Useful Process Data from the Injection Molding

Transcription

INJECTION MOLDING
The wide variety of demands during injection
molding requires a detailed process characterization and precise process control
Useful Process Data
from the Injection
Molding Machine
Material Characterization. Hand-in-hand, research and industry are developing
new avenues for injection molding-specific process control. If the relevant process
data are collected and evaluated at the injection molding machine according to
the requirements, changes in the process can be detected online. At batch, color or
residual moisture changes, the operator immediately receives information on
fluctuations in process and quality and can intervene directly.
FELIX HEINZLER ET AL.
he production of high-quality injection moldings demands precise
monitoring of the production
process. It is not unusual for an initially
T
Translated from Kunststoffe 2/2014, pp. 52–56
Article as PDF-File at www.kunststoffeinternational.com; Document Number: PE111601
42
stable process to become unbalanced due
to external factors – and bad parts are produced. Changes in input conditions also
necessitate adjustments to the process settings.
The machine operator has a wide variety of parameters at his disposal with
which he can monitor the injection molding process. Chart plotters and trend
curves visualize changes and allow the
production history to be viewed. But in
order to be able to comprehensively analyze the injection molding process, tools
are required which allow a deeper insight
into the process.
KraussMaffei Technologies GmbH,
Munich, Germany, is working actively on
this topic and cooperates here with research institutes such as the Faculty for
Engineering Design and Plastics Machin-
© Carl Hanser Verlag, Munich
Kunststoffe international 2/2014
Internet-PDF-Datei. Diese PDF Datei enthält das Recht zur unbeschränkten Intranet- und Internetnutzung, sowie zur
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INJECTION MOLDING
ery at the Institute for Product Engineering (IPE) of the University of DuisburgEssen, Germany, in order to further develop the state of the art. Within the
framework of this cooperation, for example, new approaches to injection
molding-specific process control are developed and tested in practice. Examples
of this are the inline detection of the
residual moisture in hydrophilic plastics
among the engineering thermoplastics
and a general monitoring of the melt
state in order to boost the process and
product quality.
Fig. 1. In the control system of injection molding
machines (here
MC6, KraussMaffei) the trend
and current values for process
parameters can
be visualized on
chart plotters
and trend curves
Indicators Describe the
Injection Molding Process
Process parameters such as forces at the
screw shaft, mold cavity pressure, temperatures, screw position or injection speed
can be easily visualized using chart plotters. Indicators derived from these, such
as maximum melt or mold cavity pressure, plasticizing time or mean cylinder
and mold temperatures are recalculated
for each injection molding cycles and displayed (Fig. 1). In this way, even creeping
changes can be detected from the observation of the historical development of
the different parameters. The mold cavity pressure, in particular, is a parameter
frequently used for improving the process
control and automated, model-based
quality control [1–3].
The increasing integration of the sensors installed in injection molding production cells allows vast amounts of
measurement data to be recorded that can
be analyzed with respect to the process.
Standard machines, for example, have a
data link to the frequency converters of
11 g/10 min
20
22
(Fig. 2).
>
100 min-1
175 min-1
250 min-1
25.14
25.25
25.26
25.16
25.34
25.44
25.29
25.03
Viscosity index
24.66
28
Dwell time
25
24
20.38
20.60
21.03
24
25.35
29.71
24.91
25.41
Viscosity index
Pa·s
28
22
and is made representable and evaluable
i.a. by indicators such as a viscosity index.
The viscosity index is formed by integration of the melt pressure over the injection time, rather like a flow index. The
integration limits have to be modified according to the closing behavior of the
non-return valve (Fig. 2).
The plasticizing phase is crucial for the
processing and the initial melt state before injection. Until now the plasticizing
time has been available for evaluation of
the plasticizing process. Indicators such
as the mean screw torque during plasticizing or the plasticizing energy applied
via the screw (as the product of motor
torque and screw speed) offer further
possibilities for gaining detailed information about the process. Initial trials show
that the flow properties during the injection phase can already be influenced here
26
Pa·s
26
Plasticating speed:
19 g/10 min
30.35
32
6 g/10 min
30.40
MFI:
the drives, to the charge amplifiers of temperature and mold cavity pressure sensors or even to electronic water supply
systems. Torque and pressure curves, but
also flow rates and flow temperatures, can
thus be continuously recorded and correlated with the process and product
quality [4]. In order to be able to identify the right indicators for the various demands in the production environment,
KraussMaffei and the Institute for Product Engineering have carried out extensive series of trials with different molds,
raw materials and machines.
A parameter combination as a control
variable which signals a very wide variety
of effects on the machine is the melt state
[5]. This describes the “quality” of the
plastic melt (pressure, temperature, viscosity) under the prevailing conditions.
The information is collected during the
process phases injection and plasticizing
s
34
23
30
65
bar
Back pressure
100
© Kunststoffe
Fig. 2. The indicator used for a material comparison (Sabic PP 571, 575, 576) is the viscosity index during injection. Parameters such as back pressure,
plasticating speed or dwell time have little influence on the material used. A change in the MFI in the otherwise identical material, on the other hand,
can be clearly identified
www.kunststoffe-international.com
43
INJECTION MOLDING
s
PP 1
Mold wall temperature
°C
75
35
mm
mm
25
300
25
15
55
1.7
10
5
1.8
1.9
2.0
Time
2.1
2.2
s
s
bar
20
60
PP 3
400
70
65
PP 2
35
250
20
200
15
150
10
100
5
50
0
2.3
0
1.5
Screw position
PP 3
Melt pressure
PP 2
Screw position
PP 1
85
1.7
1.9
2.1
Time
2.3
2.5
s
0
2.7
© Kunststoffe
Demands on Moldings with
High Surface Quality
If the ambient conditions or the properties of the raw material change during
production, the molding quality changes
at the same time (with identical machine
settings) [7]. For example, the moldings
are over-filled or under-filled, or warpage
or surface flaws occur. When evaluating
the indicators, the whole system of ma-
44
12.0
Nm
11.5
11.0
10.5
10.0
9.5
9.0
700
bar
690
Molding weight
Plastication torque
Changeover melt pressure
680
7.65
g
7.60
7.55
670
7.50
660
7.45
650
7.40
640
630
Molding weight
The dynamic injection phase is very well
suited to deriving indicators which describe the quality of the metered raw material. If we assume constant conditions
in the mold, then the effects of changes in
the raw material can be measured very
easily. A proven method of monitoring is
the calculation of a flow index as a pressure integral over a defined section of the
screw stroke [4]. The influence of the viscosity on the process parameters during
the injection phase can be seen from this
example of three polypropylene grades
with different viscosities (Fig. 3).
Changes in the raw material (charge or
color changes), however, often also lead
to changes in the closing behavior of the
non-return valve, so that the injection
pressure curve is shifted parallel to and
along the screw stroke [6]. A flow index
formed over a fixed distance would interpret this effect as a change in the flowability of the raw material. The effect of
changes in viscosity on the part weight
can be very well illustrated in the trial with
one raw material and different admixed
color pigments (Fig. 4).
Mean torque during plastication
Effects of Raw Material Changes
during the Injection Phase
Changeover melt pressure
Fig. 3. The example of the three tested polypropylene grades with different MFI shows the influence on the flow behavior in the cavity and on the closing
behavior of the non-return valve. The temperature curve (left) and melt pressure curve (right), measured using a temperature sensor in the mold cavity
wall and a force sensor behind the screw show the influences on the process
7.35
Sabic PP white golden
575
blue
green brown yellow orange
red
© Kunststoffe
Fig. 4. In most cases the switch from the injection phase to the holding pressure phase is positiondependent. The admixture of a masterbatch changes the flow properties of the starting material. As
a result, the mean torque during plastication and the pressure level at the time of the changeover are
decreased
chine, mold and cooling system has to be
considered. A large number of effects can
be explained by the combination of different indicators across the different
process phases.A change in the mold temperature, for example, has a significant influence on the indicators for the injection
phase, but not on the indicators for the
plasticizing phase. On the other hand, external factors resulting from the drying of
the raw material, for example, have an impact on both the plasticizing phase and
the injection phase.
In the field of visible parts and parts
which are subsequently finished by painting or galvanizing, surface properties are
playing an increasingly significant role.
For these parts with their generally complex demands, a constant part weight or
constant part dimensions can no longer
be taken as the sole quality criterion, so
that 100 % visual inspections are frequently necessary.
Even minimal changes in the residual
moisture content of the raw material
change the processing properties of the
material and can then be seen on the
high-gloss surfaces, for example as moisture streaks. With engineering plastics
such as polybutylene terephthalate (PBT)
or polyamide (PA), we have effects from
the processing time in addition to the
conditioning and drying which have an
influence on the processing behavior and
the part quality [8, 9].
Of course the manufacturer’s specifications have to be observed with regard
to the processing time and maximum
residual moisture content; these are
approx. <10 min dwell time in the plas-
INJECTION MOLDING
ticating unit and <0.02 to 0.04 % residual moisture content, e.g. for PBT. Random sample testing of a raw material during production, however, would involve
a large number of measurements in the
laboratory. But not even these checks
would permit a continuous evaluation of
the raw material state and of the part
quality. As a rule, nominal drying times
are specified for the hydrophilic raw materials. If different numbers of production lines now draw material from a central drying unit at different rates due to
break and standstill times, the material
state is undefined – and process and quality fluctuations occur.
270
Torque
Nm
900
250
240
230
Nm
220
0.4
0.5
0.6
0.7
Torque
1.2
1.3 s 1.4
100
-100
Residual moisture 0.01 %
-300
-500
0
0.2
0.4
0.6
0.8
Plastication time
1.0
1.2
1.4
s
1.6
© Kunststoffe
Fig. 5. The plastication torque at the screw drive can be directly allocated to the residual moisture
levels. The higher the residual moisture content in the material, the lower the flow resistance
In order to identify creeping fluctuations,
a detection model for material changes
during the running production process
was developed. Here the required plasticizing energy and the mean motor torque
during a constant phase of plastication
are referenced and assigned to the process
changes in the event of deviations.
In the example presented below, selectively conditioned material with different
residual moisture contents was charged
into the process. During the plastication
phase, there is a large and prolonged contact with the material via the screw and
the necessary drive with a direct relationship to the flow properties. A clear relationship can indeed be clearly seen between the torques for the differently conditioned material and the residual moisture content (Fig. 5).
The physical effects of the trapped
residual moisture reduce the viscosity in
Plastication energy
1.1
300
Detection of Residual Moisture
Influences
5.0
Residual moisture content < 0.03 %
kWs
4.8
4.7
4.6
4.5
4.4
4.3
4.2
4.1
4.0
3.9
3.8
3.7
25
50
75
100
0
0.8
0.9
1.0
Plastication time
500
the process, so that the torque required
for plastication drops. A characteristic
mean plastication torque (without starting and braking torques) can be directly
assigned to the residual moisture levels.
The high resolution over time allows a
clear distinction to be made between the
mean torques required and a representation of the screw rotation. The energy input into the plastication process via the
screw torque highlights this relationship,
while the energy input by the heating system shows no significant relationship
with respect to the material change (Fig. 6).
The reference plastication energy and
the characteristic mean torque thus allow
a clear statement to be made about the
material properties. For correspondingly
referenced processes, a 100 % check can
Residual moisture content 0.1 %
125
150
175 200
Cycle
thus be installed even for minimal, but
quality-determining effects of residual
moisture fluctuations or other influences
on the material properties. A particular
advantage here is that the plastics processor does not have to install any additional sensors in the system, but can merely
make use of the information already
available.
Conclusion
The demands on the quality of injection
moldings must be monitored using intelligent solutions for process control in
order to be able to meet cost targets in
the injection molding production. With
the corresponding know-how, the possibilities available for monitoring and vi- >
Residual moisture content 0.12 %
225
250
275
300
325
350
Fig. 6. The resulting energy
level during plastication
shows a significant relationship to the change in
the material conditioning
levels (cycle 100 and 240)
in an exemplary process
with Polyamide 6. The required drive energy shows
during plastication, in line
with the residual moisture
levels, that a detection at
the limits of the processing range with a large
database can be very precisely implemented
© Kunststoffe
www.kunststoffe-international.com
45
INJECTION MOLDING
sualizing process data can be used for
quality control and process optimization.
Due to the wide variety of quality demands, there will be no general panacea
for compensating external influences in
the near future, but the latest approaches offer flexible possibilities for referencing the process and the specific requirements.
A selective inline detection of disturbances in the process is already possible
today to support quality control. The derivation of expedient control strategies and
the implementation of intelligent control
concepts will significantly boost the quality of processes and products. The challenge of the future will continue to be the
need to make ever larger volumes of information useful to the machine operator – one approach here is to integrate
corresponding assistance functions into
the machine control system.
ACKNOWLEDGMENT
Lanxess Deutschland GmbH, Leverkusen, and
mouldtec Kunststoff GmbH, Kaufbeuren, both Germany, supported this project with material and
masterbatch.
46
REFERENCES
1. Gruber, J.: Prozessführung beim Thermoplastspritzgiessen auf Basis des Werkzeuginnendrucks.
Dissertation, RWTH Aachen 2005.
2. Michaeli, W.; Schreiber, A.; Lettowski, C.: Optimierung der Prozessführung beim Thermoplastspritzgießen durch Online-Regelung auf Basis von
Prozessgrößen. Kunststofftechnik, 04 (2008),
pp. 1–17.
3. Mustafa, M. A.: Modellbasierte Ansätze zur Qualitätsregelung beim Kunststoffspritzgießen. Dissertation, University of Essen 20004. Schiffers, R.: Verbesserung der Prozessfähigkeit
beim Spritzgießen durch Nutzung von Prozessdaten und eine neuartige Schneckenhubführung. Dissertation, University of Duisburg-Essen 2009.
5. Cavic, A.: Kontinuierliche Prozessüberwachung
beim Spritzgiessen unter Einbeziehung von
Konzepten zur Verbesserung der Schmelzequalität.
Dissertation, University of Stuttgart 2005.
6. Kazmer, D. O.; Velusamy, S.; Westerdale, S.; Johnston, S.; Gao, R. X.: A comparison of seven filling
to packing switchover methods for injection molding. Polymer Engineering and Science, 50 (2010),
pp. 2,031–2,043.
7. Boss, M.; Wodke, T.: Capillary Rheometry Optimizes Injection Molding. Kunststoffe international
97 (2007) 11, pp. 87–89.
8. Pongratz, S.: Alterung von Kunststoffen während
der Verarbeitung und im Gebrauch. Dissertation,
University of Erlangen-Nuremberg 2000.
9. Ehrenstein, G.; Pongratz, S.: Beständigkeit von
Kunststoffen. Carl Hanser Verlag, Munich 2007.
THE AUTHORS
FELIX A. HEINZLER, born in 1984, has been research associate since 2010 and since 2012 Group
Leader Injection Molding at the Institute for Product
Engineering (IPE) of the University of Duisburg-Essen,
Germany; [email protected]
M.SC. STEFAN KRUPPA, born in 1983, has been
development engineer in the Machine Technology
and Test division of KraussMaffei Technologies
GmbH, Munich, Germany, since 2010.
DR.-ING. REINHARD SCHIFFERS, born in 1977, has
been Head of the Machine Technology and Test division of KraussMaffei Technologies since 2012.
PROF. DR.-ING. JOHANNES WORTBERG, born in
1951, has held the Chair of the Faculty for Engineering Design and Plastics Machinery at the Institute for
Product Engineering since 2001.

Useful Process Data from the Injection Molding

Transcription

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