사

Optimizing the Fiber-to-Yarn Production Process with a Combined Neural Network/Genetic Algorithm Approach

  • 출판일1997.09
  • 저자
  • 서지사항
  • 등록일 2016.11.02
  • 조회수 237
Yarn quality is an important aspect of fiber to yarn processing. The resulting yarn must possess optimal properties and minimal defects. The fiber to yarn process is very complex, and a mathematical function has not been developed to represent the entire process. Researchers used a neural network combined with a genetic algorithm to simulate and optimize the the fiber to yarn process. The neural network used machine settings and fiber quality parameters as input and yarn tenacity and elongation as output to model the process. The genetic algorithm optimized the input parameters for obtaining the best yarns. The multi-objective optimization required the genetic algorithm to be enforced with a sharing function and a Pareto optmization. Results indicate that simultaneous optimization can be achieved as a function of the necessary input parameters. 18 refs.
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