(a) (b)
Figure 7。 The values of responses at the initial values: (a) clamping force diagram, (b) warpage value。
Table 3。 Coefficients of responses。 Table 5。 ANOVA table for clamping force。
Source SQ MS F P
Model 1。497 × 107 7。486 × 105 396。31 < 0。0001
A 13292。36 13292。36 7。04 0。0137
B 4。449 × 105 4。449 × 105 235。53 < 0。0001
C 14975。6 14975。6 7。65 0。0115
D 1。211 × 107 1。211 × 107 6408。93 < 0。0001
BD 14360。43 14360。43 7。6 0。0107
A2 18064。49 18064。49 9。56 0。0048
B2 1。019 × 106 1。019 × 106 539。29 < 0。0001
C2 13849。4 13849。4 7。33 0。012
D2 7。072 × 105 7。072 × 105 374。43 < 0。0001
Residual 47219。81 1888。79
Total 1。502 × 107
melt temperature, packing time, packing pressure and cooling time, respectively。 Approximate equations of two responses are presented as Eqs。 (3) and (4), respectively。 Table 3 de- scribes values for coefficients of equations as determined by regression method。
shown in the Table 2。
According to simulation results, regression response sur- face models for the two objective functions of evaluating clamping force and warpage are derived。 Clamping force and
warpage initial values are shown in the Figure 7。
Second-order polynomial regression is employed to estab-
lish non-linear relationships among design variables and res- ponses。 The responses are functions of mold temperature,
Table 4。 ANOVA table for warpage。
Based on computational cost and time-of simulation, as
compared with the MoldFlow, the developed predictive model is a much simpler and more efficient in predicting outputs with change-of-design variables。 The adequacy of the developed models, including warpage and clamping force via ANOVA analysis with sums of squares (SQ), mean squares (MS), F-value (F), P-value (P) is shown in table 4 and table 5, respectively。 The backward process eliminated the insignifi- cant terms to adjust the quadratic models。 汽车挡泥板注塑成型中能源效率英文文献和中文翻译(5):http://www.youerw.com/fanyi/lunwen_86764.html