Comparison of Switchover Methods for Injection Molding David O. Kazmer, Sugany Velusamy, Sarah Westerdale, and Stephen Johnston Plastics Engineering Department University of Massachusetts, Lowell Priamus Users Group Meeting September 30th, 2008

Agenda Motivation Switchover Methods Manufacturing competitiveness Characteristics of highly productive molders Switchover Methods Overview Experimental Setup Results Conclusions

Is U.S. Manufacturing in Decline?

Is U.S. Manufacturing in Decline?

U.S. Manufacturing Productivity

U.S. Manufacturing Productivity Manufacturers need 1.5% annual productivity gains to remain competitive Where is it going to come from? Cost Category Typical Plant Overseas Plant Automated Plant Direct materials (resin, sheet, fasteners, etc.) 0.50 0.48 Indirect material (supplies, lubricants, etc.) 0.03 Direct labor (operators, set-up, supervisors, etc.) 0.25 0.08 0.05 Indirect labor (maintenance, janitorial, etc.) 0.02 Fringe benefits (insurance, retirement, vacation, etc.) 0.07 Other manufacturing overhead (rent, utilities, machine depreciation, etc) 0.10 Shipping (sea, rail, truck, etc.) 0.00 “Landed” product cost 1.00 0.80 0.73

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 and application 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 workers Obsolete Competitive

Obsolete vs. Competitive Number of supervisors Obsolete Competitive

Obsolete vs. Competitive Plant size Obsolete Competitive

Obsolete vs. Competitive Energy usage Obsolete Competitive

U.S. Manufacturing Productivity Manufacturers need 1.5% annual productivity gains to remain competitive Cost Category Typical Plant Overseas Plant Automated Plant Direct materials (resin, sheet, fasteners, etc.) 0.50 0.48 Indirect material (supplies, lubricants, etc.) 0.03 Direct labor (operators, set-up, supervisors, etc.) 0.25 0.08 0.05 Indirect labor (maintenance, janitorial, etc.) 0.02 Fringe benefits (insurance, retirement, vacation, etc.) 0.07 Other manufacturing overhead (rent, utilities, machine depreciation, etc) 0.10 Shipping (sea, rail, truck, etc.) 0.00 “Landed” product cost 1.00 0.80 0.73

Agenda Motivation Switchover Methods Manufacturing competitiveness Attributes of highly productive molders Switchover Methods Overview Experimental Setup Results Conclusions 17

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 Velocity time Switchover Filling Stage Packing Stage Nozzle Condition Velocity =f(t) Pressure =f(t) End of Flow Condition Pressure=0 Velocity =0 Stiffness Low to Medium Very High Pressure time 18

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 Stage Filling Stage 19

Experimental Setup Molding Machine Plastic Material: 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

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) (-) 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) Injection Speed (mm/s) Pack Pressure (bar) Cooling time (s) Barrel Temps (oC) Coolant Plastizing Rate (RPM) 80.0 25.0 200 20.0 210 75 150 1 79.5 23.1 199 20.7 211 76 147 2 80.5 19.3 209 153 3 26.9 74 4 5 201 6 7 8

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

Main Effects on Impact Thickness for Ram Position Switchover Good process robustness

90 Cycles across the DOE for Filling Time Switchover 31

Main Effects on Impact Thickness for Filling Time Switchover Very poor process robustness

90 Cycles across the DOE for Cavity Pressure Switchover 33

Main Effects on Impact Thickness for Cavity Pressure Switchover Good process robustness

90 Cycles across the DOE for Cavity Temperature Switchover 35

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 Variation (%) More robust Long Run Variation (%)

Switchover Performance: Long-Run Variation Injection time Runner pressure Machine pressure Screw position Cavity pressure Nozzle pressure Cavity temperature

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. 40

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 number DMI-0428366/0428669 Priamus System Technologies 42