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1 MAP lecture, 2003 Hamilton Institute Two dimensional (2D) System Ideas for Industrial Processes Peter Wellstead.

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Presentation on theme: "1 MAP lecture, 2003 Hamilton Institute Two dimensional (2D) System Ideas for Industrial Processes Peter Wellstead."— Presentation transcript:

1 1 MAP lecture, 2003 Hamilton Institute Two dimensional (2D) System Ideas for Industrial Processes Peter Wellstead

2 2 MAP lecture, 2003 Hamilton Institute Examples of Practical 2D Processes v Plastic film extrusion v Coating processes (adhesives on paper sheets) v Steel rolling and continuous casting v Spray actuation systems v Paper making

3 3 MAP lecture, 2003 Hamilton Institute Motivation: Personal Experience v 1970-72: real-time image processing for bubble chamber photographs v 1975-85: self-tuning control v 1980s: self-tuning filters for 2D images v 1980s: modeling and control of polymer film extruders v 1990s: algorithms for practical 2D systems

4 4 MAP lecture, 2003 Hamilton Institute Motivation: Practical (e.g. Paper Making) v Technical - product quality and plant flexibility v Economics – 1% reduction in waste produces a 300,000 Euro saving per year per machine v Environment - EU plant efficiency requirements

5 5 MAP lecture, 2003 Hamilton Institute Motivation: Research v A generic class of 2-D dynamic systems - paper, plastic film, sheet forming, coating and converting v Opportunity for innovation - 2-D concepts not previously used in sheet forming. v Applications driven research - real 2-D systems as motivation for appropriate 2-D theory.

6 6 MAP lecture, 2003 Hamilton Institute Idealised Plastic Film Extruder System: Aspects of the Control Problem

7 7 MAP lecture, 2003 Hamilton Institute sensors estimators controllers sensor signal processing actuation

8 8 MAP lecture, 2003 Hamilton Institute Control Issues: Practical 2D Systems Models and identification v Sensors and sensor signal processing v Control

9 9 MAP lecture, 2003 Hamilton Institute Models

10 10 MAP lecture, 2003 Hamilton Institute Models 2-D models should describe the joint MD and CD variations

11 11 MAP lecture, 2003 Hamilton Institute Models v Model for identification: 2D-ARMAX

12 12 MAP lecture, 2003 Hamilton Institute Models v Two dimensional data structures for sheet processes Structures used in image processing This structure for 2-D control

13 13 MAP lecture, 2003 Hamilton Institute Identification v 2-D identification: 2D-ARMAX estimation ý 2-D adaptive memory methods ý non causal model estimation methods ý edge effects

14 14 MAP lecture, 2003 Hamilton Institute Identification v non causal model estimation methods v uses row-recursive methods for FIR 2-D filter implementation to generate prediction errors and simulate

15 15 MAP lecture, 2003 Hamilton Institute Identification v 2-D adaptive memory methods v 2-D forgetting factors give selected weights to information from all directions

16 16 MAP lecture, 2003 Hamilton Institute Identification v Structure estimation v Example shows a method for QP support size estimation

17 17 MAP lecture, 2003 Hamilton Institute Control Issues: Practical 2D Systems v Models and identification Sensors and sensor signal processing v Control

18 18 MAP lecture, 2003 Hamilton Institute Sensors: the requirement v Extrusion line speeds move at 300m/min. Paper machines move at 1000m/min (~30miles/h) v Less than 0.002% of a paper roll is measured v Need for increased density of measurement

19 19 MAP lecture, 2003 Hamilton Institute Scanning gauge data collection Data collected on this path But what is happening to the product here? And here

20 20 MAP lecture, 2003 Hamilton Institute How do we get full sheet information? v Hardware for Full Sheet Sensing –sensor arrays v Software for Full Sheet Sensing –Generalised Sampling Theory

21 21 MAP lecture, 2003 Hamilton Institute Distortion of sheet data using scanning gauges v Collecting data along a zig-zag path scanning gauges are performing a 2-D SAMPLING PROCESS. v 2-D spectral analysis shows that the two scans (left scan and right scan) collect sheet data in different ways.

22 22 MAP lecture, 2003 Hamilton Institute Sampling theory reminder v One dimension 0T2T3T4T5T6T7T 01/T2/T-1/T-2/T t f time domain frequency domain Data spectrum 0 v Two dimensions T2T3T4T5T f 1 f 2 1/T -1/T time/space domain 2-D frequency domain

23 23 MAP lecture, 2003 Hamilton Institute Spectra of scanning gauges v The scans are NOT in the CD, v Alternate scans are in opposite directions v RESULT: the two sets of spectra are distorted and in different ways

24 24 MAP lecture, 2003 Hamilton Institute Scan averaging interpretation In a basic scanner the results of adjacent scans are averaged

25 25 MAP lecture, 2003 Hamilton Institute Result of basic gauge signal processing Data spectrum left scan Right scan spectrum added

26 26 MAP lecture, 2003 Hamilton Institute How to avoid distortion and get full sheet information v Use Generalised Sampling to reconstruct the MD signal. v Get the full sheet information by assembling the reconstructed MD signals

27 27 MAP lecture, 2003 Hamilton Institute Generalised Sampling v By considering reconstruction along an MD line, the Generalised Sampling Theorem can be used to reconstruct the full 2-D sheet and double the bandwidth.

28 28 MAP lecture, 2003 Hamilton Institute 2T Signal processing interpretation Sampling along the MD as a generalised sampling process Signal processing block diagram

29 29 MAP lecture, 2003 Hamilton Institute MD reconstruction results Reconstruction of MD data using generalised sampling Reconstruction of MD using conventional signal processing Actual MD data Results using conventional methods

30 30 MAP lecture, 2003 Hamilton Institute Scan averaging interpretation Generalised sampling reconstruction results in an average of scans in both directions. The weightings used at each CD point is different

31 31 MAP lecture, 2003 Hamilton Institute Summary v Conventional averaging of scanner data gives a distorted view of the sheet variations, and has an aliassing bandwidth of 1/2T. v Generalised sampling reconstructs full sheet data by compensating for the scanning geometry. The bandwidth is DOUBLED to 1/T.

32 32 MAP lecture, 2003 Hamilton Institute How do we get full sheet information? v Hardware for Full Sheet Sensing –sensor arrays. v Software for Full Sheet Sensing –use 2-D sampling theory find out how and under what conditions full sheet information can be reconstructed from scanning gauge data.

33 33 MAP lecture, 2003 Hamilton Institute Multi-gauge scanning arrays v Sensor signal processing doubles the scanning gauge bandwidth v Arrays of gauges give an expensive solution if more bandwidth is required v Scanning arrays are a scaleable solution to the bandwidth problem

34 34 MAP lecture, 2003 Hamilton Institute Multi-gauge scanning array

35 35 MAP lecture, 2003 Hamilton Institute Practical 2D Systems: Scanning Sensor Array Research System

36 36 MAP lecture, 2003 Hamilton Institute Multi-gauge scanning arrays v Calibration of sensors across the web/sheet done by special calibration transfer trick v Only one expensive gauge is required v Gauge technologies can be mixed (e.g. beta gauge and infrared) v Generalised sampling is applicable to multiple gauges

37 37 MAP lecture, 2003 Hamilton Institute Control Issues: Practical 2D Systems v Models and identification Sensors and sensor signal processing v Control (Courtesy of Honeywell)

38 38 MAP lecture, 2003 Hamilton Institute CD Profile Control Loop v The pursuit of better paper quality has placed new demands on Cross Directional (CD) control systems –smaller zone sizes –faster response –lower CD spreads CD ControllerCD Process

39 39 MAP lecture, 2003 Hamilton Institute Symptoms of a tuning problem… Sluggish Response Actuator Picketing Streaks Oscillations

40 40 MAP lecture, 2003 Hamilton Institute Tuning: just right!!! smooth paper! active, but not picketing

41 41 MAP lecture, 2003 Hamilton Institute sensors estimators controllers sensor signal processing actuation

42 42 MAP lecture, 2003 Hamilton Institute NEW DIRECTIONS: 2D Scanning Actuators v Consider Mass Deposition Processes –eg spray painting v Source of mass is spray gun that is moved over surface –manipulation usually done by robot v Aim to deposit specific mass profile on surface –for most applications, desired profile is uniform (ie flat)

43 43 MAP lecture, 2003 Hamilton Institute Scanning Actuators v Given footprint of mass flow rate from gun v What track should the gun follow over the surface to achieve desired mass profile? v Scanning actuator is dual of scanning sensor

44 44 MAP lecture, 2003 Hamilton Institute Raster Pattern v Results from 2D scanning theory tell you: –how close to put the tracks –how far off edges you need to scan to avoid edge effects Robot Path Part being Sprayed

45 45 MAP lecture, 2003 Hamilton Institute More Complex Paths v Generalised Scanning Theory also shows that this path is also valid v Path is suitable for thermal spray processes –aim to achieve specific temperature profile –more difficult problem because heat flows Robot Path

46 46 MAP lecture, 2003 Hamilton Institute Example of 2D Spray Actuation v SPRAY FORMATION OF METAL –Spray forming of metal as an alternative to casting –2D generalised sampling ideas from sensing are DUALISED to get dual results for actuation. –Metal is sprayed in a special pattern to optimise spray cast metal quality

47 47 MAP lecture, 2003 Hamilton Institute Benefits of spray-forming Reduced cost v Costs US$ 100Million to provide tooling for new car model Reduced time v Takes >18 months to produce tooling for big parts (bumpers, bonnets, door panels etc) v Freeze design long before production

48 48 MAP lecture, 2003 Hamilton Institute

49 49 MAP lecture, 2003 Hamilton Institute

50 50 MAP lecture, 2003 Hamilton Institute

51 51 MAP lecture, 2003 Hamilton Institute

52 52 MAP lecture, 2003 Hamilton Institute Typical sprayed steel flat

53 53 MAP lecture, 2003 Hamilton Institute 2D Spray Actuation v Painting. v Spray coating v Metal deposition v And many more For example………..

54 54 MAP lecture, 2003 Hamilton Institute And 2D Sensing again: Sub-sea profiling

55 55 MAP lecture, 2003 Hamilton Institute Acknowledgements v Greg Stuart of Honeywell: Greg supplied the information and slides of his profile control system. v Steven Duncan of Oxford University: Steven supplied slides of his 2D actuation systems v Final photograph from CropDusters


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