JEC COMPOSITES MAGAZINE - Issue #114 - July 2017 - 25

Fast development and intelligent process control for RTM

Fig. 1: Lab-scale RTM
set-up at Airborne

the start of shrinkage can be obtained with
pressure sensors. The new pressure sensors
contribute to the RTM process with several
new features. The piezo-resistive measuring technique allows unlimited measuring
times with very low measuring failures. The
effects of changing form or resin temperatures on the pressure reading are eliminated
by an advanced temperature compensation
algorithm. The low-pressure sensor has two
separate pressure ranges, a highly amplified
range, e.g. for monitoring the vacuum conditions inside the form, and a pressure range for
the normal injection pressure. With the help
of pressure sensors, process failures or deviations can be detected, also allowing a correlation of part quality with the pressure curve.
They are therefore an important prerequisite
for a reproducible and stable process, as well
as for the further automatization of the RTM
process.
RTM application
Airborne (The Netherlands) has a long history in developing and manufacturing tidal
blades for energy applications. Within the
Ecomise project, the company developed a

brand new 2m long tidal blade from carbon
fibre, epoxy and core materials, to be manufactured by closed mould injection in one
shot within an oven. In order to study in more
detail the filling and curing processes that take
place in thick laminates, Airborne first designed and constructed a metal RTM mould
with the possibility to introduce a variety of
sensors. Afterwards, all the sensors and systems moved to an industrial environment for
the production of the new tidal blade.
Lab-scale application
The sensors and the process monitoring systems were first installed in the specially-designed RTM steel mould to check and develop the infusion and curing processes, as can
be seen in Figure 1.
The mould cavity was 700 x 200mm and
100mm thick, with a draft angle of 15°. The
resin was injected at the bottom of the tool
and the vents were placed at the top so that
the resin impregnated the part through its
thickness. More than 20 sensors were used
to measure the resin arrival, resin cure, temperature development and pressure inside the
mould:

Fig. 2: Measured cavity pressure (P), tool temperature (T1) and internal laminate
temperatures (T4 and T5)

- 2 cure, 4 resin arrival and 6 temperature
durable sensors for real-time monitoring of
resin arrival and cure in the mould cavity,
combined with OptiMold and OptiFlow
systems provided by Synthesites (Figure
1).
- 4 disposable resin arrival sensors to check
the flow front through the thickness during
process development.
- 4 disposable thermocouples to check the
flow front through the thickness during
process development.
- 1 advanced pressure sensor to measure the
pressure inside the mould cavity during injection and curing; a Kistler 4001AQ10-01
sensor was used.
Due to the high UD carbon fibre content and
the thickness of the part, the injection needed
more than 100 minutes to conclude, as can
be seen in Figure 2 which shows the temperature at the tool (T1) and inside the part
(T5@25mm and T4@50mm through-thickness). To enhance resin flow and speed up
filling, the injection pressure was raised from
simple vacuum up to 6 bars at the end of the
injection, as can be seen again in Figure 2 by
the curve (P), which indicates the pressure in
the cavity from the pressure sensor close to
the injection gate.
After the end of filling, the injection pressure
was maintained almost constant for some
time; but when the injection pressure was
released, the pressure started to decrease rapidly as the resin cure started, as seen by the
increase of resistance in Figure 3. During curing, the internal temperature of the laminate
rose sharply, resulting in an exotherm peak

Fig. 3: Evolution of measured tool temperature (T) and resistivity (R) and predicted
Tg (TgR)

N°114 July 2017

/ jec composites magazine

Table of Contents for the Digital Edition of JEC COMPOSITES MAGAZINE - Issue #114 - July 2017