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Teaching-learning-based optimization of ring and rotor spinning processes

Abstract

In a textile mill, ring and rotor spinning processes are the main technologies employed to convert cotton fibres into yarns with desirable qualities. It has been observed that in order to produce yarns having satisfactory quality characteristics, those spinning processes need to be run while setting different input variables (control parameters) at their optimal operating levels. In this paper, the optimal parametric combinations of back and front zone variables in a ring frame, and input variables in a rotor spinning process are determined based on teaching-learning-based optimization algorithm. The optimization performance of this algorithm is also compared with four other techniques, e.g., firefly algorithm, differential evolution algorithm, cuckoo search algorithm and quantum particle swarm optimization algorithm with respect to accuracy and consistency of the derived solutions. This optimization technique can thus be effectively applied to any of the intermediate processes in a textile mill to obtain the best combinations of different input variables so as to achieve the target quality characteristics.

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Correspondence to Shankar Chakraborty.

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Diyaley, S., Chakraborty, S. Teaching-learning-based optimization of ring and rotor spinning processes. Soft Comput 25, 10287–10307 (2021). https://doi.org/10.1007/s00500-021-05990-0

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  • DOI: https://doi.org/10.1007/s00500-021-05990-0

Keywords

  • Ring spinning
  • Rotor spinning
  • Optimization
  • Teaching-learning-based optimization
  • Yarn
  • Quality