Ziqi Yang

24 Match, 2012

Iterative Learning Control of the Injection Stretch-Blow Moulding Process

1

Intelligent System and ControlSchool of Electronics, Electrical

Engineering and Computer Science

Queen’s University Belfast

Email: zyang06@qub.ac.uk

2

Outline

Introduction

Simulation

Identification

Control

Plan

3

1. Introduction – Blow molding

Extrusion blow molding

Injection blow molding

Stretch blow molding

4

1. Introduction – preform reheat

Temperature distribution inside and outside

each part from base to shoulder

5

2. Introduction – Stretch blow moulding

2. Simulation - Abaqus and Python

Abaqus – finite element analysis

Python – Abaqus based on Python

7

2. Simulation - Abaqus and Python

8

2. Simulation - Minitab and Main/Interaction effect analysis

4029175

4000

3000

2000

10001086

11010510095

4000

3000

2000

1000100500

Mass flow rate

Mean

Pressure

Temperature Timing

Main Effects Plot for Base VolumeData Means

1086 11010510095 1005005000

3000

10005000

3000

10005000

3000

1000

Mass flow rate

Pressure

Temperature

Timing

5172940

rateflow

Mass

68

10

Pressure

95100105110

Temperature

Interaction Plot for Base VolumeData Means

9

2. Simulation - Minitab and Main effect analysis

4029175

1140011250111001095010800

1086

11010510095

1140011250111001095010800

100500

Mass flow rate

Mean

Pressure

Temperature Timing

Main Effects Plot for Sidewall VolumeData Means

1086 11010510095 100500

11500

11000

10500

11500

11000

10500

11500

11000

10500

Mass flow rate

Pressure

Temperature

Timing

5172940

rateflow

Mass

68

10

Pressure

95100105110

Temperature

Interaction Plot for Sidewall VolumeData Means

10

2. Simulation - Minitab and Main effect analysis

4029175

7000

6000

5000

1086

11010510095

7000

6000

5000

100500

Mass flow rate

Mean

Pressure

Temperature Timing

Main Effects Plot for Shoulder VolumeData Means

1086 11010510095 1005008000

6000

40008000

6000

40008000

6000

4000

Mass flow rate

Pressure

Temperature

Timing

5172940

rateflow

Mass

68

10

Pressure

95100105110

Temperature

Interaction Plot for Shoulder VolumeData Means

11

3. Molde

RBF

0 20 40 60 80 100 120 140-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Number of simulation

norm

aliz

atio

n da

ta o

f Bas

eVol

ume

RBF BaseVolume model figure

measuredmodel prediction

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4. Control - Iterative Learning Control

ILC can fast achieve perfect tracking in a repetitive mode process

ILC have good performance in non-linear systems

ILC is a mode-free control method which is low degree of

dependence on model accuracy

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4. Control - Iterative Learning Control

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5. Plan for next step

Build model by Gaussian process

Combine fuzzy logic control with ILC

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