A hybrid optimization algorithm for gate locations in the liquid composite molding process

• Published in: Textile research journal Vol. 92; no. 23-24; pp. 4912 - 4920
• Main Authors: Liu, Junling, Xie, Junbo, Chen, Li
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.12.2022; Sage Publications Ltd
• Subjects: Algorithms; anisotropy; fabrics; Gates; Genetic algorithms; Injection; Laminates; Liquid composite molding; liquids; Mathematical models; Numerical models; Optimization; Simulated annealing; Simulation; system optimization; textiles; Process optimization; liquid composite molding; simulated annealing; genetic algorithms
• ISSN: 0040-5175; 1746-7748; 1746-7748
• DOI: 10.1177/00405175221109625

It is costly to optimize the location of multiple injection gates through a trial and error-based method in the liquid composite molding, even though there are high fidelity physics-based numerical models. A hybrid optimization method called the Simulated Annealing Genetic Algorithm is proposed in this article, which uses the genetic algorithm to provide a global search for a predetermined time and then is further improved by the simulated annealing algorithm. The optimization results of multiple injection gates show that the number of convergence iterations using the Simulated Annealing Genetic Algorithm is less than that using the genetic algorithm, and the phenomenon becomes more obvious as the number of injection gates increases. The case shows that the Simulated Annealing Genetic Algorithm can solve the multiple injection gate configuration problems of highly anisotropic laminates without extra work. The optimization results are in good agreement with the experimental results.