# Sustainable Optimization of the Injection Molding Process Using Particle Swarm Optimization (PSO)
## 1 Introduction  
The injection molding industry faces growing pressure to adopt sustainable practices, not only to meet regulatory requirements but also to align with global environmental goalssuch as reduced carbon emissions and resource conservation [1; 2]. Traditional optimization efforts often prioritize production efficiency and cost reduction, but sustainable optimization extends these objectives to include minimizing energy consumption, reducing material waste, and lowering the carbon footprint of manufacturing processes [3; 4]. For instance, improper parameter settings can lead to excessive energy use during heating and cooling cycles or result in defective parts that contribute to material waste [5; 6]. By integrating data-driven optimization techniques, such as the proposed BPNN-PSO framework, manufacturers can achieve a dual benefit: enhancing product quality while simultaneously reducing environmental impact [7; 8]. This approach aligns with the principles of green manufacturing, where process efficiency directly translates to sustainability gains [9; 10]. Small and medium-sized enterprises (SMEs) play a critical role in the global manufacturing landscape but often lack the resources to implement advanced sustainability initiatives. The high costs of Computer-aided engineering (CAE) tools and specialized training can deter SMEs from adopting energy-efficient practices or waste-reduction technologies. The proposed BPNN-PSO system addresses this gap by offering a cost-effective solution that optimizes process parameters without requiring expensive software or extensive expertise. By reducing trial-and-error testing and minimizing defective rates, the system not only improves production efficiency but also conserves raw materials and energy. This democratization of sustainable optimization tools empowers SMEs to compete in markets increasingly driven by environmental accountability, fostering broader adoption of eco-friendly manufacturing practices across the industry. Plastic materials are fundamental components within modern manufacturing and everyday consumer products, thanks to their diverse applications ranging from packaging solutions to complex electronic devices like smartphones and laptops. This versatility can be attributed to several intrinsic properties, including their lightweight nature, low density, remarkable tensile strength, and excellent electrical and chemical insulation characteristics. These properties make plastics highly sought after in electronics, automotive, and electrical equipment [1; 2]. Notably, the evolution of injection molding technology has further consolidated the essential role of plastics in manufacturing processes due to the technique's capacity to produce high-quality products efficiently [3; 4]. Despite these advantages, current studies indicate that the injection molding process faces challenges in optimizing process parameters. Incorrect parameter settings can lead to inconsistent product quality and heightened energy costs during production. For example, recent research by [5] demonstrated a novel hybrid optimization method integrating a one-dimensional Convolutional Neural Network (1D-CNN) with the NSGA-II algorithm, reducing product deformation significantly [5]. The injection molding sector, traditionally viewed as less demanding in quality than high-tech industries, is shifting due to evolving market expectations for enhanced precision and quality, particularly in components destined for electronic devices ([PERSON] et al.) [6; 7]. In response to these increased demands, machine learning-based techniques have become efficient tools for mapping intricate parameter relationships and elevating product quality across injection-molded components. For instance, a study by [PERSON] et al. [6] successfully combined the Taguchi method with particle swarm optimization (PSO) to optimize injection molding processes, resulting in noteworthy improvements in both product quality and productivity [8; 9].