Multi-Objective Optimization of Injection-Molded Car Door Handles Using Taguchi Method and Grey Relational Analysis

Injection-molded automotive components, such as exterior car door handles, often suffer from defects like volumetric shrinkage, residual stress, and deflection, impacting their structural integrity and aesthetic quality. Traditional optimization methods struggle to balance the multiple conflicting objectives involved in material selection, mold design, and process parameters. This study addresses these challenges by integrating the Taguchi method and Grey Relational Analysis (GRA) for multi-objective optimization in Moldflow simulations. Key process parameters, including melt temperature, injection pressure, and cooling time, were systematically analyzed and optimized to reduce defects. Simulation trials using Acrylonitrile Butadiene Styrene (ABS), Polypropylene (PP), and Polycarbonate (PC) demonstrated notable improvements, achieving a 56.4% reduction in weld-line width and a 68.9% decrease in sink-mark depth. These results highlight the effectiveness of the combined Taguchi-GRA approach in enhancing injection molding efficiency, improving product quality, and ensuring component longevity. The study establishes a robust foundation for further research on optimizing complex injection-molded automotive parts and exploring advanced material formulations to meet stringent industry standards.