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

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Area of Science:

  • Industrial Manufacturing
  • Textile Engineering
  • Artificial Intelligence

Background:

  • Process optimization is crucial for industrial efficiency, especially in textiles.
  • Traditional methods like Grey Relational Analysis (GRA) and Response Surface Methodology (RSM) have limitations in complex optimization tasks.
  • Developing advanced techniques is necessary to improve yarn production quality and operational efficiency.

Purpose of the Study:

  • To compare AI-based methods with GRA for optimizing speed frame parameters in 100% polyester spun yarn production.
  • To evaluate the prediction accuracy of Artificial Neural Network (ANN) models against RSM.
  • To assess the effectiveness of the Genetic Algorithm (GA) for multi-criteria optimization in textile manufacturing.

Main Methods:

  • Response Surface Methodology (RSM) for experimental design.
  • Grey Relational Analysis (GRA) to calculate Grey Relational Grade (GRG) based on yarn evenness (CVm%) and imperfection index (IPI).
  • Artificial Neural Network (ANN) for predictive modeling and Genetic Algorithm (GA) for optimization.

Main Results:

  • The ANN model demonstrated superior prediction accuracy (R² ≈ 1) compared to the RSM model (R² ≈ 0.65).
  • GA-based optimization yielded better results than GRA, leading to improved yarn quality.
  • Optimal parameters identified: twist = 23 TPM, break draft = 1.26, spacer size = 5.1 mm, overhang = 3.5 mm.

Conclusions:

  • AI-based methods, particularly ANN and GA, offer superior performance and reliability for optimizing textile manufacturing processes.
  • These findings suggest a shift towards intelligent optimization techniques for enhanced industrial efficiency and product quality.
  • The study validates the potential of AI in advancing the textile industry through data-driven process improvements.