Multi-objective optimization of speed frame parameters for polyester spun yarn using artificial intelligence and grey relational analysis.

ABSTRACT: Systematic approaches to process optimization are essential for enhancing operational efficiency in industrial manufacturing, particularly in the textile sector. This study investigates and compares the effectiveness of Artificial Intelligence (AI) based modeling and optimization techniques with Grey Relational Analysis (GRA) in determining the optimal operational parameters of the speed frame for producing 100% polyester spun yarn. The key parameters analyzed include twist, break draft, spacer size, and overhang, with Response Surface Methodology (RSM) employed to design the experiments and ensure sufficient variability in the input space. Yarn quality was evaluated using the Grey Relational Grade (GRG), calculated through GRA based on two critical response variables: yarn evenness (CVm%)

SUBMITTER: Savadroodbari HA 

PROVIDER: S-EPMC12575616 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature