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Inverse input optimization for tuning two-ply yarn processing parameters using feedforward neural network.

Habib Amiri Savadroodbari1, Mohsen Rezahasani2, Mohammad Javad Abghary2

  • 1Department of Textile Engineering, Amirkabir University of Technology, Tehran, Iran. habib.amiri1375@gmail.com.

Scientific Reports
|June 23, 2026
PubMed
Summary

Inverse Input Optimization (IIO) efficiently tunes industrial processes, like cotton yarn production, by using an artificial neural network (ANN) to optimize yarn parameters for increased tenacity. This novel approach proves more computationally efficient than Genetic Algorithms.

Keywords:
Feedforward neural networkInverse input OptimizationNumerical gradientsTensile strengthTwo-ply yarn

Related Experiment Videos

Area of Science:

  • Textile Engineering
  • Artificial Intelligence
  • Process Optimization

Background:

  • Industrial processes require efficient optimization techniques to enhance product quality and reduce costs.
  • Artificial Neural Networks (ANNs) can model complex nonlinear relationships in process parameters.
  • Traditional optimization methods may be computationally intensive.

Purpose of the Study:

  • To introduce and evaluate Inverse Input Optimization (IIO) as a novel method for tuning industrial processes.
  • To optimize two-ply cotton yarn parameters for improved tenacity using an ANN-guided approach.
  • To compare the efficiency of IIO with established optimization algorithms like Genetic Algorithm (GA).

Main Methods:

  • Trained a feedforward ANN with experimental data to map yarn process parameters to tenacity.
  • Employed the IIO framework to iteratively adjust input parameters based on ANN predictions and target tenacity.
  • Utilized numerical gradient estimation, adaptive update rates, and momentum for optimization adjustments.
  • Performed sensitivity analysis using the Garson Weight Method (GWM) to identify influential parameters.

Main Results:

  • The twist direction of two-ply yarn was identified as the most significant factor influencing tenacity (32.61%).
  • IIO optimized yarn parameters (1000 TPM first/second-ply twist, 790 TPM two-ply twist, Z-twist) increased tenacity from 28.72 cN/tex to 35.62 cN/tex.
  • IIO achieved comparable results to GA but required significantly fewer function evaluations (271 vs. 1020).

Conclusions:

  • Inverse Input Optimization (IIO) is a simple, efficient, and cost-effective strategy for industrial process optimization.
  • IIO offers a competitive alternative to traditional methods like GA, particularly in terms of computational efficiency.
  • The study demonstrates the successful application of IIO in enhancing cotton yarn tenacity through optimized process parameters.