Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm
By B. Ozcelik, Tuncay Erzurumlu. 2006
379 citations recorded by the source corpus.
Subfields: Automotive Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/40b2da9642dc0d2ccfe219c1cd9fa17a037b0002
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
- Manuel Lozano
- Daniel Molina
- Francisco Herrera
- Satoshi Kitayama
- Salvador Garcia
- Shuji Aiba
- Masahiro Takano
- Hiroyasu Miyakawa
In Mechanical Engineering
- Manuel Lozano
- Daniel Molina
- Francisco Herrera
- Satoshi Kitayama
- Salvador Garcia
- Shuji Aiba
- Masahiro Takano
- Hiroyasu Miyakawa
Citations in the corpus
- A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization (2009, methodology): “When we find statistical studies , they are usually based on the average and variance by using parametric tests (ANOVA, t-test, etc. . . .) (Czarn et al. 2004; Ozcelik and Erzurumlu 2006; Rojas et al. 2002).”
- Multi-objective optimization of injection molding process parameters for short cycle time and warpage reduction using conformal cooling channel (2016, methodology): “Ozcelik and Erzurumlu optimized the process parameters in the PIM for warpage minimization [7], in which the critical process parameters were firstly selected by analysis of variance (ANOVA).”