Optimization of injection molding parameters for bending and tensile strength of ABS and ABS/carbon fiber composites using Taguchi–Grey correlation analysis
Abstract
This study investigates the effects of injection molding parameters on the flexural and tensile strengths of neat acrylonitrile-butadiene-styrene (ABS) and woven carbon fiber fabric-reinforced ABS composites (ABS/CF) fabricated by a two-step insert injection molding process. A Taguchi L25 orthogonal experimental design was employed with five processing parameters: filling pressure (104-118 MPa), packing pressure (96-100 MPa), filling time (1-3 s), packing time (0.5-2.5 s), and melt temperature (228-232 °C). The results show that flexural strength increased from 5.86-6.49 MPa (ABS) to 5.76-6.93 MPa (ABS/CF), while tensile strength increased from 33.35-35.17 MPa to 38.20-41.27 MPa, corresponding to improvements of approximately 7% and 17-18%, respectively. ANOVA results indicate that filling pressure is the most significant factor, with p-values of 0.012 and 0.039 for flexural performance and a maximum contribution of 47.08% for tensile strength. The optimal parameter combination identified by Taguchi-Grey analysis yields Grey relational grades of 0.725 and 0.728 for flexural and tensile strength, respectively. Confirmation experiments show good agreement with predicted values, with deviations of 1.59% (flexural) and 2.98% (tensile). Overall, this study provides a comprehensive understanding of the process-structure-property relationships in injection-molded ABS/CF composites and demonstrates that the Taguchi-Grey approach is an effective method for simultaneous optimization of multiple mechanical properties. The findings offer practical guidelines for improving the mechanical performance of fiber-reinforced thermoplastic composites in thin-walled and structural applications.
Fields of Study & Subjects
Injection Molding Process and Properties
Polymer Foaming and Composites
Epoxy Resin Curing Processes
Publication details
- Journal / source
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Volume
- 21
- Issue
- 7
- Pages
- e0352647–e0352647
- Publication date
- 15 July 2026
- Language
- EN