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injection location is changed to a new place as is shown in Fig. 8
labelled “alternative injection location.” The evaluation criterion
only uses uniform end-of-fill temperature, that is, only one crite-
ria construction variable is used as the criteria. Since the whole
region of variation is necessary, the variation of the Y coordinate
is also changed, from −20 to 20 mm.
With these modifications to the enhanced integration model,
there are now 16×11 = 176 iterations. The contour chart of the
new analysis results is shown in Fig. 10. It shows that in terms of
the criterion of uniform end-of-fill temperature distribution, the
hole feature should avoid being placed close to the injection lo-
cation, because in this area the objective function tends to have
higher values (0.8–1). The result also shows that the objective
Fig. 10. Contour chart of the new results when using new injection location
function is distributed roughly symmetrical over the line from in-
jection location to the furthest point of the region. Apart from
this, the distribution is not orderly. This reflects the complexity
of injection moulding process itself. The optimal hole location is
reported as:
• X coordinate = 30,
• Y coordinate = 20,
which is at the point farthest from the injection location.
There is no doubt that modification of part shape will affect
CAE analysis results, thus affect part mouldability and quality.
The above case study has demonstrated this point. However, the
injection moulding process is a rather complicated process. It is
in general rather difficult for the designers to predict the effect
of part-shape modification on the part mouldability and quality,
especially when multiple criteria are specified. Thus the tool de-
veloped for automatic shape modification would be useful for
part designers.
6 Conclusions
Existing computer tools, such as simulation packages, are not ca-
pable of direct improvement of injection-moulded-part design.
Although there have been some optimisation algorithms for in-
jection moulding design, these algorithms are not oriented to
part design, especially part-shape design and optimisation. It is
generally difficult to predict the effect of part shape on its mould-
ability and quality, hence trial-and-error shape design and mod-
ification is not effective, nor efficient. To address this problem,
we have described an enhanced CAD-CAE integration model,
which incorporates shape modification variables, criteria con-
struction variables, and mouldability and other quality measuring
criteria. By using this model, not only can CAE analysis infor-
mation be directly specified over the part CAD geometry model,
designers can also specify the feature to be modified and how it is
to be modified. Hence, the model supports automatic part-shape
modification by gradually modifying the part shape according
to the assigned values for the shape-modification variables, witheach shape variation analysed using the relevant CAE subrou-
tines. The iterative modification-analysis continues either until
the specified verification criteria are satisfied, or until all vari-
ations are examined and evaluated, with the optimal part shape
selected and reported.
The software implementation and case study has demon-
strated the usefulness of the presented work. Future research is
aimed at providing support for specification of different ranges
of shape modification variables, such that the feature position
variation may be over any region or area on the part geometry.
Research will also be carried out regarding the optimal route or
pattern of feature position variations so as to expedite the search