molded part prediction

7/30/2019 Molded Part Prediction

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ON-LINE PREDICTION OF MOLDED PART PROPERTIES IN

RIM-PROCESSING BY CONTROL OF CAVITY PRESSURE

Enno Henze, Walter Michaeli

Institute for Plastics Processing (IKV), Aachen, Germany

AbstractThe very short cross-linking start-time of high-speed-

RIM-systems used for automotive body applications leads

to high demands on process control in order to guarantee a

reproducible run of process. A method of controlling the

RIM process using the course of the mold cavity pressure

is developed. Characteristic process values calculated from

the mold cavity pressure are correlated with part attributes.

On the basis of the correlation analysis statistical process

models are calculated which describe part attributes de-

pending on the characteristic process values.

Introduction

Large and medium series polyurethane (PU) parts are

mainly used for automotive body applications as bumpers,

spoilers, side panels and exterior trim parts.

Improvements of economy in Reaction Injection

Molding (RIM) especially in the processing fiber-

reinforced polyurethanes (RRIM) are achieved by reduc-

tion of cycle time using fast curing PU-systems (high-

speed-RIM-systems). Such PU-Systems are characterized

by a cross linking start-time of less than one second and a

curing time of approx. 25 to 30 second (1).

Due to the very short metering times the demands on

process control increase extremely. A reproducible run of

process can only be achieved by constant process parame-

ters. Although in industrial production the process pa-

rameters are monitored by the machine control unit the run

of process however has still not reached the optimal level.

The quality target of prime importance in manufac-

turing large area automotive parts is the achievement of a

class-A-surface, which can be painted without any follow-

up treatment. In industrial praxis this target however can-

not be realized over a longer period of time.

Since surface defects like porosities and pin holes can

only be recognized after the painting process, time inten-

sive repair work is required in order to achieve a perfect

surface quality. The formation of surface defects in RRIM-

part manufacturing is analyzed in various investigations,

which show that the cavity pressure is the determining pa-

rameter for the surface quality.

ObjectiveDue to the complexity of the production process an

the dominant influence of the chemical raw material use

the causes for the occurrence of process disturbances a

difficult to describe.

Therefore statistical process models, which describ

quantitatively and on-line the influence of process-specif

quantities on the manufacturing of microcellular PU-par

are developed. The investigations are focused on proces

quantities measured during the molding process, e.g. th

course of the cavity pressure which is not used for proces

monitoring today.

Monitoring of process quantities

The basic prerequisite for the monitoring of the cavi

pressure course is its significant influence on the part prop

erties to be controlled. One of the main difficulties is th

definition of criteria which indicate the occurrence of pro

cess disturbances. The check of process quantities on sin

gle significant values like upper or lower tolerance limits

not successful, since there are various causes for reachin

the limit which often do not correlate obviously with th

part imperfections observed.

The division of the manufacturing process into th

three subprocesses mixing and metering, molding and d

molding makes clear that the process quantities which ca

be measured in the mold cavity like cavity pressure an

mold temperature are influenced by many parameters. Ad

ditionally the courses of the process quantities do not onl

depend on time but also on the processing technique, th

machine technology and on the point where they are mea

ured (Figure 1).

Figure 2 illustrates the required injection pressure o

an experimental mold at the end of the injection phase ca

culated by the simulation program CADMOULD-3DSince the cavity can only be filled to a filling rate of 95 t

98 % the required filling pressure does not increase mo

notonously over the whole flow path. Critical part area

can be detected near the gate and at the end of the flo

path. A high pressure level near the gate leads to the effec

that the gas loading becomes ineffective since the gas ca

not be solved sufficiently out of the reaction mixture. Th

consequences are sink marks. On the other hand a lo

pressure level at the end of the flow path causes the occu

rence of pin holes at the surface of the part. The proce


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must be performed in such a way that a sufficient pressure

level can be achieved in the whole cavity and held over a

certain period of time. The difference is made clear in Fig-

ure 3, which shows two calculated courses of the cavity

pressure in a distance of 1 m length of flow path.

Additional problems in monitoring process quantities

result from the fact that a correlation between the time

history of the courses of the process quantities and a dis-crete part property has to be established. However a cor-

relation in means of a regression model can only be fixed

between discrete values. In order to solve this problem a

method developed at IKV for the injection molding of

thermoplastics and elastomers is adapted to the needs of

RIM processing (2, 3). The method is based on the idea to

describe the course of process quantities by discrete char-

acteristic values like maxima, minima, integrals or gradi-

ents which are correlated with part properties. In case of a

reliable process model changes in part properties can be

recognized by changes of the characteristic process values.

In means of adapting the method two main distinctions

have to be drawn between the injection molding and the

RIM process. Due to the very short metering times and the

simultaneous mixing process a variation of the injection

speed during the injection phase cannot be realized. Fur-

thermore the cavity pressure and the mold temperature is

only determined by the metering and curing process. With

exception to the holding-pressure-technique the curing

process itself cannot be influenced by machine input pa-

rameters after metering.

In the investigations discussed below it is shown how

the course of the cavity pressure can be described by char-acteristic process values and how a correlation between the

machine input parameters, the process quantities and the

resulting part properties can be established.

Experimental analysis

An central-composite-experimental design is used for

variation of those process parameters which can fluctuate

during the manufacturing process or which show a decisive

influence on the course of the cavity pressure. The pa-

rameters analyzed in the experimental studies are the mold

filling ratio Fwhich is a function of the volume flows ofthe components and the injection time, the gas loading x,

and the temperature of the reaction mixture TG:

The filling rate is of decisive importance for the pres-

sure level in the cavity. Increasing filling rates lead to

higher cavity pressure. Although injection time and volume

flows can be controlled exactly the occurrence of flash

caused by mold breathing and venting bore-holes lead t

volume variations in the cavity. The gas loading and th

temperature of the reaction mixture influence the mol

filling behavior and the foaming process, which means th

both parameters effect the course of the cavity pressure.

The investigations were performed with a high-speed

RIM-system of a cross-linking start time below one secon

and a curing time of 30 s. The experimental mold was simple plate (200 x 200 x 5 mm

3) equipped with a pressu

sensor near the gate. The plate was injected using a later

fan gate. The test parts were produced on high-pressure r

circulation plant with a gas loading unit and a nozzle ne

dle controlled mixing head with an internal outlet throttle.

Definition of characteristic process values

The following independent values were calculate

from the measured machine input quantities:

average gas loading over the whole cycle xm, average volume flow of both components over the in

jection time Qpoly, Qiso,

average component temperature of both componenTpoly, Tiso,

injection time tinjection,

filling rate Faccording to Equation (1) and

reaction temperature TMaccording to Equation (2).

Figure 4 shows the calculated values and the accord

ing setpoint. The results indicate that the process values d

not achieve exactly their setpoints. This illustrates th

problem of achieving a stationary point-of-operation in

limited period of time. Despite these fluctuations the stan

dard deviation of the measurements is on a low leve

which means that the process correlation can be detecte

with a high statistical significance.

The characteristic process values calculated from th

course of the cavity pressure are shown in Figure 5. Th

flow front reaches the pressure sensor at pointpInStart. Fro

now on the pressure rises. At pointpInEnd the filling proce

is terminated, the pressure continues rising due to the ex

pansion of the gas. At point pCPStart the cleaning pisto

moves forward until its front position is reached in pCPEn

The additional compression caused by the motion of thcleaning piston leads to a further pressure rise pRSDiff. Aft

that point the pressure rises slowly due to the expansion o

the gas until the pressure maximum is reached in pMaNow the pressure decreases since the curing process come

to an end and the part is cooled.

Calculation of the process models

Due to the small dimensions of the test geometry an

the short flow path surface defects cannot be expecte


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Therefore the average density calculated from the weight is

taken as the relevant part property.

The calculation of the process models consists of two

steps. First the characteristic process values are correlated

with the average density. On basis of the correlation analy-

sis a stepwise multiple regression is performed in order to

determine a mathematical equation which describes the

density as a function of the characteristic process values.

The following different kinds of process models are

discussed:

process model on the basis of machine input values,

process model on the basis of characteristic processvalues and

process model on the basis of machine input parametersand characteristic process values.

In industrial production the feedback values of ma-

chine input parameters are usually measured by the ma-

chine control unit and proved whether they are within thetolerance limits or not. A conclusion according to the ef-

fect of the actual feedback values on the part properties is

not drawn since this requires a systematic process analysis

by means of experimental design. Therefore the first possi-

bility represents a further development of the hitherto pro-

cess control:

The second approach is based exclusively on process

quantities measured in the mold:

Actually values calculated from the cavity pressure are

used. But it is also possible to use additional process quan-

tities, e.g. the course of the mold temperature. This ap-

proach guarantees that process disturbances which influ-

ence the process after metering and mold filling are indi-

cated and visualized. Since the characteristic process val-

ues calculated above correlate definitely with the machine

input quantities fluctuations of the machine input quantities

are also taken into account by this approach.

The third approach consists of a combination of the

first and the second approach. In this case machine- and

mold-based process quantities are taken into account by

the model equation. This procedure is sensitive to fluctua-

tions of process parameters as well as to disturbances dur-

ing mold filling and curing:

Figure 6 to 8 illustrate the comparison between th

measured and the model predicted part properties. The lef

handed chart shows the regression diagram with the pre

diction interval, the accuracy R

2

and the Fisher-value FThe right-handed diagram displays the measured and th

predicted density versus the test number.

The comparison points out a significant differenc

between the different approaches. All models are charac

terized by a high accuracy R2

in principle but there are sti

some main differences which have to mentioned. Th

model which is only based on characteristic process value

indicates the lowest accuracy R2. The model based on m

chine input parameters is quite acceptable. The addition

integration of characteristic process values leads to th

highest accuracy R2

and to an even 30 % smaller predic

tion interval. Therefore it is possible to predict the densitto an accuracy of 1 % with a probability of 95 %.

Conclusion

The method presented establishes the on-line mon

toring of the RIM process by statistical process model

Therefore a systematic procedure on the basis of exper

mental design is required. Although the method has bee

verified using a simple test geometry there are no restri

tions for complex part structures and additional part prop

erties.

Although a prediction of molded part properties cabe performed by statistical process models based on mea

ured input parameters the results point out that proce

quantities measured during the molding process have to b

taken into account in order to achieve a reliable proces

monitoring.

References

(1) Braun, H.-J:, Eyerer, P.,"PUR-RIM- und -RRIM Tech

nologie: Fortschritte und Wirtschaftlichkeit", Kunststof

78 (1988) 10, p. 991-996

(2) Michaeli, W., Henze, E., "Reproduzierbare Prozefhrung bei der Verarbeitung von Polyurethanen m

Hilfe statistischer Prozemodelle", AiF research repor

IKV, Aachen, 1997

(3) Gierth, M.,"Methoden und Hilfsmittel zur prozenahe

Qualittssicherung beim Spritzgieen von Thermoplasten

doctoral thesis, RWTH Aachen, 199


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Figure 1: Process quantities in RIM-processing

Figure 2: Calculated filling pressure of an

experimental mold

0 20 40 60 80

85

90

95

100

test number

fillingrateF

[%]

0 20 40 60 80

42

44

46

48

50

test number

setpoint

measured value

TM

[C]

0 1 2 3 4 50

5

10

15

pressure balance

in the cavity

pressure rise caused

be cleaning piston

injection phasenear the gate

at end of the flow path

filling rate : 90 %

gas loading : 50 %

Tmixture

: 45 C

time [s]

cavitypressure

[105Pa]

Figure 3: Calculated courses of cavity pressure

0 20 40 60 8035

40

45

50

55

test number

gasloading

[%]

0 20 40 600,90

0,95

1,00

1,05

1,10

test number

measured value

density

[103kg/m3]

Figure 4: Machine input values and resulting part density


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0 2 4 6 8 10 120

1

2

3

4

5

6

7

pInStart

pCPDiff

pMax

pCPEnd

pExpans

pCPStart

pMaxInt

pInInt

pInEnd

pInStart

flow front reaches pressure sensor

pInEnd

max. injection pressure

pCPStart

start of cleaning piston

pCPEnd

stop of cleaning piston

p

CPDiff

pressure rise caused by cleaning piston

pMax

max. cavity pressure

pExpans

pressure rise caused by reaction

pInInt

Integral from t=0 to pInEnd

pMaxInt

Integral from pMax

time [s]

cavitypres

sure

p[105Pa]

Figure 5: Characteristic process values calculated

from cavity pressure

0,90 0,95 1,00 1,05 1,100,90

0,95

1,00

1,05

1,10

R2 = 98 %

F = 434

pegression

95% prediction

calculated

[103kg/m3]

measured

[103kg/m3]

0 20 40 60 800,90

0,95

1,00

1,05

1,10measured

predicted

test number

density

[103k

g/m

3]

Figure 6: Process model on the basis of machine

input values

0,90 0,95 1,00 1,05 1,100,90

0,95

1,00

1,05

1,10

R2 = 92 %

F = 264

regression

95% prediction

calculated

[103 kg/m3]

measured

[103kg/m3]

0 20 40 60 800,90

0,95

1,00

1,05

1,10

measured

predicted

test number

dens

ity

[103kg/m3]

Figure 7: Process model calculated from

characteristic process values

0,90 0,95 1,00 1,05 1,100,90

0,95

1,00

1,05

1,10

R2 = 99 %

F = 837

regression

95% prediction

calculated

[g/cm3]

measured

[103kg/m

3]

0 20 40 60 800,90

0,95

1,00

1,05

1,10measured

predicted

test number

density

[103kg/m

3]

Figure 8: Process model calculated from mach

input and characteristic process valu

molded part prediction

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