RobustPlaNet— Shock-robust Design of Plants and their Supply Chain Networks

Project acronym: RobustPlaNet

Call and Contract: FP7-2013-NMP-ICT-FOF(RTD)

Grant agreement no.: 609087

Project Duration: 01.10.2013 – 30.09.2016

RobustPlaNet: Shock-robust Design of Plants and their

Supply-Chain-Networks

FoF Impact @CRIT

10.11.2016, Vignola (MO)

Presenter: Prof. Marcello Colledani, Department of Mechanical Engineering, Politecnico di Milano, Italy

Manufacturing @ MECC

2The net of the Politecnico di Milano

2

Key Figures

38.000 students:

Engineering: 24.000 (15% of Italian engineers)

Architecture: 10.000

Design: 4.000

12 Departments, 25 PhD programs, 35 MS programs.

Ranked in the EU top 10 for Mechanical, Aeronautical &

Manufacturing Engineering (QS 2013).

• Dynamics of Mechanical Systems

• Vehicles

• Machine Design

• Manufacturing Technologies and Systems

• Advanced Materials

• Measurements and Experimental Techniques

• Methods and Tools for Product Design.

Department of Mechanical

Engineering

3

De-manufacturing Plant: Teaching / Research Factory

A Pilot Plant for the remanufacturing and recycling of PCBs (Printed Circuit

Boards) from mechatronic automotive components, is being designed and

currently under installation at ITIA-CNR (January 2013). Project Funded by

Regione Lombardia: 1.5 Million Euro.

Cell 1: Disassembly

Cell 2: ReworkingCell 3: Recycling

FP7. 2011-2014. Innovative Proactive Quality Control System for in-process Multi-Stage Defect Reduction.

FP7. 2013-2016. Shock-robust Design of Plants and their Supply Chain Networks

H2020. 2015-2018. Customer-driven design of product-services and

production networks to adapt to regional market requirements.

H2020. 2015-2017. CSA. Factory of the Future Clusters.

FP7. 2011-2015. Remote Laser Welding System Navigator for Eco &

Resilient Automotive Factories

• Platform for early stage design of RLW automotive assembly lines.• Knowledge based engineering of automotive assembly lines.• Multi-objective optimization of manufacturing systems.

• Integrated Quality and Production Logistics Analysis of Production Systems.• Defect propagation avoidance solutions for ZDM at system level.

• Integrated product and production systems evolution.• System level opportunistic maintenance policy optimization in manufacturing lines.• Reconfiguration of automotive body assembly systems.• Remanufacturing decision support system.

• Ontologies for product-process-system information formalization.• System engineering, visualization and animation.• Plant capability assessment tools.

• Project clustering for exploitation, standardization and dissemination. • Roadmapping on Zero Defect Manufacturing.

H2020. 2015-2018. Rapid Reconfiguration of Flexible Production Systems • System engineering and reconfiguration planning.• Mechatronic object description and formalization.

FP7 and H2020 projects

Main objective

- Develop

“technology-based business approaches”

- To support

“collaborative and robust production networks”

- With the goal of

“Timely delivering innovative product-services,

In very dynamic and

unpredictable global environments”

Current situation RobustPlaNet solution

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3 Demonstrators

5

Challenges

RobustPlaNetShock-robust Design of Plants and their Supply-Chain-Networks

Impacts

Consortium

Approach

Academic Partners

Propagation of disturbances and risks

Volatile business environment and increasing globalization

Hierarchical supply chain and product-based business models

Industrial Partners

6

Increase production system resilience to unexpected disturbances

Increase service level

Achieve Collaborative and robust Production Networks

Link Application Domains to Use Cases

7

ReconfigurationRobust Planning and Scheduling

Opportunistic Maintenance

Use Case 2 Use Case 3Use Case 1

Supply Chain

OMA, ITC, FESTO, MAR, MCM, KIT, POLIMI

KNORR, ITC,MAR, MCM, SZTAKI, POLIMI

VPN, MCM, FESTO, ITCSZTAKI, POLIMI, TWENTE

SZTAKI, KIT, POLIMI, DAI, FESTO, MAR, MCM, TWENTE, VPN, SZTAKI, KNORR, ITC

Use Case 4

Remanu-facturing

ReconfigurationRobust Planning and Scheduling

Opportunistic Maintenance

Use Case 2 Use Case 3Use Case 1

Supply Chain

OMA, ITC, FESTO, MAR, MCM, KIT, POLIMI

KNORR, ITC,MAR, MCM, SZTAKI, POLIMI

VPN, MCM, FESTO, ITCSZTAKI, POLIMI, TWENTE

SZTAKI, KIT, POLIMI, DAI, FESTO, MAR, MCM, TWENTE, VPN, SZTAKI, KNORR, ITC

Use Case 4

Remanu-facturing

ReconfigurationRobust Planning and Scheduling

Opportunistic Maintenance

Use Case 2 Use Case 3Use Case 1

Supply Chain

OMA, ITC, FESTO, MAR, MCM, KIT, POLIMI

KNORR, ITC,MAR, MCM, SZTAKI, POLIMI

VPN, MCM, FESTO, ITCSZTAKI, POLIMI, TWENTE

SZTAKI, KIT, POLIMI, DAI, FESTO, MAR, MCM, TWENTE, VPN, SZTAKI, KNORR, ITC

Use Case 4

Remanu-facturing

8

Maintenance use case: OMA

• Privately held Italian aerospace Company

• Established in 1950

• Located in Foligno (150 Km North of Rome)

• Currently 600 employees (40 in the Design & Development

Department and 20 in the Production Engineering)

• Revenues in the range of 70 Million Euro

• website: www.omafoligno.it

Officine Meccaniche Aeronautiche – Foligno, ITALY

9

OMA Use Case: Manufacturing of complexaeronautics parts.

Product features:Critical structural titanium parts.Small lots, large variability parts.Complex shape.

System and process features:3 cutting tools storage units (400 tools each).8-20 cutting hours.80% of material is removed.

Specific Challenges: • Service level strongly affected by corrective maintenance operations.• Extended tool-life planning times by pre-process experiments (130 hours).• Conservative and expensive tool management policy (no condition monitoring).

10

RobustPlaNet opportunistic maintenance solution and architecture

Machine degradation

monitoring by fingerprint

cycles (identification of

5 degradation modes).c

State-based production

and opportunistic

maintenance planning

(+ 20% service level).Production

plan

Tool condition monitoring

by data fusion of process

and in-process tool wear

data (-70% tool-life

planning time, 40

hours).

11

Multi-sensor data fusion for tool wear monitoring

What does it do?

In-situ measurement of single cutter radius (proxy of tool wear)

In-process monitoring of torque/power signals via GEM system (proxy of tool wear)

Chip thickness simulation via Vericut for torque/power signal normalization

FUSION

In line procedure for tool

wear monitoring and

optimization of

replacement strategy.

05/09/2016

RobustPlaNet 36M Final Meeting at OMA12

Fingerprint analysis for machine degradation monitoring

What does it do?

• Automatic execution of fingerprint cycle via the jFMX Supervisor (current frequency: twice per week)

• Duration: 33 minutes

• Cycles divided into eight sections, i.e., sequences of operations

RPN-FP-CYCLE_V2.PRG file ;==============================================================================

; RobustPlaNet OMA Use-Case

;

; Machine Fingerprint cycle

;------------------------------------------------------------------------------

; Authors: Vittorio Lorenzi, Roberto Galilei, Luigi Calegari - MCM

; Version: V2 10/02/2016

;------------------------------------------------------------------------------

;

; GEM Parameters

; --------------

; H20 = Program Version

; H21 = Program Section

; H22 = Axis or Spindle Speed

;

; Sections

; --------

; H21 = 99 Initial Axes positioning

; Feed = 5000

; H21 = 1 X Axis incremental positioning with inversion

; From X = -800 to X = +800

; 5 Steps = +400/-100, Feed = 500

; From X = +800 to X = -800

; 5 Steps = -400/+100, Feed = 1000

; From X = -800 to X = +800

; 5 Steps = +400/-100, Feed = 2000

; H21 = 2 Y Axis incremental positioning with inversion

; From Y = +90 to Y = +1390

; 6 Steps = +300/-100, Feed = 500

; From Y = +1390 to Y = +90

; 6 Steps = -300/+100, Feed = 1000

; From Y = +90 to Y = +1390

; 6 Steps = +300/-100, Feed = 2000

; H21 = 3 Z Axis incremental positioning with inversion

; From Z = +80 to Z = +1490

; 6 Steps = +305/-100, Feed = 500

; From Z = +1490 to Z = +80

; 6 Steps = -305/+100, Feed = 1000

; From Z = +80 to Z = +1490

; 6 Steps = +305/-100, Feed = 2000

; H21 = 4 B Axis incremental positioning with inversion

; From B = +0 to B = +360

; 3 Steps = +180/-90, Feed = 500

; From B = +360 to B = +0

; 3 Steps = -180/+90, Feed = 1000

; From B = +0 to B = +360

; 3 Steps = +180/-90, Feed = 2000

; H21 = 5 G17: Circular interpolation in XY plane

; Feed = 2000

; H21 = 6 G18: Circular interpolation in ZX plane

; Feed = 2000

; H21 = 7 G19: Circular interpolation in YZ plane

; Feed = 2000

; H21 = 8 Spindle rotation at various Speeds

; Speed = 500/1000/3000/4000

;==============================================================================

; History of Changes

;------------------------------------------------------------------------------

; Date Change Authors

;------------------------------------------------------------------------------

; 28/12/2015 V1: Added GEM parameters and comments L.Calegari

;------------------------------------------------------------------------------

; 07/01/2016 V1: Comments update (Feed/Speed) V.Lorenzi

;------------------------------------------------------------------------------

; 08/01/2016 V1: Added tests Y, Z, B with Feed=2000 L.Calegari

;------------------------------------------------------------------------------

; 08/01/2016 V2: Test on X axis changed (same as Y, Z, B) L.Calegari

;------------------------------------------------------------------------------

; 13/01/2016 V2: All tests changed R.Galilei

; X from -800 to +800 L.Calegari

; Y from +90 to +1390

; Z from +80 to +1580

; B from +0 to +360

; Corrections on tests 5, 6 ,7

13

Fingerprint analysis for machine degradation monitoring

How it works?

Multi-sensor

signature of first

34 fingerprint

cycles

(4 months)

14

Fingerprint analysis for machine degradation monitoring

ResultsExample: Faulty cycle 1:

intermittent modification of

the feed - Z axis

(representative of an

anomalous glide along the

linear guide)

15

Integrated Maintenance and production planning

- Production orders

- Deliveries dates

- Machines assignments

Process chain model

Reliability

Machine speed

Buffer configuration

OptimizationOptimization

algorithms0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0

State model of the system

Service levels under

alternative policies

1 2 V

m1 m2 mV

D

mD=0

l1,2 l2,3

p1

p2

pV

Y1 Y2 YV

rCM

lV-1,V

l1,3

l1,V

l2,V

Initial component states

- When to produce

- When to maintain

- Completion times

Production Plan

11 21 31 41 51 61

12 22 32 42 52 62

M1

M2

d1

o1 o2 Ov-1 OvOx

p1,1p2,1 p3,1 p4,1 p5,1

λ1,2 λ2,3 λ3, 4 λ 4,5

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p3,2 p4,2 p5,2

r2d1

o1 o2 ov-1 ovox

p1p2 px pv-1 pv

λ1,2 λ2,x λx, v-1 λ v-1,v

r1

FA1 FA 2

PM

rFA1 rFA2

rPM

1/MTTD1 1/MTTD2

1/MTTDv-1 1/MTTDv1/MTTDx

11 21 31 41 51 61

12 22 32 42 52 62

Integrated production

and maintenance pan

Tardiness

Lot completion time

Production and Maintenance Mgr.

x

Evaluator

14.04.201616

OMA Use Case

17

RobustPlaNet Relevant Exploitation Stories

A list of 8 new exploitable products have been elaborated. The mostpromising business opportunities are as follows:

◦ MARPOSS will include the multi-sensor solution, including the laser pre-setter and data fusion algorithms, as a technology for the direct toolcondition monitoring in complex machining operations.

◦ MCM will include the sensors and the software for machine signaturemonitoring as an additional service to the machine tool users formonitoring the state of degradation of critical components.

◦ Technology Transfer Ltd. has been established in Hungary by SZTAKI,partially based on RPN results, to bring to the market the RPN cockpit.

◦ Teckno, a spin-off of Politecnico di Milano, established in cooperation withMCM, will exploit the technology and the software for the efficientplanning of the tool life in complex machining operations.

18

Robust production planning case

Test feed

Rework

ProdB

ProdC

Test

bu

ffe

r

FA1 FA1 FA1

Assembly

ProdA

WS1 WS2 WS6WS5WS4WS3

Test and rework Final assembly

0% 5% 10% 15%

1

5

9

13

17

21

25

Reject rates

0

2000

4000

6000

8000

10000

1 81

52

22

93

64

35

05

76

47

17

88

59

29

91

06

113

120

127

134

141

148

Yearly volume

Flexible, manual assembly line

Stochastic reject ratesDiverse yearly volumes

• More than 150 products variants with fluctuating customer orders

• Unbalanced utilization of the resources

• Stochastic effect (varying cycle times and reject rates, machine breakdowns)

• High number of diverse quality parameters causing rework (e.g. test failures, process times)

Is your plan

can be robust

enough?

19

Robust production planning case

Data gathering

• Combination of ERP (planning) and MES (shop-floor sensor) data

• Merge and process data to use in simulation models

Simulation analysis

• Import shop-floor data in the simulation model (stochastic effects)

• Run simulation experiment to project the behaviour of the system

Data analysis

• Process the simulation-provided data

• Build regression models to predict the real capacity requirements of production scenarios

Production planning

• Integrate regression models in mathematical optimization programs

• Optimize the production plan applying the regression functions

Machinery, deburring and surface treatmentFinal assembly

Simulation

Data analysis module with regression model

Production planning Lot-sizing and capacity planning

Inventory

Orders

qpt

Q(qt)

Lot sizes and headcounts:

xit ,wt

Control policies

Virtual production scenarios

Lot sizes and operator-machine assignments:

rojπ ,zmjπ Robust

planning

method

covering the

full intra-plant

process chain

Real-time data aggregation

Simulation data based

statistical learning

Optimization

20

Knorr Bremse use case

21

Remanufacturing at KB: Current Situation

Mechatronic modules for EBS.

“Premium quality remanufactured products are

set to play an even more important part in

Knorr-Bremse’s business... And so we are

bundling our remanufacturing expertise and

increasing our production capacities”.

Wolfgang Krinner, Member of the Executive

Board. [Source: Knorr-Bremse invests heavily

in remanufacturing business].

The current remanufacturing process is

carried out in a plant of 9.000 m2 in

Czech Republic, for 300 individual

product types.

After remanufacturing, the components

are re-assembled in Germany.

22

Knorr Bremse remanufacturing use case

Challenges: • Large variability in the condition of the post consumer products. • Remanufacturing decisions are taken by the operator based on Standard Operations Sheets – SOS. The resulting regeneration rate is 0.7.

Input Post consumer part

Visual Check by the operator

Operator Reman decision

Disassembly Cleaning

Final Inspection

Scrap

Good for reassembly

Remanufacturing of mechatronic parts for the automotive aftermarket as a sustainable circular economy business.

Process-chain:

14.04.201623

To propose a Decision Support System to adapt the disassembly

strategy in feed-forward mode, depending on the quality conditions of

the input post-consumer products.

Input post-consumer product

Automatic + Visual inspection

Decision Support System(DSS)

Part condition dependent optimal disassembly plan

Cleaning

Component Inspection

Scrap

Re-assembly

Final Testing

Scrap

Knorr Bremse remanufacturing use case

24

RobustPlaNet remanufacturing solution

• Classification of 60 post-consumer parts according to the following criteria.

• Complete disassembly of the test parts. Analysis of the task feasibility and times

and derivation of the disassembly precedence graph.

• Statistical analysis of the results by ANOVA and identification of significant

quality features:

• Age of the product.

• Level of Corrosion.

• Level of contamination.

Quality

Class

Quality Features Probability

Age Corrosion

level

Contamination

level

Q1 <2008 High Low 0.15

Q2 >2007 High Low 0.1

Q3 <2008 High High 0.25

Q4 >2007 High High 0.15

Q5 >2007 Low Low 0.2

Q6 >2007 Low High 0.15

z

x

y Closing plate

Piston

Case

Silencer

Solenoids

Fixing plate

PCB

Cap pneumatic connector

QualityClassi icationCriteria

Damageson

themainbody

Corrosionon

thescrews

Corrosionon

themainbody

Painted/

notPainedDirtiness

Manufacturing

Date

Scratch OntheFrame

OntheHoles

OntheFixing

Holes

OntheTop

Onthe

Length

D4:Very

Dirty

D3:Dirty

D2:Quite

Clean

D1:Clean

Product

number

Quantitativefeature

Qualitativefeature

Weight

Corrosion

on the

main body

Inputpost-consumerproduct

Automa c+Visualinspec on

OperatorRemanDecisionSupportSystem

(DSS)

Partcondi ondependentop maldisassemblyplan

Cleaning

ComponentInspec on

Scrap

Re-assembly

FinalTes ng

Scrap

Part inspection and classification

Optical Character Recognition

Hyperspectral Imaging System

Disassembly Planning

Quality Class of the part

under processing

Decision Support System(DSS)

Decision Support System - architecture

• Regeneration rate > 0.8

• Disassembly time reduction = -15%.

26

Year 2020 2021 2022 2023 2024 2025

Yearly additional turnover with Reman

Products due to the increase of

efficiency and robustness brought by

the RPN solution

0,50% 1,00% 1,50% 2,00% 2,50% 3,00%

Additional Cores Collected per Year

[Million Units] 0,18 0,53 1,05 1,75 2,63 3,68

Yearly Saving for End Consumer

[Meuro] 49,00 98,00 147,00 196,00 245,00 294,00

Cumulative Savings for End Consumer

[Meuro] 49,00 147,00 294,00 490,00 735,00 1029,00

Added value of RPN extended to the EU automotive remanufacturing industry.

RobustPlaNet remanufacturing solution for Circular Economy: Economic Impact Evaluation

De- and Remanufacturing Pilot Network

Pilot Idea: Application Domain

The main objective of the De-and Remanufacturing pilot network is to integrate amultidisciplinary set of advanced and innovative enabling technologies and digitalinnovations (TRL 7-8) and to exploit the regional Smart Specializations in synergic wayto offer services to European end-users, mainly manufacturing companies, to solvespecific sustainability-oriented problems related to their products.

The pilot network nodes will act as Innovation Hubs for Circular Economy, being anetwork of competence and technology centers and supporting future producer-drivenreplication at industrial scale (TRL 9).

De- and Remanufacturing Pilot Network

Strategy: demonstrating integrated innovative solutions and

de-risking private investments in Circular Economy

G7 Summit Declaration June 2015: The G7 Alliance on Resource Efficiency promotes Circular Economy, Remanufacturing and

Recycling as strategic actions.

At European level, the Commission has launched inDecember 2015 the strategic initiative “Closing theloop - An EU action plan for the Circular Economy”.

H2020 R&I projects under the Focus Area “Industry 2020 in the Circular Economy”, calls

CIRC, Spire and FOF, at TRL 6-7.

Applied Research - Techn. Validation - Demonstration - Ready to market

Lack of infrastructures that can demonstrate to industry

integrated circular economy solutions and business models,

de-risking the private investment.

These Innovation Hubs should act as “technology gateways" that

any business sector can use.

De-and Reman

pilot network

14.04.201629

RobustPlaNet remanufacturing solution: New exploitation routes – Vanguard Initiative

Regional/Cross-Regional Use

Case

Involved Regions

Turbine blades in wind energy

systems.

Basque Countries, Saxony, Lombardy,

Tampere

Heavy machinery components

remanufacturing.Tampere, Basque Countries, Lombardy

Automotive parts regeneration. Lombardy, Saxony

TLC and Electronics systems

recovery.Lombardy, Tampere

Automotive car body parts

reprocessingSaxony

Remanufacturing of e-motor Saxony, Lombardy

A detailed analysis of specific sectorial Use Cases has been performed, involving the related

industrial partners, in order to estimate the effectiveness of the Pilot Network business model.

Tampere

Scotland

Norte

Basque

Country

Saxony

Lombardy

Tampere

Scotland

Norte

Basque

Country

Saxony

Lombardy

14.04.201630

Region Technical contact Organization Email

Lombardy Marcello Colledani Politecnico di Milano marcello.colledani@polimi.it

Tampere Reijo Tuokko Tampere University of Technology reijo.tuokko@tut.fi

Norte Luis Carneiro INESC Porto / PRODUTECH Cluster luis.carneiro@inescporto.pt

Scotland Alison Hunter Scotland Europa alison.hunter@scotent.co.uk

Saxony Katja HaferburgFraunhofer Institute for Machine Tools and

Forming Technologykatja.haferburg@iwu.fraunhofer.de

Basque Country Ane Irazustabarrena Tecnalia ane.irazustabarrena@tecnalia.com

A document has been delivered to the Vanguard ESM

Steering Committee reporting the current configuration

of the Pilot Network, the operational Business Plan,

and the Governance of the Pilots.

For more information contact:

Prof. Marcello Colledani (Polimi): European

Coordinator of the Vanguard ESM “De-and

Remanufacturing” for Circular Economy Pilot Network.

RobustPlaNet remanufacturing solution: New exploitation routes – Vanguard Initiative

05.09.2016.31

Festo AG & Co. KG | General

Challenge: Product Range Complexity

North AmericaLatin AmericaEuropeAsia

Regional requirements

Customer requirementsApprox. 300,000 customers

4 main productgroups

30,000 catalogproducts

Million variants(customer specific)

Industrial requirements

Focus industries:• Automotive• F&B• ELA• Bio & Pharma• Industrial Water• End Line Packaging• Chemicals

13

23

32

Reconfigurable Supply Chain Networks Challenges and Objectives

201

52

01

52

01

52

01

52

01

62

01

62

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62

01

62

01

72

01

72

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72

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72

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82

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82

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82

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92

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92

02

02

02

02

02

02

02

02

02

12

02

12

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12

02

12

02

22

02

22

02

22

02

2

Global Local for Local

Business environment Alternatives

Status quo

Reconfiguration strategy

Sourcing / Production / DistributionFluctuations over time

Demand

Tariff barriers

Labour costs

…

CostsDelivery

reliability

Global Local

for

Local

Identification of an optimal reconfiguration strategy of the supply chain network of FESTO AG

05.09.2016.33

AS-IS situation |objectives

Design support for supply chain configurations

Identification of an optimal (re-) configuration strategy

Evaluation of configurations in an agent-based stochastic environment

Integrated decision-support for reconfigurable supply chain networks

Scope

Modular Toolbox

Objective Results

05.09.2016.34

Simulation

• Building supply network model dynamically

• Visualizing supply chain operations

• Evaluating optimized reconfiguration policies in a dynamic and uncertain environment

• Performing what-if analyses for decision support

05.09.2016.35

Details of the Demo | Results

Time - to-

serve

- 12%

Delivery

class

reliability

+ 10%

OEE

+ 3-5%

~40 analysed and

evaluated design scenarios

Action lists with

potential change enabler

Implementation plan

for reconfiguration process

Potentials

Enabler

Methodological Facts and Figures

Network level

Plant level

System level

Integrated

planning workflow

Generation and

analysis of

different network

scenarios

Integration of

changeability in

network planning

Multi-level

analysis tool for

identification of

disruptive

parameters

Academic dissemination Planned in DOW

Total by M24

Conference Papers 12 20

Journal papers 3 5

Successful PhD works 3 6 active

RobustPlaNetWorkshops, Special Tracks or events.

3 4

Courses Including RobustPlaNet R&D results.

4 3

36

RobustPlaNet Dissemination Events

Industrial dissemination events

• Constructeurdag 2014, NL

• Cluster Tecnologico Nazionale FabbricaIntelligente, Milan, IItaly

• Important EU industrial fairs: Fastener Fair, INDISTRIE, MSV NIITRA, INTEC

Planned demonstration events:

• Hanover Fair 2016.

• Industrial Technologies in Amsterdam 2016.

• Project workshop in Italy, September 2016.

Other activities

• Regular Newsletter.

• Linked page.

• Exploitation and valorization workshop.

Important academic events

• Special track at MIM 2016.

• CIRP General assembly 2015 & 2016.

Overview of scientific outputs:

Reconfigurability, flexibility and co-

evolution, i.e. frequent changes.

Quality performance after the system

ramp-up.

Increasing complexity:

• Product variants and features;

• Globally distributed suppliers.

• More demanding specifications.

• Additional burden on diagnostics

and root-cause analysis.

Customization and

Personalization of products.Small lot and one-of-a-kind

productions.

Sustainable Manufacturing. Material re-use and zero-waste.

Global trends

New inspection technologies. 100%, in-line inspections.

"Zero Defect Manufacturing" is an emerging paradigm aiming at going beyond

traditional six-sigma approaches in highly technology intensive and strategic

manufacturing sectors through knowledge-based approaches.

Challenges for the EU Industry

New ICT for Manufacturing. Need to manage big data.

The Zero-Defect Manufacturing Paradigm

SUSTAINABLE TRANSPORT

Mass production

of electrical drives

European E-mobility

Industry:

1.One of the most

important products (12M

units in EU27)

2. Foreseen to increase by

1020% in the next 5 years.

AGEING SOCIETY

Micro-intravascular

catheters production

European Medical Techn.

Sector:

1. Innovative industry.

2. 90 Billion Euros Market

in EU (33% of world

market) share.

3. Growing at a rate of 5-

6% per year.

CLEAN ENERGY:

WIND POWER

Precise large-part

machining of components

for the gearbox

European Windmill Sector:

1. Markets growth of

130.000 M€ by 2105

2. New higher power

producing windmills require

bigger components.

Addressed Markets and Industries

SOCIETAL CHALLENGES

Production of electric drives for the E-mobility sector.

Challenges:

• End-of-line product functionality

assessment (magnetic torque verification).

• Inefficient feedback on the processes.

• Need for in-line quality control.

Process Stages

M1: laminated stack

from suppliers loading.

M2: magnets assembly.

M3: stack magnetization

M4: heating station.

M5: rotor assembly.

M6: rotor balancing.

M7: marking.

Inspection Stage

MK: based on the

whole engine

magnetic moment.

ROTOR LINE

Conforming

parts

MK

Scrap

STATOR LINE

Mz

Mx

Mj

Mk

Mg

MK-1

ASSEMBLY

Magnets

M3

M2,1

M5

M4

M7

M1 M

6

B1,1

M2,2

B1,2

B2

B3

B4

B0,1

B0,2

Magnets

Quality Correlations

MK+1

Rework

Bosch – Electric Drives Assembly

Assembled rotor

and stator.

Solutions:

• New sensor for measuring the magnetic field

contribution of each magnet in each stack- > in-line

inspection stage.

• Prognosis model to relate the rotor magnetic intensity

distribution to the single stack magnetization profiles.

• New intelligent rotor assembly strategies (rotation

adjustment, selective assembly).

ROTOR LINE

Conforming

parts

Scrap

or rework

STATOR LINE

Mz

Mx

Mj

Mk

Mg

MK-1

ASSEMBLY

Magnets

M3

M2,1

M5

M4

M7

M1 M

6

B1,1

M2,2

B1,2

B2

B3

B4

B0,1

B0,2

Magnets

Quality Correlation

MK

BA

BB

BC

ROTOR LINE

Conforming

parts

Scrap

or rework

STATOR LINE

Mz

Mx

Mj

Mk

Mg

MK-1

ASSEMBLY

Magnets

M3

M2,1

M5

M4

M7

M1 M

6

B1,1

M2,2

B1,2

B2

B4

B0,1

B0,2

Magnets

Quality Correlation

MK

Bosch – Electric Drives Assembly

Bosch – Electric Drives Assembly

magnetization

too highmagnetization

too low find optimal

combination online

stack buffer rotor assembly

magnetic field of

complete rotor

within tolerances

magnetization

too highmagnetization

too low

clusters A,B,C

A

B

C

classification

of stacksexecution of

offline policy

rotor assembly

magnetic field of

complete rotor

within tolerances

Inspection

New tolerance-oriented assembly strategies

2- Selective Assembly

rotor within tolerances although single stacks show deviations

StrategyEEff

[parts/t.u]

ETot

[parts/t.u]

Ysystem ∆% EEff vs.

Baseline

Base line 0.5752 0.6729 0.85 -

Optimized rotor assembly 0.6704 0.6704 1.00 +16.55%

Selective Assembly 0.6261 0.6726 0.93 +8.85%

1- Optimized Assembly

No Classes 2 Classes 4 Classes 6 Classes 8 Classes0

0.5

1

1.5

2x 10

4

No of ClassesV

ariance o

f A

ssem

ble

Magnetization

Assembly Variance (Integral Magnetization)

Demonstrator at Bosch GmbH- Stuttgart, Feuerbach

Inspection device.

Rotor optimization

software tool.

Optimal rotor

assembly station.

Integrated Zero Defect Manufacturing Solution for High Value Adding Multi-stage Manufacturing Systems

Consortium

Clustering Initiatives: 4ZDM and Focus

www.4zdm.eu

Common targeted sectors & market

applications Common addressed

manufacturing processes

Common vision of a European ZDM paradigm

Contribution to a common ZDM reference architecture

Share technological approaches within ZDM

Share demonstrator cases & research results

Contribution to international standards

… more than 4 individual on-going projects!!

Clustering Initiatives: 4ZDM and Focus

Common targeted sectors & market

applications Common addressed

manufacturing processes

Common vision of a European ZDM paradigm

Contribution to a common ZDM reference architecture

Share technological approaches within ZDM

Share demonstrator cases & research results

Contribution to international standards

… more than 4 individual on-going projects!!

Clustering Initiatives: 4ZDM and Focus

FOCUS – “Factory of the Future Clusters” is a CSA

funded under the call FoF 7 – 2014 “Support for the

enhancement of the impact of FoF PPP projects”

RobustPlaNet— Shock-robust Design of Plants and their Supply Chain Networks

Project acronym: RobustPlaNet

Call and Contract: FP7-2013-NMP-ICT-FOF(RTD)

Grant agreement no.: 609087

Project Duration: 01.10.2013 – 30.09.2016

RobustPlaNet: Shock-robust Design of Plants and their

Supply-Chain-Networks

FoF Impact @CRIT

10.11.2016, Vignola (MO)

Presenter: Prof. Marcello Colledani, Department of Mechanical Engineering, Politecnico di Milano, Italy