Optimization of Processing Parameters in Injection Molding Based on Adaptive Ant Colony Algorithm

Abstract:

An optimization method, integrating correlation degree, response surface method and ant colony algorithm, is proposed for exploring optimal parameters with the molding quality evaluation of warpage amount in injection molding. Initially, a novel formula calculating correlation degree is brought forth on the basis of the definition of distance and place value, and the parameters are chosen using the correlation degree method. Then the approximate model of the injection molding process is constructed by Kriging model with the determined parameters. Finally, the adaptive genetic algorithm and the ant colony algorithm are adopted to solve the optimization problem respectively, and injection molding tests are experimentally performed to validate the optimization results of parameters in injection molding. The experimental results demonstrate that the ant colony algorithm is superior to the genetic algorithm in solving the optimization problem for the low-dimensional design variables vector and the short coding length.

• Periodical: Advanced Materials Research (Volumes 179-180)
• Pages: 304-310
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.179-180.304
• Online since: January 2011
• Authors: Feng Li Huang, Shui Sheng Chen, Jin Mei Gu
• Keywords: Ant Colony Algorithm (ACA), Correlation Degree, Injection Molding, Kriging Model

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