Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods
By M. Altan. 2010
280 citations recorded by the source corpus.
Subfields: Industrial and Manufacturing Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/032b4efe044595afe4c16c1798a017f7f314c25a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Industrial and Manufacturing Engineering
In Mechanical Engineering
Citations in the corpus
- Back Propagation neural network modeling for warpage prediction and optimization of plastic products during injection molding (2011, background): “They suggested that neural network is an efficient predictive tool for shrinkage [21].”
- Parameter Study in Plastic Injection Molding Process using Statistical Methods and IWO Algorithm (2011, methodology): “ANOVA RESULTS FOR POLYSTYRENE Source Degree of Freedom (DF i ) Sum of Square (SS i ) Mean Square (MS i ) F Value P value T 2 1.92948 0.96474 * 18.27 0.000 P i 2 0.16539 0.08270 1.57 0.236 P p 2 1.35027 0.67513 * 12.78 0.000 t p 2 0.40681 0.20341 * 3.85 0.041 Error 18 0.95057 0.05281 Total 26 4 .8025”