STREAM-0D: a new vision for Zero-Defect Manufacturing

STREAM-0D: A NEW
VISION FOR ZERO-
DEFECT
MANUFACTURING
March 27th, 2020
Simulation in Real Time for
Manufacturing with Zero Defects
José R. Valdés,
ITAINNOVA

KEY DATA
• Project title: Simulation in Real Time for
Manufacturing with Zero Defects
• Topic: Factories of the Future - Strategies at system
level for multi-stage manufacturing
• 10 partners
• Total budget: 5.186.110 €
• Total EU contribution: 4.159.145 €
• Duration: 42 months (Oct. 1st 2016 - Mar 31st 2020)

• Powerful
• Accurate
• Complex
• Computationally
expensive
• Unable to run in real
time
PHYSICS-BASED MODELS

Knowledge-based models
(FEM, CFD, …)
?

• Mathematical method
• Applied to PB models
• Parametric functions
• As accurate as the base
model
• Real time
REDUCED ORDER
MODELLING
Output KPIs=f(input parameters)

3
KPI Targets
ROM
OPTIMISED
PROCESS
PARAMETERS
Data –sets &
data analysis
MODEL
RECALIBRATION
Knowledge-based models
(FEM, CFD, …)
Real-time model
ZERO-DEFECT PRODUCT
FULFULLING TARGET KPIS
Data-driven models
DATA

Online data
acquisition
systems Real-time
simulation
model
Adaptive
control
Prediction of KPIs based on the
so-far measured data
Critical data
fed into model
Critical data (process or
component parameters)
capturedonline
Optimisation of process
parameters to adjust KPI to
its design value
KPI Targets
• Adjust processes in
real time, through
smart decisions based
on the forecast of
simulation models
running in real time
• Absorb the effect of component variability by adjusting process variables
• Increase process flexibility
–setting design targets online
–customizing batches of units
–reducing down time to
change design specifications

End user cases
▪Braking actuation
units (Booster)
▪Gliwice (Poland)
▪Tapered roller
bearings
▪ Zaragoza (Spain)
▪Body / door
seals
▪ Logroño
(Spain)

• Physics-based simulation
• Model Order Reduction
• Innovative in-line measurements &
virtual sensors
• Optimisation algorithms & adaptive
control
• Cloud & Model recalibration
• Data-based models
Key Technologies

Impact delivered
• Exploit the full potential of simulation in production processes
• Use Digital Twins for quick adaptation of process lines
• Monitor and adjust the process in real-time → ZD in selected
product performance indicators
• Reduce rejections by 10%
• Decrease adjustment time by 30% → increase production
flexibility
• Reduce production costs by 15%
• Increase production rates by 15%

NEW APPROACHES
AND
OPPORTUNITIES
TOWARDS ZERO-
DEFECT AND SMART
MANUFACTURING
March 3rd, 2020
Wroclaw University of
Science and
Technology
THANK YOU FOR
YOUR ATTENTION!
STREAM-0D has received funding from the European Union’s
Horizon 2020 research and innovation programme under grant
agreement No 723082.

STREAM-0D: A New
Vision For Zero-
Defect Manufacturing
March 27th, 2020
Application of Digital Twins in the
Production of Automotive Components
Alberto Ortega,
Business Development at ITAINNOVA

Two different approaches to
create a digital twin:
Based on historical process data
Based on data obtained by simulation

DIGITAL TWIN
based on historical process data

NOK parts data
introduction
Machine parameters acquisition
Server

Server
OEE performance
Process parameters visualization

Analysis of influences between parameters
Mathematical models

PREDICTIVE
MONITORING
MODELS
TRAINING&
EVOLUTION
MODELS
ADITIONAL SENSORS
MACHINERY
DATA
OPERATOR
REVIEWS
QUALITYLAB
INSPECTIONS
DATAGATHERING&STREAMING
DATA
DATA
CAPTURE
DATADATA MODELS MODELS
DATA LAKE

DIGITAL TWIN
based on data obtained by simulation

PROCESS PARAMETERS SELECTION
SIMULATION RUNS

FOAMING DEGREE
VULCANIZATION DEGREE
DIMENSIONS
Foamed
rubber Foamed
rubber
Roll
surface
Rubber 1
Rubber 2
Glass
fibre
Metal
band

New CAELIAs in the future:
- Blow molding
- Rubber injection

FOAMING DEGREE
VULCANIZATION DEGREE
DIMENSIONS
PROCESS PARAMETERS SELECTION
SIMULATION RUNS

BENEFITS OF SIMULATION
Valid information before start of production
Exploration of all range of process values
No need of sensorization until step 5

DIGITAL TWIN
Combining historical and simulation data

SIMULATION DATA HISTORICAL DATA

THANK YOU FOR
YOUR ATTENTION!
March 3rd, 2020
Wroclaw University of
Science and
Technology
STREAM-0D has received funding from the European Union’s
Horizon 2020 research and innovation programme under grant
agreement No 723082.

STREAM-0D: A NEW
VISION FOR ZERO-
DEFECT
MANUFACTURING
March 27th, 2020
Sensoring and Data-Based Models
for Defect Prevention in
Automotive Production
Alexios Papacharalampopoulos,
LMS

Contents
1. Zero Defect requirements for data
2. Data acquisition and challenges
3. Data Management & Visualization
4. Data connection with other modules (i.e. ROMs)
5. DDMs (low TRL)
6. DDMs (high TRL)
7. Towards Dig. Twin
8. Conclusions

https://www.researchgate.net/publication/332535519_Zero_defect_manufacturing_state-of-the-
art_review_shortcomings_and_future_directions_in_research/figures
https://www.stream-0d.com/blog/industry40-zdm-new-industrial-era/
What ZDM means

Data Level Format Level Processing Level
Interoperability

Suitability of models
Alexios Papacharalampopoulos, Panagiotis Stavropoulos, Demetris Petrides, Katerina Motsi (2019) Towards a Digital
Twin for Manufacturing Processes: applicability on laser welding, 12th CIRP Conference on Intelligent Computation in
Manufacturing Engineering, Gulf of Naples, Italy

DDM of low TRL #1
Alexios Papacharalampopoulos, et al, CMS Conference 2020

DDM of low TRL #2
Alexios Papacharalampopoulos, et al, CMS Conference 2020

Using DDMs
Papacharalampopoulos, A., Petrides, D., & Stavropoulos, P. (2019). A defect tracking tool framework for multi-process
products. Procedia CIRP, 79, 523-527.

DDM of high TRL #1
• Objective: Forecasting 4 Key Perfomance Indicators (KPIs) defining the quality of the end
product.
• Inputs: 13 Input Variables from two production lines representing Machine Parameters and
Measurements.
• Data: In total ∼3.500 observations are included for the DDM development purposes.
• AI/ML Models: Random Forests (RTs), Gradient Boosted Machines (GBMs), Support Vector
Machines (SVMs).
• Challenges:
CH1: AI/ML methods need thousands of data to function properly.
SO1: RTs, GBMs can cope with fewer data. Use Feature Engineering techniques to extract meaningful
features from the data
CH2: Some input features are too noisy.
SO2: Principal Component Analysis is used to remove noise from the data.
• Verification:
1. Open-Loop procedures: Test the ability of the DDMs to adopt in a real production environment.
2. Close-Loop procedures: Implement the DDMs in the production process.

DDM of high TRL #2
• Objective: Forecasting 6 Key Perfomance Indicators (KPIs) representing the geometry
of the end product.
• Inputs: 10 Input Variables from two production lines representing Machine
Parameters and Measurements.
• Data: In total ∼55.500 observations are included for the DDM development
purposes.
• AI/ML Models: Random Forests (RTs), Gradient Boosted Machines (GBMs), Support
Vector Machines (SVMs).
• Challenges:
CH1: Matching input variables with the 6 KPIs measurements (Outputs).
SO1: Carefully designed procedures for data pairing considering discontinuities in the production.
CH2: Select those input variables that have the biggest impact in the production process. This process
requires to understand how the DDMs works.
The Problem! AI/ML methods are function as black-boxes.
SO2: Use advanced meta-analysis methods, such as SHAP (SHapley Additive exPlanations) to extract
knowledge from the DDMs.
• Verification:

DDM of high TRL #3
• Objective: Forecasting 4 Key Perfomance Indicators (KPIs) defining the quality of the end
product.
• Inputs: 9 Input Variables from two production lines representing Machine Parameters and
Measurements.
• Data: In total ∼450.500 observations are included for the DDM development purposes.
• AI/ML Models: Random Forests (RTs), Gradient Boosted Machines (GBMs), Support Vector
Machines (SVMs), Convolutional NNs (CNNs), Long Short-Term Memory Recurrent NNs (LSTM-
RNNs)
• Challenges:
CH1: Matching input variables with the 4 KPIs measurements (Outputs).
SO1: Carefully designed procedures for data pairing considering discontinuities in the production.
CH2: Select those input variables that have the biggest impact in the production process. This process
requires to understand how the DDMs works.
CH3: Some input features are too noisy.
SO2: Principal Component Analysis is used to remove noise from the data.
• Verification:

Virtual line & Digital Twin
Control
Optimization
Calibration
What if
scenarios

• Main problem of data is interoperability
• Second problem is the usefulness
• Physics provide intuition and knowledge
• DDMs handle uncertainty
• Digital Twins is an umbrella term requiring diverse technologies
Conclusions

STREAM-0D: A NEW
VISION FOR ZERO-
DEFECT
MANUFACTURING
March 27th, 2020
Instantaneous optimization of
process parameters
Alberto Landini,
STAM

• A strategy for process control taking advantage of ROM and
DDM capabilities
• Optimal process parameters computation through numerical
minimization algorithms
• Adaptation of ROM through periodic recalibration of unknown
parameters
• Prediction of production defects and correction as preventive
action
INTRODUCTION

STEP BY STEP APPROACH
1. From knowledge-based model to ROM
• Ability to run simulation in real-time
• Complex processes can be simulated directly in-line

2. Data acquisition system
• All ROM parameters are measured in-line
• It enables real-time simulation with ROM
• Identification of controllable and non-controllable parameters
ROM parameters Description Machine Production line channel(s)
d Inner diameter diametroInterior metrica_3
d1 Outer diameter Arana_Z1 metrica_9
M Cone height Arana_Z1 metrica_7
B Raceway height Arana_Z1 metrica_8
T Part temperature Arana_Z1 metrica_19

3. KPIs definition
• Definition of the final product quality targets
• Development of algorithm to extract KPIs features from ROM

4. Development of the optimization algorithms
• Cost function that describes deviation from desired target
• Process parameters boundaries to avoid unfeasible solutions
• Iterative numerical minimization algorithms
𝑐𝑜𝑠𝑡 = (෍
𝑖=1
𝑁
𝑃𝑖 − 𝑃𝑖 𝑡𝑎𝑟𝑔𝑒𝑡 ∗ 𝑤𝑖)
𝑠. 𝑡. : 𝑥𝑖 𝑚𝑖𝑛 ≤ 𝑥𝑖 ≤ 𝑥𝑖 𝑚𝑎𝑥
𝑦𝑖 𝑚𝑖𝑛 ≤ 𝑦𝑖 ≤ 𝑦𝑖 𝑚𝑎𝑥

5. Optimization module implementation
• When process parameters change, new optimal set-point for
control parameters are computed
• Error is predicted and corrected before it occurs

5. Virtual line simulation: No optimization
0,04
0,06
0,08
0,1
0,12
Time
Non-controlled parameter measurement
0,3
0,31
0,32
0,33
0,34
0,35
0,36
0,37
0,38
1
69
137
205
273
341
409
477
545
613
681
749
817
885
953
1021
1089
1157
1225
1293
1361
1429
1497
1565
1633
1701
1769
1837
1905
1973
2041
2109
2177
2245
2313
2381
2449
2517
2585
2653
2721
2789
2857
2925
2993
3061
3129
3197
3265
3333
3401
3469
3537
3605
3673
3741
3809
3877
3945
Control parameter setpoint

5. Virtual line simulation: No optimization
0,04
0,06
0,08
0,1
0,12
Time
-20
0
20
40
60
80
100
120
140
°C
Time
End of line part temperature error

5. Virtual line simulation: Optimization
0,04
0,05
0,06
0,07
0,08
0,09
0,1
0,11
Time
0,3
0,32
0,34
0,36
0,38
Time
Control parameter set point

-80
-60
-40
-20
0
20
40
60
80
100
120
140
°C
Time
End of line part temperature error
5. Virtual line simulation: Optimization
0,3
0,31
0,32
0,33
0,34
0,35
0,36
0,37
0,38
Control parameter set point

6. ROM Recalibration
• Some ROM parameters cannot be measured in-line and need
to be estimated
• Historic data is used to calibrate their value
• Runs on a dedicated STREAM-0D cloud database

7. DDM to complete process information
• DDM can describe different features than ROM
• DDM can highlight specific plants variability not described by
physic-based models
• Improvement of STREAM-0D solution implementation in multiple
plants

8. Complete STREAM-0D control scheme

STREAM-0D SYSTEM
IMPLEMENTATION
Extrusion IR oven MWoven
Convective
oven
After
treatments
Cloud database
Industrial line
Industrial PC
Optimization
Decision-making
User Interface
Recalibration

STREAM-0D: A New
Vision For Zero-
March 27th, 2020
Cloud Implementation and
Recalibration
Giulia Barbano,
Integrated Environmental Solutions

March 27th, 2020 | STREAM-0D: A New Vision For Zero-Defect Manufacturing - Webinar 2
https://www.iesve.com/icl

iSCAN core
1-sec
database
storage and
analysis
streaming
data
batching
1-sec
resolution
API
(sub)hourly
alarms
iSCAN for industry 4.0

Get information out of dataData management
•Industrial users have multiple lines and data sources; complex architectures to track the whole production
•iSCAN normalises & collects data across existing storage (e.g. SCADA), fetch via streaming sources (e.g. MQTT)
•Information visualized through dashboards: view production status securely anywhere in the world
Analyse the whole processForecasting
•The industry 4.0 paradigm relies on using data for zero defect manufacturing
•iSCAN allows to run analytics and data checks on models to detect deviations before they happen
•Automated embedded analytics + runs of forecasting models via API
Make decisions and implement themPredictive control
•Based on forecasting, industrial users need to implement line changes, manually or automatically
•iSCAN stores analytics results and sends warnings & status messages via dashboard (for manual control)
•When a condition is true, iSCAN can send custom data to a web service (for automated control)

recalibration
...ROM based...
how to keep
ROM reliable?

Identify likely values for parameters
in the ROM
(i.e. values which are not set or
measured from the line directly -
assumptions)
Use recalibration on these
parameters: the ROM
continues to accurately
represent the process it is
modelling

ROM
recalibration
process
Identify set / measured data from line, and outputs comparable to ROM outputs
Identify parameters for recalibration
Make sure ROM has explicit recalibration parameter(s)
Carry out sensitivity analysis for ROM parameters
Identify targets, thresholds, triggers for recalibration
Define cost functions for optimisation
Implement ROM parametric optimisation script
Implement triggers for recalibration
Automate recalibration
Store results in cloud DB

STREAM-0D: A NEW
VISION FOR ZERO-
DEFECT
MANUFACTURING
March 27th, 2020
Standard Profil: Optimisation of Rubber
Extrusion Processes in Car Sealing
Production
Salvador Izquierdo,
ITAINNOVA

The product
▪ Body / door seals made of rubber
▪ Logroño (Spain)

Performance
▪ Quality specifications
▪ Critical lengths definition
▪ Vulcanization degree

Manufacturing process
Convective
oven
After
treatments
Profile
check
Profile shape
Vulcanization
degree
Temperature profile (control loops)

Manufacturing process
Convective
oven
After
treatments
Profile
check
Profile shape
Vulcanization
degree
Temperature profile (control loops)
Extrusion
speed
IR Power Gas mass flowMW PowerExtrusion T

STREAM0D solution
Coupled temperature-displacement transient analysis
Ambient
• Gravity
• SFilm
(convection)
• 3.5 s
IR Oven
• Gravity
• Surface flux
• 2 s
Ambient
• Gravity
• SFilm
(convection)
• 2.75 s
Microwave Oven
• Gravity
• Volumetric flux
• 22.45 s
Ambient
• Gravity
• SFilm
(convection)
• 0.8 s
Gas Oven
• Gravity
• SFilm
(convection)
• 53 s
Cooling
(ambient)
• Gravity
• SFilm
(convection)
Ambient1
IR-Oven
Ambient2
Micro-
Oven
Ambient3
Gas1-
Oven
Bath
Ambient4
Gas2-
Oven
Close
profile
Ambient5
Length (m)
1.35
0.00
2.25
3.30
12.30
12.65
33.85
39.85
51.85
85.85
86.85
Initialtemperature
Ambient:Convection h(v,T) + Tambient
Superficialflux
Volumetricflux
Convection:
h(v,T)+Tgas1
Convection:
h(v,T)+Tgas2
Convection:
h(v,T)+Tbath
Convection:
h(v,T)+Tambient
All:Gravity+ Pressure in cavities
Displ.Rollsurface Extrusiondirection
Roll surface
Rubber
Rubber+BA
Metal
band

STREAM0D solution
Vulcanization Foaming
Temperature
time
Verification of material model

STREAM0D solution
ROM interface

Scaling STREAM0D
▪ Current development time
for each profile is 1 week.
▪ This is within characteristics
set up times for the line.

STREAM-0D: A New
Vision for Zero-
March 27th, 2020
ZF: Minimising the Variability of
the Pedal Feel in Car Brake
Boosters
Leticia Gracia,
ITAINNOVA

2
The Product
Control valve
Input Rod
Valve Body
Reaction disc
Output Rod
Booster shells
Rubber Diaphragm
& Diaphragm plate
Poppet
Primary plunger
Master Cylinder

3
Positive gap Gap = 0 Negative gap (penetration)
The Performance
Jump-in

3March 27th, 2020 | Wroclaw University of Science and Technology
Control Unit
Assembly Process

7
Simulation Model
1
2
Length to
adjust by
crushing
Ratio disc
Valve body
Output
rodReaction
disc
Item n. 32482313
Item n. 32481774

10
HBubble Station
STREAM-0D Approach:
C10
C01
n

11
STREAM0D-Solution
Specific Control Loop Layout

12
STREAM0D-Solution
Process relevant parameters

14
STREAM0D-Solution
Control Module

15
STREAM0D-Solution
1- Optimization Loop

16
STREAM0D-Solution
2- Optimization & Recalibration loop

21/05/2020
EXPERTS
IN BEARING
SOLUTIONS

21/05/2020
88M€
FORECASTED
SALES 2020
4,5%
R&D
INVESTMENT*
530
FTE
OUR NUMBERS
15%
INTERNET OF
THINGS
INVESTMENT**
*% total sales 2019
**% total CAPEX 2019
LOCAL
FACILITIES
IN 7 COUNTRIES
INDUSTRY
4.0
EU financed
project

21/05/2020
Tapered roller bearing · Needle roller bearings · Wheel end hub units
Ball Bearings · Cylindrical roller bearings · Passenger car bearing kits
FULL RANGE FOR EACH
APPLICATION

21/05/2020
Z0
Z1
PRESET LINE
Z2
Z3
• Heavy duty TRB mid size < 200 mm OD.
• Wheel HUB bearings Bus & Truck
• Capacity 2020 : 2,500,000 pcs / year
FERSA ZARAGOZA

21/05/2020
4.0 TECHNOLOGY
AUTONOMOUS
ROBOTS
INTERNET
OF THINGS
REAL TIME
MONITORING
BIG DATA &
ANALYTICS
SYSTEM
INTEGRATION
ARTIFICIAL VISION
ADVANCE QUALITY
CONTROLS

21/05/2020
SEE IT
NOW
H2020 EUROPEAN PROJECT

21/05/2020
Retos 2016
• Measuring in real time raw
material through of the pre-process
device
• Measuring in real time finish good
material through the post-process
device
• Adjust measurement due to
temperature
• Send information to grinding device
to optimize process in real time
Fersa Bearings
• Increase process capability
• Optimize our process with the
necessary grinding cycle time for
every single part.
• Scrap and rework reduction due to
the process stability and the
robustness of the process against
changes of the raw material
• Improve product stability and quality
OBJECTIVES

21/05/2020
Inner Ring 1 - 2
Rib (M)
Inner Ring 1 - 2
Raceway (d1)-Flange Ø (A)
ROLLERS
CAGE
Outer Ring
Raceway (D1)-Seal
Outer Ring
Height (C)
Inner Ring
Height (B)
Inner Ring 1 - 2
Inner Ø (d)
Height Z1 - GRINDING
Z1-D1
Z1-d1 Z1-M Z1-d
Inner Ring 1 - 2
Raceway (d1)
Outer Ring
Raceway (D1)
Z1 - HONING
HONING OR
HONING IR
Z1 - ASSEMBLY
Outer Ring
Outer Ø (D)
Z1-D
Inner Ring TiInner Ring Assy Bearing assy
Rollers and Cage assy Ti T
Marking and
Packaging
Z1-D1
Outer Ring Te
Te
Post-processPre-process
Post-process
PROCESS

21/05/2020
MEASURING EQUIPMENT
• Automatic Quality Station in line flow
• Toolingless measurement: M, d1, B
dimensions
• Temperature measurement adjustment
• PLC communication to server

21/05/2020
Entry of workpieces
Station1: Measuring Temperature,B
and d1Automatic Calibration by
master of each Station
Thermocouple: Probe for high performance surfaces
with the tip encapsulated in silicone to measure the
workpiece temperature by contact in the first station of
the machine previously to measuring in the Ecuator
Gauging system. (Brand SKF TMDT 2-43, Max.
Temperature 300 ° C / 570 ° F and response time 3 sec. )
Gauging System: Ecuator Gauging System Measuring
B (high), M (flange) & d1(raceway) in real time in
the second station of the machine. (Model Renisaw
Ecuator 300). With this measurement system we can
measure any other part of the bearing that we want,
O.D, Bore, etc… with 1 micron of precision.
Flange (M)
Grinding
machine.
Nova 10
Raceway
(d1)
Grinding
Machine.
Nova 9
Pre process with
I.R. Flange machine
MEASURING EQUIPMENT

21/05/2020
ROM OF TEMPERATURE INFLUENCE IN
BEARINGS DIMENSIONS

21/05/2020
Data Driven Model 1: Feedback bore diameter N-11
DATA DRIVEN MODELS

21/05/2020
DATA DRIVEN MODELS
Data Driven Model 2: Pre-process flange N-10 and RIFA 2

21/05/2020
USER INTERFACE TO MONITOR AND
CONTROL
Monitoring:
• Temperature control of
manufacturing
• Rejection rates
• Dimension evolution
• Machine parameters control
Analysis& Control:
• Hystorical data
• System alerts to control
process
Simulation:
• Dimension prediction based on
machine parameters,
environmental conditions and
raw material dimensions

21/05/2020
FROM HIGH
PRECISSION TO
CUTTING EDGE
MANUFACTURER

STREAM-0D: a new vision for Zero-Defect Manufacturing

Recommended

Recommended

More Related Content

Similar to STREAM-0D: a new vision for Zero-Defect Manufacturing

Similar to STREAM-0D: a new vision for Zero-Defect Manufacturing (20)

Recently uploaded

Recently uploaded (20)

STREAM-0D: a new vision for Zero-Defect Manufacturing