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