facilities注塑模具英文翻译

2021年1月21日发(作者：乐爵士)

Minimizing

manufacturing

costs
for
thin
injection
molded

plastic components
1.
Introduction

In most industrial applications,
the manufacturing cost
of
a plastic part is
mainly
governed by the amount of material used in the molding process.

Thus, current approaches for plastic part design and manufacturing focus primarily
on establishing the minimum part thickness to reduce material usage.

The assumption is that designing the mold and molding processes to the minimum
thickness requirement should lead to the minimum manufacturing cost.

Nowadays,
electronic
products
such
as
mobile
phones
and
medical
devices
are
becoming ever more complex and their sizes are continually being reduced.

The demand for small and thin plastic components for miniaturization assembly has
considerably increased in recent years.
Other
factors
besides
minimal
material
usage
may
also
become
important
when
manufacturing thin plastic components.

In particular, for thin parts, the injection molding pressure may become significant
and has to be considered in the first phase of manufacturing.
Employing current design approaches for plastic parts will fail to produce the true
minimum manufacturing cost in these cases.

Thus, tackling thin plastic parts requires a new approach, alongside existing mold
design principles and molding techniques.

1.1
Current research

Today, computer-aided simulation software is essential for the design of plastic parts
and molds. Such software increases the efficiency of the design process by reducing
the design cost and lead time [1].

Major
systems,
such
as
Mold
Flow
and
C-Flow,
use
finite
element
analysis
to
simulate the filling phenomena, including flow patterns and filling sequences. Thus,
the
molding
conditions
can
be
predicted
and
validated,
so
that
early
design
modifications
can be
achieved. Although
available software is
capable
of analyzing
the flow conditions, and the stress and the temperature distribution conditions of the
component under various molding scenarios, they do not yield design parameters with
minimum manufacturing cost [2,3].

The output data of the software only give parameter value ranges for reference and
leaves
the
decision
making
to
the
component
designer.
Several
attempts
have
also
been made to optimize the parameters in feeding [4
–
7], cooling [2,8,9], and ejection

These attempts were based on maximizing the flow ability of molten material during
the molding process by using empirical relation ships between the product and mold
design parameters.

Some researchers have made efforts to improve plastic part quality by Reducing the
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sink mark [11] and the part deformation after molding [12], analyzing the effects of
wall thickness and the flow length of the part [13], and analyzing the internal structure
of
the
plastic
part
design
and
filling
materials
flows
of
the
mold
design
[14].
Reifschneider
[15]
has
compared
three
types
of
mold
filling
simulation
programs,
including Part Adviser, Fusion, and Insight, with actual experimental testing. All these
approaches have established methods that can save a lot of time and cost. However,
they
just
tackled
the
design
parameters
of
the
plastic
part
and
mold
individually
during the design stage. In addition, they did not provide the design parameters with
minimum manufacturing cost.


Studies
applying
various
artificial

intelligence
methods
and
techniques
have
been
found
that
mainly
focus
on
optimization
analysis
of
injection
molding
parameters
[16,17]. For in- stance He et al. [3] introduced a fuzzy- neuro approach for automatic
resetting
of
molding
process
parameters.
By
contrast
,
Helps
et
al.
[18,19]
adopted
artificial neural networks to predict the setting of molding conditions and plastic part
quality
control
in
molding.
Clearly,
the
development
of
comprehensive
molding
process
models
and
computer-aided
manufacturing
provides
a
basis
for
realizing
molding parameter optimization [3 , 16,17].

Mok et al. [20] propose a hybrid neural
network and genetic algorithm approach incorporating Case- Based Reasoning (CBR)
to derive initial settings for molding parameters for parts with similar design features
quickly
and
with
acceptable
accuracy.
Mok’s
approach
was
based
on
past
product
processing data, and was limited to designs that are similar to previous product data.
However,
no
real
R&D
effort
has
been
found
that
considers
minimizing
manufacturing costs for thin plastic components.


Generally,
the
current
practical
approach
for
minimizing
the
manufacturing
cost
of
plastic components is to minimize the thickness and the dimensions of the part at the
product design stage, and then to calculate the costs of the mold design and molding
process for the part accordingly, as shown in Fig. 1.
The current approach may not be able to obtain the real minimum manufacturing cost
when handling thin plastic components.
1.2Manufacturing
requirements


for
a
typical
thin
plastic
component

As
a
test
example, the typical manufacturing requirements for

a thin square plastic part with a
center hole, as shown in Fig. 2,

are given in Table 1.

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Fig.1. The current practical approach
Fig.2. Test example of a small
plastic component


Table1. Customer requirements for the example component

2
. The current practical approach

As
shown
in
Fig.1,
the
current
approach
consists
of
three
phases:
product
design,
mold design and molding process parameter setting. A main objective in the product
design is to establish the physical dimensions of the part such as its thickness, width
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