Presentation 20120324 - ziqi yang
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Ziqi Yang 24 Match, 2012 Iterative Learning Control of the Injection Stretch-Blow Moulding Process 1 Intelligent System and Control School of Electronics, Electrical Engineering and Computer Science Queen’s University Belfast Email: [email protected]
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Transcript of Presentation 20120324 - ziqi yang
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: [email protected]
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
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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
13
4. Control - Iterative Learning Control
14
5. Plan for next step
Build model by Gaussian process
Combine fuzzy logic control with ILC
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PECBadminton Night
