# Sustainable Optimization of the Injection Molding Process Using Particle Swarm Optimization (PSO)
## 1 Introduction
These machine learning applications are starting to redefine industry norms, illustrating the potential to adapt traditional methods to meet contemporary demands for accuracy and reliability [10]. The six optimized parameters were selected based on their documented dominance in controlling part quality and process efficiency in injection molding. The pressure-holding switch position (20.99 mm) governs the transition from injection to packing phase, with studies showing it directly impacts residual stressesand dimensional stability [5; 11; 12]. Optimal positioning reduces warpage by 18-22% in polypropylene parts while minimizing energy waste during phase transitions [13]. For material preparation, the melt endpoint (96.19 mm) ensures consistent plasticization, with research indicating it maintains melt temperature homogeneity within \(\pm\)2 \({}^{\circ}\)C--critical for avoiding flow-related defects [14]. The injection endpoint (21.75 mm) complements this by controlling final fill position, preventing both material waste from over-packing and surface defects from under-packing, accounting for 31% of weight variation in design of experiments (DOEs) studies [6; 12; 15]. Thermal parameters demonstrate particularly strong quality linkages. The nozzle temperature (224.7 \({}^{\circ}\)C) balances flow consistency against degradation risks, where \(\pm\)5 \({}^{\circ}\)C variations alter PP3040 viscosity by 12% [16; 17]. Similarly, the melt temperature (234.8 \({}^{\circ}\)C) represents a critical compromise--below 230 \({}^{\circ}\)C increases injection pressure requirements by 25% [18], while exceeding 240 \({}^{\circ}\)C risks molecular weight loss \(>\) 8% due to thermal degradation [19]. Holding pressure (39.8 bar) completes the parameter set by compensating for shrinkage during cooling. Recent work confirms that pressures above 50 bar increase energy use by 15% without quality benefits [20], while optimized settings improve thin-walled part density by 9-12% [7; 13]. This systematic parameter selection, validated through preliminary screening experiments (\(p<\) 0.01), ensures comprehensive control over flow dynamics, thermal stability, and packing efficiency--addressing both quality specifications and sustainability objectives through reduced energy and material waste. The inherent strength of injection molding lies in its suitability for mass production. However, achieving optimal conditions for molding remains contingent on extensive testing, which can be time-consuming and labor-intensive while also depending heavily on operator expertise [11; 12]. The high interdependence of process parameters complicates matters; it is widely recognized that minor adjustments in one variable can generate significant repercussions downstream. Consequently, time-intensive trial-and-error methods often lead to elevated operational costs and diminished production efficiency. Data-driven optimization strategies have emerged as promising alternatives, which can minimize defects and stabilize production through a more calculated approach [19; 21]. For example, integrating statistical designs with computational models, such as the Taguchi method, has successfully addressed production issues like warpage [11; 13]. Computer-aided engineering (CAE) tools, such as mold flow analysis software, have become integral in determining initial process parameters for injection molding. However, these tools come with high costs and necessitate specialized training, which can restrict small and medium-sized enterprises (SMEs) from fully taking advantage of their potential benefits [18; 20]. Given the financial and resource constraints within many SMEs, there's an urgent need for practical and cost-effective methods to streamline the optimization of injection molding parameters. Such advancements are essential for enhancing product quality, operational efficiency, and competitiveness within the injection molding industry [15; 22].