extreme process monitoring and in-line quality assesment of micromouldings polymer process...
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EXTREME PROCESS MONITORING AND IN-LINE QUALITY ASSESMENT OF MICROMOULDINGS
Polymer Process Engineering 2005
EXTREME PROCESS MONITORING AND IN-LINE QUALITY ASSESMENT OF MICROMOULDINGS
Polymer Process Engineering 2005
B R Whiteside, R Spares, M T Martyn, P D Coates, IRC in Polymer Science & Technology, Dept of Mechanical & Medical Engineering
University of Bradford, Bradford BD7 1DP, UK
B R Whiteside, R Spares, M T Martyn, P D Coates, IRC in Polymer Science & Technology, Dept of Mechanical & Medical Engineering
University of Bradford, Bradford BD7 1DP, UK
ContentsContents
• Introduction• Process monitoring research
• Experimental set up• Results
• Product Inspection• Theory• Experimental• Evaluation
• Introduction• Process monitoring research
• Experimental set up• Results
• Product Inspection• Theory• Experimental• Evaluation
Micromoulding at BradfordMicromoulding at Bradford
• Micromoulding research since 2001• Analysis of novel process dynamics • Product property assessment
• 2005 – Centre for Micro and Nano Moulding• MNM Lab - 7 micromoulding machines• Metrology laboratory (AFM, SEM, Interferometry, Optical
techniques)
• Micromoulding research since 2001• Analysis of novel process dynamics • Product property assessment
• 2005 – Centre for Micro and Nano Moulding• MNM Lab - 7 micromoulding machines• Metrology laboratory (AFM, SEM, Interferometry, Optical
techniques)
Microsystem 50Microsystem 50 Fanuc Roboshot 5tFanuc Roboshot 5t Metrology laboratoryMetrology laboratory
Battenfeld Microsystem 50Battenfeld Microsystem 50
• Purpose built micro injection process
• Novel solution for injection/metering
• Servo-electric drives
• Automatic parts handling
• Clean room filtration
• Modular
• Purpose built micro injection process
• Novel solution for injection/metering
• Servo-electric drives
• Automatic parts handling
• Clean room filtration
• Modular
Battenfeld Microsystem 50Battenfeld Microsystem 50
Process CharacterisationProcess Characterisation
Dynisco PCI 4011 Piezo load transducer
Dynisco PCI 4011 Piezo load transducer
Dynisco PCI 4006 piezo load transducer
Dynisco PCI 4006 piezo load transducer
Temposonics R series displacement transducer
Temposonics R series displacement transducer
J-type thermocouplesJ-type thermocouples
• A suite of sensors installed on the Microsystem to help determine process dynamics
• A suite of sensors installed on the Microsystem to help determine process dynamics
Plus machine encoder outputsPlus machine encoder outputs
Typical Process DataTypical Process Data
StartinjectStartinject
EndinjectEndinject
ProductsolidificationProductsolidification
MouldopenMouldopen
Why measure process dynamics?Why measure process dynamics?
• Pure research• Highlight interesting/unexpected behaviour• Validation of constitutive equations/computer
based models
• Development for industry• Identify processing problems• Assist with process optimisation
• Pure research• Highlight interesting/unexpected behaviour• Validation of constitutive equations/computer
based models
• Development for industry• Identify processing problems• Assist with process optimisation
Experimental detailsExperimental details
• Evaluate the response of process measurement to forced changes in moulding conditions• Melt temperature variation • Mould temperature variation
• Determine which parameter is the statistically most sensitive to process variation• Peak injection pressure• Peak cavity pressure• Injection pressure integral• Cavity pressure integral
• Evaluate the response of process measurement to forced changes in moulding conditions• Melt temperature variation • Mould temperature variation
• Determine which parameter is the statistically most sensitive to process variation• Peak injection pressure• Peak cavity pressure• Injection pressure integral• Cavity pressure integral
Product detailsProduct details
0.34mg (HDPE), 0.49mg (POM)
Large diameter = 1.0mm
Small diameter = 0.5mm
Gate dimension 0.1 x 0.2mm
0.34mg (HDPE), 0.49mg (POM)
Large diameter = 1.0mm
Small diameter = 0.5mm
Gate dimension 0.1 x 0.2mm
Melt temperature variationMelt temperature variation
Cavity pressure integral data appears to be the most sensitive indicator of changeCavity pressure integral data appears to be the most sensitive indicator of change
Melt temperature variationMelt temperature variation
Scatterplot matrix shows that integral measurements perform best and cavity pressure measurements are the most sensitive
Scatterplot matrix shows that integral measurements perform best and cavity pressure measurements are the most sensitive
Mould temperature variationMould temperature variation
Cavity pressure integral appears to show most sensitivity to process variationCavity pressure integral appears to show most sensitivity to process variation
Mould temperature variationMould temperature variation
Cavity pressure measurements most sensitiveCavity pressure measurements most sensitive
Repeatability comparisonRepeatability comparison
23.1mg (HDPE)
Plaque dimensions 7.3 x 3 x 1mm
Steps 1.0, 0.5, 0.25mm
23.1mg (HDPE)
Plaque dimensions 7.3 x 3 x 1mm
Steps 1.0, 0.5, 0.25mm
0.34mg (HDPE)
Large diameter = 1.0mm;
Small diameter = 0.5mm
Gate dimension 0.1 x 0.2mm
0.34mg (HDPE)
Large diameter = 1.0mm;
Small diameter = 0.5mm
Gate dimension 0.1 x 0.2mm
Coefficient of variationCoefficient of variation
23.1mg product23.1mg product
0.34mg product0.34mg product
What can we draw from this?What can we draw from this?
• Micromouldings form a small fraction of the total shot weight at the end of the flow path
• Small process variations have a large impact on moulding quality
• Cavity pressure sensors are required to monitor/maintain the process window
• Product imaging required where process yield <100%
• Micromouldings form a small fraction of the total shot weight at the end of the flow path
• Small process variations have a large impact on moulding quality
• Cavity pressure sensors are required to monitor/maintain the process window
• Product imaging required where process yield <100%
How do we relate process conditions to defective mouldings?
How do we relate process conditions to defective mouldings?
• Monitor the process conditions during a production run and subsequently measure product properties• Atomic force microscopy• Surface profilometry• Nanoindenting• Machine vision
• Use statistical methods to correlate defects with data acquisition results
• Monitor the process conditions during a production run and subsequently measure product properties• Atomic force microscopy• Surface profilometry• Nanoindenting• Machine vision
• Use statistical methods to correlate defects with data acquisition results
Time consumingTime consuming
Commercial vision systemsCommercial vision systems
• Expensive• Require multiple cameras for 3-d measurements• Typically low resolution cameras
Ideal system
• Microscope lenses• Megapixel resolution or better• 3-d measurements• Fast acquisition speeds• Reasonable price
• Expensive• Require multiple cameras for 3-d measurements• Typically low resolution cameras
Ideal system
• Microscope lenses• Megapixel resolution or better• 3-d measurements• Fast acquisition speeds• Reasonable price
Image analysis of MicromouldingsImage analysis of Micromouldings
• For many micromoulded products microscope lenses are required for accurate optical assessment
• Microscopes typically have very small depths of field so it is difficult to image a 3-dimensional surface
• Extended depth of field techniques have arisen to address this problem and these methods can also be used to generate 3-dimensional information
• For many micromoulded products microscope lenses are required for accurate optical assessment
• Microscopes typically have very small depths of field so it is difficult to image a 3-dimensional surface
• Extended depth of field techniques have arisen to address this problem and these methods can also be used to generate 3-dimensional information
MethodMethod
CCD CameraCCD Camera
MicroscopeMicroscope
• Traverse the sample towards the microscope in 1um increments
• Capture each image to pc
• Process data to determine which frames are in focus for each pixel in the image
• Create 3-D dataset
• Traverse the sample towards the microscope in 1um increments
• Capture each image to pc
• Process data to determine which frames are in focus for each pixel in the image
• Create 3-D dataset
Focal PlaneFocal Plane
Motorised StageMotorised Stage
Image capture/stage controlling PC
Image capture/stage controlling PC
Focus algorithms?Focus algorithms?• Use convolution kernels to look for pixel regions with
high local intensity gradients (contrast)• Sobel filters: -
• Use convolution kernels to look for pixel regions with high local intensity gradients (contrast)
• Sobel filters: -
-1 0 +1
-2 0 +2
-1 0 +1
+1 +2 +1
0 0 0
-1 -2 -1
GxGx GyGy
Reconstruct a full focus image from the pixels of best contrast in each of the image ‘slices’. The slice location provides height information for that pixel
Reconstruct a full focus image from the pixels of best contrast in each of the image ‘slices’. The slice location provides height information for that pixel
yxyx GGGGG 22
Resultant imagesResultant images
The capture system creates two datasets – the full focus image and the height data
Height data well suited for standard machine vision analysis
The capture system creates two datasets – the full focus image and the height data
Height data well suited for standard machine vision analysis
Extended depth of fieldExtended depth of field HeightmapHeightmap Coloured heightmapColoured heightmap
Analysis procedureAnalysis procedure
National Instruments Labview 7.1 / Vision 7.1National Instruments Labview 7.1 / Vision 7.1
3-dimensional information3-dimensional information
Cursors allow calibrated dimension information to be read directly from the plot
Results appear good
Cursors allow calibrated dimension information to be read directly from the plot
Results appear good
Short shot studyShort shot study
Short shots produced on Battenfeld Microsystem Resin: BP Rigidex 5050 HDPEShort shots produced on Battenfeld Microsystem Resin: BP Rigidex 5050 HDPE
Short shot componentShort shot component
2-d view is not easily able to spot incomplete filling. EDOF techniques can easily detect part filled components2-d view is not easily able to spot incomplete filling. EDOF techniques can easily detect part filled components
3-D representation3-D representation
Microscope imagesMicroscope images 3-D image generated from heightmap and full focus data generated by EDOF system
3-D image generated from heightmap and full focus data generated by EDOF system
Fracture/debris defectFracture/debris defect
2-dimensional data may miss surface defects such as this.EDOF technique clearly shows presence of undesirable surface properties
2-dimensional data may miss surface defects such as this.EDOF technique clearly shows presence of undesirable surface properties
Technique validationTechnique validation
• The process appears to provide reasonable results but comparison of results with other techniques gives confidence
• Products were imaged using a Wyko optical profiler and compared with EDOF data
• The process appears to provide reasonable results but comparison of results with other techniques gives confidence
• Products were imaged using a Wyko optical profiler and compared with EDOF data
Wyko NT1100 uses white light Interference to generate high accuracy surface measurements
Technique is slow and susceptible to mechanical and thermal instabilities making it unsuitable for at-process monitoring
Wyko NT1100 uses white light Interference to generate high accuracy surface measurements
Technique is slow and susceptible to mechanical and thermal instabilities making it unsuitable for at-process monitoring
Comparison of techniquesComparison of techniques
EDOF techniqueEDOF technique WLI techniqueWLI technique
EDOF technique data shown above is unfiltered
WLI system loses data where reflected light intensity is not sufficient for adequate interference fringes
EDOF technique data shown above is unfiltered
WLI system loses data where reflected light intensity is not sufficient for adequate interference fringes
Comparison of techniquesComparison of techniques
Similar profile information, but EDOF technique shows errors at edges where peaks occur due to lightingSimilar profile information, but EDOF technique shows errors at edges where peaks occur due to lighting
Comparison of techniquesComparison of techniques
Good agreement between results with slight ‘tilt’ on WLI dataDue to image flattening – different x-y planesGood agreement between results with slight ‘tilt’ on WLI dataDue to image flattening – different x-y planes
Technique refinementsTechnique refinements
• For maximum accuracy within machine cycle time:• 1-D high precision stepping stage• Ring lighting/darkfield lighting • High speed, high resolution camera/PCI express• Rapid image processing
• Fast PC• On-card processing
• For maximum accuracy within machine cycle time:• 1-D high precision stepping stage• Ring lighting/darkfield lighting • High speed, high resolution camera/PCI express• Rapid image processing
• Fast PC• On-card processing
Vision system summaryVision system summary
• Single camera system capable of 3-D measurements
• Resolution ~ few µm • Fast camera required to reduce acquisition
times• System allows 3-D manipulation of virtual
product to verify moulding quality
• Single camera system capable of 3-D measurements
• Resolution ~ few µm • Fast camera required to reduce acquisition
times• System allows 3-D manipulation of virtual
product to verify moulding quality
The goalThe goal
Data acquisition suite incorporating:
• Temperature measurement• Piezo pressure measurement• Ultrasonic measurements• 3-d characterisation
Allowing for evaluation of full process history of micromoulded products and enabling the determination of crucial parameters that influence micromoulding success
Data acquisition suite incorporating:
• Temperature measurement• Piezo pressure measurement• Ultrasonic measurements• 3-d characterisation
Allowing for evaluation of full process history of micromoulded products and enabling the determination of crucial parameters that influence micromoulding success
Thank YouThank You
Acknowledgements
• EPSRC
• Yorkshire Forward
• Members of the Micromoulding Interest Group www.ukmig.com
Acknowledgements
• EPSRC
• Yorkshire Forward
• Members of the Micromoulding Interest Group www.ukmig.com
www.ukmig.comwww.ukmig.com
For more information and details about the Micromoulding Interest Group
For more information and details about the Micromoulding Interest Group