Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method
By Changyu Shen, Lixia Wang, Qian Li. 2007
405 citations recorded by the source corpus.
Subfields: Automotive Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/54f412da8631e6bcbc29cfa1f9f5e7bc840254a3
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In Automotive Engineering
In Industrial and Manufacturing Engineering
In Mechanical Engineering
Citations in the corpus
- Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data (2017, background): “GAs solve linear and nonlinear problems through searching all areas of the state space and operating promising regions through crossover, mutation, intersection, and selection operations of individuals in the population (Shen et al. 2007).”
- A review of data mining applications for quality improvement in manufacturing industry (2011, methodology): “, 2000), to determine the optimal values of process parameters in plastic injection moulding (Kurtaran et al., 2005; Shen et al., 2007), to minimize warpage of thin shell plastic parts (Ozcelik & Erzurumlu, 2006), and to determine parameter settings in a silicon compound manu-”