1. Multivariate Analysis Overcomes Complexities in Injection Molding
September 18, 2016
11/15/2018
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2. Disclosure or duplication without consent is prohibited
ABSTRACT
Over the years, automotive exterior parts have become more complex and substantially larger, yet are molded at faster cycle times. The transformation in design and challenging manufacturing demands have driven changes in tool design, hot runner design, material formulation and molding machine functionality. With these increasing challenges, we have to ask ourselves if conventional methods of quality control, which are typically univariate, are still effective. The short answer is no. This presentation demonstrates how multivariate analysis extracts pertinent information from large amounts of complex data. It is then able to identify the correlation structure and relationships that exist between multiple process variables and present it visually. We’ll present a project comparing univariate and multivariate approaches. These methods hold the promise to both reduce the dependency on subjective, visual inspection and make lights-out manufacturing more viable.
11/15/2018
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3. BACKGROUND- Purpose and Objectives
Goal:
Utilize MVA methodology as a tool for quality control
Objectives:
Reduce inspection by use of automated parametric analysis
Expand process knowledge
Streamline process troubleshooting
Move to lights-out production
11/15/2018
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4. Injection Moulding Quality Control
Injection Moulding Process Evolution
Automotive exterior parts have become more complex, substantially larger and are being moulded at faster cycle times
The transformation in design and the challenging manufacturing demands has driven changes in tool design, hot runner design, material formulation, and moulding machine functionality
2001 Fascia
2015 Fascia
Material Test
2001
TPO Spec.
2015
% Change
Melt flow rate
18.5
35.5
91.9%
Flexural Modulus (Mpa)
1020
1800
77.5%
Mould Shrinkage
(after bake) 15 8
46.7%
Filler Content 12 ∞%
Part Design
Part design has changed noticeably
Size and weight of a typical part has increased
Some of our fascia weigh as much as 19 lbs.
The overall shape and contours of the moulded part is more complex
Intricate attachment features have been designed to minimize gap
Tool Design
Increased part complexity requires improved sequential valve gate control
These designs require precise valve gate locations to minimize flow lines and weld line
Faster cycle times requires that the cooling process be optimized with Improved cooling circuit designs
Material
Faster Cycle Times: Materials have changed as shown in the chart.
Melt flow has increased to enable the part to be filled faster and at lower pressures
We see melt flows today of 25 and even as high as 50
Additives have been designed to accelerate crystallization time, improve demoulding and many other benefits
Flexural Modulus has moved from 800 MPA to about 1800 Mpa with Mpa in the future
Filler content is higher to increase flexural modulus as well as reduce the cost of the overall compound. This drives other concerns for downstream processes.
Dimensional stability
And all the while maintaining dimensional stability.
Injection Fill Sequence
Frozen Pressure Profile
11/15/2018
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5. Disclosure or duplication without consent is prohibited
As the injection moulding process evolves are the conventional methods of quality control still effective?
Injection moulding has changed significantly since I started my plastics career back in the 70’s. We don’t twist knobs, adjust valves in the floor to adjust pressures, or set the travel on the screw with a thumbwheel and a limit switch.
Everything is electronic today, so the question we need to ask ourselves is, ….
As the injection moulding process has evolved, are conventional methods of quality control still effective ?
11/15/2018
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6. Injection Moulding Quality Control
Process Alarms and Univariate Analysis
Most moulding machines offer process value alarms or conventional SPC charting capabilities
Machine Controls have kept pace, allowing moulding operators to monitor many process conditions
Many moulding machines offer process alarms or conventional SPC charting
Key process values are readily available on a shot to shot basis and will trigger a process alarm when a specified limit has been reached
This all leads to the concept of univariate analysis
Alarm Page
Control Chart
11/15/2018
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7. Shot Size
Hold Pressure
Hot Runner Control
Clamp Force
Back Pressure
Injection Speed
Machine Down Time
There are too many variables in the injection moulding process to rely on traditional methods of quality control
Transfer Position
Machine Calibration
Check Ring Performance
Screw Speed
Mould Temperature
As we measure more & more variables, there are just too many, to continue rejecting parts based on single variables.
Valve Gate Sequence
Barrel Temperature
Material Lot
Hold Time
Screw Wear
Regrind Percentage
Transfer Pressure
Cushion
11/15/2018
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8. Expanded Process Monitoring
Today we have many opportunities to monitor multiple process parameters
54 process output parameters are available
More parameters mean more insights/relationships can be investigated
BUT
Increased multidimensional space require complex analytical methods
11/15/2018
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9. Univariate is Not Enough
Red dots are defective parts
Max Injection Pressure is considered a critical process monitoring variable
Ineffective detection: too many false positives and negatives
11/15/2018
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10. Univariate, Bivariate, Trivariate ...
Can’t visualize beyond three dimensions, so ….
11/15/2018
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Multivariate
True
Positives
False Positive
Can’t see a 4+ dimensional density ellipse but can consider Hotelling’s T2
Do not want to pass defective parts because of the significant cost of rejecting them downstream (prime, paint, assembly)
11/15/2018
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12. Injection Moulding Quality Control
Control Chart Signals and Defect Detection
There was a direct correlation of visible defects to MVA signals above the upper control limit of 56
Dependent on the combination of multiple parameters the signals identified typical process situations associated with short shot and excessive flash defects
So now we have a methodology of providing an alarm that would tell the operator to seriously look at a part, since there is a strong probability there is an issue.
Most likely, this part would have gone through a number of downstream processes, before being found.
Attachment Clip Feature Short Shot
Excess Flash at Wing Tip
11/15/2018
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13. Injection Moulding Quality Control
Conclusions
Multivariate analysis can be a very effective method of defect detection with the injection moulding process and is considerably more effective than traditional univariate methods
The process of validating the MVA model provides further insight into understanding the injection moulding process window
Principal component analysis identifies relationships between multiple parameters and enhances process troubleshooting
In conclusion, we feel that:
Multivariate analysis isa very effective method of defect detection with the injection moulding process
MVA is a more effective indicator of quality than univariate methods
The process of developing the MVA model provides significant process insight with regard to the injection moulding process
But the process is not for the faint of heart.
It is not ‘plug & play’ technology
You should expect to spend significant amount of time developing your models on a tool / machine basis, and learn how to modify / tune your data to give you the best results.
11/15/2018
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14. Injection Moulding Quality Control
Next Steps
Further evaluate and refine MVA model
Develop online process monitoring for automated defect detection
Integrate MVA inspection, classification and disposition with lights out manufacturing
Look at new features in JMP®13 including DmodX available in the Principal Components platform
11/15/2018
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