# Sustainable Optimization of the Injection Molding Process Using Particle Swarm Optimization (PSO)
## 7 Future Works  
To further advance the sustainability potential of this framework, several promising research directions emerge. First, integrating lifecycle assessment (LCA) methodologies would enable a comprehensive evaluation of environmental impacts across the entire production chain--from raw material extraction to end-of-product life--providing actionable insights for reducing the carbon footprint of injection-molded components. Second, embedding real-time energy monitoring systems could create dynamic feedback loops, allowing the optimization algorithm to continuously adapt parameters for minimal energy use while maintaining quality standards. Most critically, future work should pursue multi-objective optimization architectures that simultaneously balance the triad of manufacturing priorities: product quality, operational cost, and ecological impact. Such an approach would align with emerging Industry 5.0 paradigms, where environmental stewardship and economic viability are equally weighted. By pioneering these data-driven sustainability initiatives, the injection molding sector can position itself at the forefront of green manufacturing--meeting both the stringent demands of global climate accords and the evolving expectations of eco-conscious markets. Building on the promising results of this study, several avenues for future research are recommended to further enhance the practical value and sustainability impact of the proposed BPNN-PSO framework. First, extending the current single-objective optimization to a multi-objective or multi-criteria approach would enable simultaneous optimization of product quality, energy efficiency, material usage, and production costs, providing a more comprehensive trade-off analysis for sustainable manufacturing. In response to the reviewers' suggestions, we also plan to develop a multi-objective optimization version that simultaneously balances production cost, product quality, and environmental metrics, further enhancing the framework's practical relevance and novelty. Second, stronger comparative benchmarking could further demonstrate the value of the proposed method. We plan to conduct additional experiments comparing our BPNN-PSO framework with other advanced algorithms (e.g., genetic algorithms, NSGA-II) on the same dataset to highlight differences in prediction accuracy, optimization speed, and sustainability impact. Third, integrating real-time adaptive control through advanced process monitoring or digital twin technologies could allow the framework to dynamically adjust process parameters based on live sensor feedback, improving robustness under varying production conditions. Additionally, future studies could investigate the application of the proposed system to more complex part geometries and multi-cavity molds, which present additional challenges for consistent quality and resource efficiency. Exploring more advanced or hybrid metaheuristic algorithms--such as combining PSO with genetic algorithms, differential evolution, or other nature-inspired methods--could also yield better convergence speed and solution quality for complex, high-dimensional parameter spaces. Incorporating lifecycle assessment (LCA) metrics into the optimization process would further strengthen the alignment with circular economy principles by quantifying environmental impacts across the entire product lifecycle. Finally, developing approaches that enable model transferability, such as transfer learning or federated learning across multiple SMEs, could enhance the scalability and practical deployment of this system in diverse production settings. These directions would collectively contribute to more intelligent, adaptive, and sustainable injection molding practices in line with Industry 5.0 aspirations. Conceptualization, Y.-T.J.; Validation, Y.-T.J.; Formal analysis, R.M.S. ; Resources, H.-L.C. ; Writing--original draft, R.M.S. ; Project administration, H.-L.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.