Multiobjective Optimization Method for Polymer Injection Molding Based on a Genetic Algorithm

Table 8

Fitting results of the sink marks using the four regression models.

	R-sq	R-sq (adjustment)	R-sq (prediction)	regression; model
Quadratic	97.51%	93.00%	37.95%	Sink mark1
Linear + parabolic	97.26%	95.96%	87.72%	Sink mark2
Linear + interaction	94.61%	89.56%	34.85%	Sink mark3
Linear	94.36%	93.28%	91.02%	Sink mark4

Annotation: R-sq was the goodness coefficient in regression fitting models.
Results for the four regression models:
Sink mark1 = 0.016148- 0.000292A + 0.000042B + 0.003042C- 0.00004 D - 0.000625E + 0.000227A×A-0.000273B×B + 0.000102C×C-0.000148 D×D-0.000273E×E + 0.000063A×B-0.000062A×C + 0.000062A×D + 0.000062A×E -0.000063B×C + 0.000063B×D + 0.000063B×E-0.000063C×D-0.000063C×E + 0.000063D×E.
Sink mark2 = 0.016148 - 0.000292A + 0.000042 B + 0.003042C- 0.000042D- 0.000625E + 0.000227A×A-0.000273B×B + 0.000102C×C- 0.000148 D×D - 0.000273E×E.
Sink mark3 = 0.015875-0.000292A + 0.000042B + 0.003042C- 0.000042D - 0.000625E + 0.000063A×B - 0.000062A×C+0.000062A×D + 0.000063A×E-0.000063B×C + 0.000063B×D + 0.000063B×E-0.000063C×D- 0.000063C×E + 0.000063D×E.
Sink mark4 = 0.015875-0.000292A + 0.000042B + 0.003042C - 0.000042D - 0.000625 E.