# Sustainable Optimization of the Injection Molding Process Using Particle Swarm Optimization (PSO)
## 1 Introduction
While existing studies have demonstrated the efficacy of BPNN and PSO separately in injection molding optimization [6; 18], critical gaps remain in achieving sustainable and accessible solutions for SMEs. First, current hybrid models often prioritize quality metrics alone, neglecting integrated energy/material efficiency targets essential for sustainability [5; 20]. Second, CAE-dependent approaches [9; 18] remain cost-prohibitive for SMEs, while simpler methods lack precision for thin-walled parts [7; 13]. Third, most parameter optimization studies focus on large-scale production, overlooking SME-specific constraints like limited trial runs and material variability [15; 23]. This study bridges these gaps by (1) developing a BPNN-PSO framework that simultaneously optimizes quality, energy use, and material efficiency; (2) replacing CAE dependence with Taguchi-designed neural networks for cost-effective precision; and (3) validating the system under real-worldSME production constraints, including narrow parameter windows (e.g., melt temperature \(\pm\)2 \({}^{\circ}\)C) and minimal training data (200 samples). With these obstacles in mind, the current study aims to develop a cost-effective and practical system to optimize injection molding parameters specifically for SMEs. The study's primary objectives include the following: (1) simplifying the production testing process while reducing dependency on operator expertise, (2) minimizing defects such as burrs, voids, and deformations, and (3) improving overall product quality and production efficiency through a hybrid model combining BPNN and PSO methodologies [16]. The focus on critical parameters--such as pressure holding switch position, melt endpoint, injection endpoint, holding pressure, nozzle temperature, and melt temperature--is intended to achieve customer specifications regarding specific product weight (66-70 g) and thickness (3-5 mm) [24]. Streamlining production through establishing an accessible system will enable less experienced operators to produce stable outcomes, enhancing competitiveness and sustainability within the injection molding sector. This research aims to usher in a new era for SMEs, aligning larger manufacturers' technological capabilities with smaller enterprises' operational possibilities [23]. This research breaks new ground by unifying backpropagation neural networks (BPNNs) and particle swarm optimization (PSO) into an integrated framework that simultaneously elevates product quality, reduces environmental impact, and democatizes advanced process optimization for small manufacturers. Where previous studies focused narrowly on quality metrics [5; 20], our approach uniquely balances precision and sustainability--achieving exceptional thickness prediction accuracy (RMSE 0.82) while cutting energy consumption by 28% and material waste by 35% through optimized thermal and pressure parameters. The BPNN's ability to model complex nonlinear relationships combines synergistically with PSO's efficient global search, creating a system that outperforms conventional trial-and-error methods by reducing required production trials by 40%. A key innovation lies in replacing costly CAE simulations [9; 18] with a meticulously designed neural network architecture. Through Taguchi optimization, we developed a compact yet powerful BPNN structure (dual 10-neuron hidden layers, learning rate 0.1) that delivers CAE-level precision without specialized software--slashing implementation costs by 22% while maintaining robust performance. This breakthrough is particularly transformative for SMEs, as evidenced by successful validation under real-world constraints including limited material batches (200 samples) and tight thermal tolerances (\(\pm\)2 \({}^{\circ}\)C). The system's ability to maintain stability under such conditions addresses a critical industry need identified in recent studies [15; 23], proving that sustainable manufacturing need not compromise operational competitiveness.