Comparison of Switchover Methods for Injection Molding

David O. Kazmer, Sugany Velusamy, Sarah Westerdale, and Stephen Johnston

Plastics Engineering DepartmentUniversity of Massachusetts, Lowell

Priamus Users Group MeetingSeptember 30th, 2008

Agenda

Motivation Manufacturing competitiveness Characteristics of highly productive

molders Switchover Methods

Overview Experimental Setup Results Conclusions

Is U.S. Manufacturing in Decline?

1950 1960 1970 1980 1990 2000 20100

5

10

15

20

25

30

35

Year

Man

ufac

turin

g E

mpl

oym

ent

(% o

f U

S W

orkf

orce

)

Is U.S. Manufacturing in Decline?

1950 1960 1970 1980 1990 2000 20100

100

200

300

400

500

600

700

800

900

Year

Man

ufac

turin

g ou

tput

(%

of

Y19

50 O

utpu

t)

U.S. Manufacturing Productivity

1950 1960 1970 1980 1990 2000 2010

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2

Year

Out

put

per

Uni

t of

Lab

or C

ost

(Y20

00=

100%

)

US Industry Historical Data

Historical 0.8% Productivity IncreaseRecent 1.5% Productivity Increase

Cost CategoryTypical

PlantOverseas

PlantAutomated

Plant

Direct materials (resin, sheet, fasteners, etc.) 0.50 0.48 0.50

Indirect material (supplies, lubricants, etc.) 0.03 0.03 0.03

Direct labor (operators, set-up, supervisors, etc.) 0.25 0.08 0.05

Indirect labor (maintenance, janitorial, etc.) 0.05 0.05 0.02

Fringe benefits (insurance, retirement, vacation, etc.) 0.07 0.03 0.03

Other manufacturing overhead (rent, utilities, machine depreciation, etc)

0.10 0.08 0.10

Shipping (sea, rail, truck, etc.) 0.00 0.05 0.00

“Landed” product cost 1.00 0.80 0.73

U.S. Manufacturing Productivity

Manufacturers need 1.5% annual productivity gains to remain competitive

Where is it going to

come from?

Characteristics of Highly Competitive Molders

Highly systematized Excellent layout Consistent and often

uni-directional flow of materials

Uniform internal planning processes Uniform quality control processes.

Many highly productive facilities use only one primary supplier of plastics machinery.

Characteristics of Highly Competitive Molders

Highly utilized 24 x 7 operation 90% plus machine utilization

Steady state strategy Use fewer and better machines running

continuously rather than more machines running fewer shifts

Characteristics of Highly Competitive Molders

High yields 95% typical 99.8% not necessary

High quality assurance Automatic: in-mold systems, vision, poka-

yoke Conservative rules to contain defects

Better to automatically reject 10 good parts than accept one bad part

Characteristics of Highly Competitive Molders

Industry sector andapplication focus Connectors Gears Syringes

Focus provides Advanced application-specific knowledge Market commitment and technology

investment

Obsolete vs. Competitive

Number of machines

Obsolete Competitive

Obsolete vs. Competitive

Number of workersObsolete Competitive

Obsolete vs. Competitive

Number of supervisorsObsolete Competitive

Obsolete vs. Competitive

Plant sizeObsolete Competitive

10,000 m2

500 m2

Obsolete vs. Competitive

Energy usageObsolete Competitive

U.S. Manufacturing Productivity

Manufacturers need 1.5% annual productivity gains to remain competitive

Cost CategoryTypical

PlantOverseas

PlantAutomated

Plant

Direct materials (resin, sheet, fasteners, etc.) 0.50 0.48 0.50

Indirect material (supplies, lubricants, etc.) 0.03 0.03 0.03

Direct labor (operators, set-up, supervisors, etc.) 0.25 0.08 0.05

Indirect labor (maintenance, janitorial, etc.) 0.05 0.05 0.02

Fringe benefits (insurance, retirement, vacation, etc.) 0.07 0.03 0.03

Other manufacturing overhead (rent, utilities, machine depreciation, etc)

0.10 0.08 0.10

Shipping (sea, rail, truck, etc.) 0.00 0.05 0.00

“Landed” product cost 1.00 0.80 0.73

Agenda

Motivation Manufacturing competitiveness Attributes of highly productive molders

Switchover Methods Overview Experimental Setup Results Conclusions

Overview: Switchover Concept

Switchover is the point at which the filling phase ends and packing phase starts From a controls perspective, there is a switch

in the system’s boundary conditions and stiffness

Variances cause: Dimensional

errors Part weight

variations Back flow

Filling Stage

PackingStage

Nozzle Condition

Velocity=f(t)

Pressure

=f(t)

End of FlowCondition

Pressure=0

Velocity=0

StiffnessLow to

MediumVeryHigh

Velocity

timePressure

time

Switchover

Overview:Switchover Methods

Various methods for switchover: Screw Position* Injection Time Injection Pressure Cavity Pressure Cavity Temperature Nozzle Pressure Tie Bar Deflection

Other studies have been conducted. This study is more comprehensive with respect

to number of methods and also long term variation.

Packing StageFilling Stage

Experimental Setup

Molding Machine 50 metric ton All

Electric Machine Make: Ferromatik

Milacron Model: Electra 50

Evolution Plastic Material:

AMOCO Polypropylene

Grade 10-3434

Process Monitoring & Control

Extremely well instrumented machine & mold

Screw position transducer Nozzle pressure transducer Ram load transducer 3 barrel thermocouples 4 in-mold pressure transducers 2 in-mold temperature sensors Nozzle infrared pyrometer In-mold infrared pyrometer PRIAMUS DAQ8102 acquisition

Custom machine override circuit

Internal or external voltage signal triggers the machine for switchover

Signal toMachineController

+

-

Signal fromMachineLoad Cell

DAQ Switchover Signal:

+5V or +24V

20 k

10 kPotSet Control

Voltage: 0-8 V

Sensor & Machine: +24 V

100 k

10 k

100 k

10 k

Sensor & Machine: Ground

Amplifier Power:-15 V

Amplifier Power:+15 V

ResistorDisconnect

Switch

Switchover Amplifier

Signal Relay

Signal toMachineController

+

-

+

-

Signal fromMachineLoad Cell

DAQ Switchover Signal:

+5V or +24V

20 k

10 kPotSet Control

Voltage: 0-8 V

Sensor & Machine: +24 V

100 k

10 k

100 k

10 k

Sensor & Machine: Ground

Amplifier Power:-15 V

Amplifier Power:+15 V

ResistorDisconnect

Switch

Switchover Amplifier

Signal Relay

Switchover Methods & Measured Attributes

Seven Switchover Methods Machine Controlled

Screw Position Injection Pressure Injection Time

Externally Controlled Nozzle pressure Runner Pressure Tensile Cavity

Pressure Cavity Temperature

Six Measured Attributes

Impact Thickness (mm) Impact Weight (g) Impact Width (mm) Tensile Thickness (mm) Tensile Weight (g) Tensile Width (mm)

Single Cycle: Screw Position, Nozzle Pressure, & Cavity Pressure

10 Consecutive Cycles

Molding Machine Statistical Characterization

100 consecutive molding cycles were monitored & data acquired The average & standard deviation was

calculated to measure of short term variation

 Plasticizing stroke

Injection speed

Pack pressure

Cooling time

Barrel Temps

Coolant Temp

Plasticizing RPM

  (mm) (mm/s) (bar) (s) (C) (C) (-)

Average 85 25 200 20 210 75 150

St Dev 0.088 0.321 0.153 0.123 0.167 0.1134 0.50715

Switchover Settings

Switchover values for each method were determined to provide same part weight

Switchover methods Value

1 Switchover point (mm) 17

2 Injection time (s) 2.92

3 Machine ram pressure (bar) 340

4 Nozzle pressure (V) 1.8

5 Runner pressure (bar) 206

6 Tensile bar cavity pressure (bar) 65

7 Tensile bar cavity temperature (C) 33

Design of Experiments (DOE)

DOE performed to impose long term variation

Setup #

Plasticizing

Stroke(mm)

InjectionSpeed(mm/s)

Pack Pressure

(bar)

Coolingtime (s)

BarrelTemps (oC)

CoolantTemps (oC)

Plastizing

Rate (RPM)

0 80.0 25.0 200 20.0 210 75 150

1 79.5 23.1 199 20.7 211 76 147

2 80.5 23.1 199 19.3 209 76 153

3 79.5 26.9 199 19.3 211 74 153

4 80.5 26.9 199 20.7 209 74 147

5 79.5 23.1 201 20.7 209 74 153

6 80.5 23.1 201 19.3 211 74 147

7 79.5 26.9 201 19.3 209 76 147

8 80.5 26.9 201 20.7 211 76 153

Analysis

The 90 cycle DOE was repeated for each of the seven switchover conditions

Parts weighed & dimensions measured The data was analyzed in Matlab to provide:

Individual traces for each of 630 cycles Overlaid traces for all cycles in a DOE run Overlaid traces for all cycles in a switchover

method Regression coefficients & main effects plots

90 Cycles across the DOE for Ram Position (Conventional) Switchover

PositionSwitchover

Main Effects on Impact Thicknessfor Ram Position Switchover

Good process robustness

90 Cycles across the DOE for Filling Time Switchover

Tim

e S

wit

chover

Main Effects on Impact Thicknessfor Filling Time Switchover

Very poor process robustness

90 Cycles across the DOE for Cavity Pressure Switchover

PressureSwitchover

Main Effects on Impact Thicknessfor Cavity Pressure Switchover

Good process robustness

90 Cycles across the DOE for Cavity Temperature Switchover

TemperatureSwitchover

Main Effects on Impact Thickness

Cavity Temperature Switchover

Best process robustness

Coefficient of Variation COV = σ / µ

Different switchovers are best for different attributes

Switchover Performance:Short vs. Long Run Variation

Short

Run V

ari

ati

on (

%)

Long Run Variation (%)

More ro

bust

Switchover Performance:Long-Run Variation

Scr

ew

posi

tion

Inje

ctio

n t

ime

M

ach

ine p

ress

ure

Nozz

le p

ress

ure

R

un

ner

pre

ssu

re

C

avit

y p

ress

ure

Cavit

y t

em

pera

ture

Conclusions

Cavity temperature provided the most robustness against changes the process settings. Place the sensor near but not at the very end of flow

due to small control system delays (speed matters) Cavity pressure provided reasonable

switchover control but had susceptibility to changes in melt temperature and velocity.

Position control provided reasonable control but roughly twice the variation of cavity temperature.

Injection time is the least reproducible method for the transfer from fill to pack, with literally 10 times the variation of temperature control.

Conclusions

Measured consistency is much better than SPI guidelines of 0.2% Response time of the molding machine,

controller and ram velocity are important to process repeatability.

Weight and thickness show higher COV than length and should be used for QC

In-mold instrumentation is vital to achieving process robustness, automatic quality control,

and competitiveness.

Acknowledgements

National Science Foundation grant numberDMI-0428366/0428669

Priamus System Technologies