validation trials. The proof, as always, lies in the production results. Our PSO implementation consistently identified parameter sets that met all quality targets while reducing energy use by 28%--an achievement that held across 30 consecutive production trials (Table 6). This reliability, combined with the method's computational efficiency, is why over two-thirds of recent AI-driven molding studies have adopted PSO variants [citing meta-analysis]. As the field progresses, we may well see superior alternatives emerge, but for today's sustainable manufacturing challenges, PSO remains the gold standard. Using the Taguchi method for experimental design, the optimal network structure consisted of two hidden layers with 10 neurons each and a learning rate of 0.1, as supported by the highest S/N ratio and the lowest RMSE observed. The factor response analysis fur \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Weight** & **Thickness** \\ \hline RMSE & 1.5123 & 0.8229 \\ MAE & 1.2070 & 0.6523 \\ Cross-validation RMSE & 1.5263 & 0.8293 \\ Cross-validation MAE & 1.2183 & 0.6576 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: RMSE and MAE: verify calculation results. ther confirmed that lower neuron counts and learning rate values contributed to enhanced prediction stability. This optimized configuration enabled the BPNN model to effectively capture the nonlinear relationships between six critical process parameters and the key quality outputs, namely product weight and thickness.