Marco Macchi Politecnico di Milanoimages.no.camcom.gov.it/f/StudiStatistica/Fabbrica40/12/12505... · 16 Prof. Marco Macchi ‐POLITECNICO DI MILANO ‐[email protected] Internet

07/04/2016 Prof. Luca Fumagalli ‐ POLITECNICO DI MILANO

Marco MacchiPolitecnico di Milano

Factory 4.0 ‐ Towards the 4th Industrial Revolution SCENARIOS, TOPICS & TRENDS

Prof. Marco Macchi ‐ POLITECNICO DI MILANO ‐ [email protected]

The Fourth Industrial Revolution (aka Industry 4.0)


Evolution or Revolution?


Smart … Advanced … 4.0

Source: Osservatorio Smart Manufacturing, Yearly Report 2016


Piano nazionale Industria 4.0Milano, 21 settembre 2016


Factory 4.0: it is time to change

The good habitsto promote

The bad habitsto be abandoned


The «voice» of the companyHow to implement the «Industry 4.0» vision in a company?

A company has its own business pressures & priorities …

Velocity

Collaboration

Transparency


GermanyIndustrie 4.0

Initiatives in other countries: Germany


Initiatives in other countries: USA

USASmart Manufacturing Leadership Coalition


UKCatapult‐High Value Manufacturing

Initiatives in other countries: UK


Initiatives worldwide



Italy in worldwide manufacturingRank 2000 2010 2014

Cina

USA

Giappone

Germania

Korea, Rep.

Italia

Brasile

Francia

India

Regno Unito

Russia

Spagna

Messico

USA

Giappone

Cina

Germania

Regno Unito

Italia

Francia

Korea, Rep

Messico

Spagna

Brasile

Canada

India

USA

Giappone

Germania

Italia

Francia

Regno Unito

Cina

Brasile

Spagna

Canada

Korea, Rep

Olanda

Messico

1990

Cina

USA

Germania

Giappone*

Korea, Rep.

India

Italia

Regno Unito

Francia

Russia

Brasile

Messico

Canada

12345678910111213

Top 13 manufacturers in the worldShare of global nominal manufacturing gross value added Data source: The World Bank


Labour productivity

Source: OECD (Organisation for Economic Co-operation and Development )

80

85

90

95

100

105

110

115

120

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013

(199

8=10

0)

Total Factor Productivity


ufacturing


Identity

Design

Manufacturing

DistributionUsage

Maintenance

Disposal & Recycle

Each item has its electronic passport: traceability is enabled along the life cycle of the single item


Internet of Things

• According to some estimates there will be 50 billion mobilewireless devices connected to the Internet across the globeby 2020.

• The total number of devices connected to the Internet in someway could reach 500 billion.

OECD (2012), “Machine-to-Machine Communications: Connecting Billions of Devices”, OECD Digital Economy Papers, No. 192, OECD Publishing.

PHYSICAL PRODUCT

ID

DATA & KNOWLEDGE


Internet of Things


Cloud computing

Infrastructureas a Service

(IaaS)

Platform as a Service (PaaS)

Software as a Service (SaaS)

Manufacturing as a Service

(MaaS)

CLO

UD

CO

MPU

TIN

G“Two types of cloud computing adoptions in themanufacturing sector have been suggested:manufacturing with direct adoption of cloudcomputing technologies, and cloud manufacturing,the manufacturing version of cloud computing.… Incloud manufacturing, distributed resources areencapsulated into cloud services and managed in acentralized way ….Cloud users can request services ranging from productdesign, manufacturing, testing, management and allother stages of a product lifecycle”

Xun Xu, 2012, “From cloud computing to cloud manufacturing”,Robotics and Computer-Integrated Manufacturing.


Cloud computing


(IaaS)




(MaaS)

CLO

UD

CO

MPU

TIN

G


Cloud computing


(IaaS)




(MaaS)

CLO

UD

CO

MPU

TIN

G

Virtual machines, storage, ... as resources in order to runsolutions demanding highly computing capabilities


Cloud computing


(IaaS)




(MaaS)

CLO

UD

CO

MPU

TIN

G

IoT platform that provides a set of functionalities for smartdevice management, DBMS, data analytics, etc.



Cloud computing


(IaaS)




(MaaS)

CLO

UD

CO

MPU

TIN

G



Platform in order to support applications and data for thecollaboration in the value chain


Cloud computing


(IaaS)




(MaaS)

CLO

UD

CO

MPU

TIN

G



Platform in order to support applications and data for thecollaboration in the value chain

Platform in order to support the virtualization and sharingof manufacturing resources


Big Data and Analytics



What about unstructured data? “80 percent of global data isunstructured, so what do we do? …”

The majority of content tends to be human-generated content:this does not fit neatly into database tables.

«… While ‘Big Data’ technologies and techniques are unlockingsecrets previously hidden in enterprise data, the largest source ofpotential insight remains largely untapped … the ‘Big Content’remains grossly underutilized and its potential largelyunexplored…» (“Big Content: The Unstructured Side of Big Data”http://blogs.gartner.com/)



http://www.ibm.com/watson/


Cyber Physical SystemsThe term «Cyber-Physical Systems» was forged by the NationalScience Foundation, in USA:

«a broad range of complex, multi-disciplinary, physically-aware next generation engineered systems that integratesembedded computing technologies (cyber part) into thephysical world»

The integration includes capabilities to sense, to communicateand to control the physical systems. Indeed, cyber and physicalpart are tighly integrated at different scales and levels.


Cyber Physical Systems

Cyberized plant/ «Plug & Produce»

Next step production efficiency

Digital Ergonomics

New data-driven servicesand business models

Data-based improvedproducts

Closed-loop manufacturing

1

2

3

4

5

6

Cyber-Physical Systems are technologicalsystems that will boost the transformation of business models, factories and supply chains

sCorPiuS – European Roadmap for Cyber-Physical Systems in Manufacturingwww.scorpius-project.eu



Cyberized plant/ «Plug & Produce»

Next step production efficiency

Digital Ergonomics

New data-driven servicesand business models

Data-based improvedproducts

Closed-loop manufacturing

1

2

3

4

5

6

Cyber-Physical Systems are technologicalsystems that will enable new opportunitiesfor the product & the factory life cycle


Cyber Physical SystemsIntegration & Intelligence are required for the implementation ofCyber Physical Systems.

Lee, J., Bagheri, B., Kao, H., 2015. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems.Manufacturing Letters, Volume 3, 18–23.

Self-comparisonb/w digital twins



Lee, J., Bagheri, B., Kao, H., 2015. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems.Manufacturing Letters, Volume 3, 18–23.

The inter-connection between machinehealth analytics through a machine–cyber interface (at the cyber level) isconceptually similar to social networks.

Self-comparisonb/w digital twins


Cyber Physical SystemsCyber-Physical Systems (CPS) are the key enablers for the“digital factory” creation since they bridge the gap between thereal and virtual world by connecting different smart objects withthe factory’s information systems and making all the actors of thefactory communicating each other across the entire value chain.E. Lee, “The Past, Present and Future of Cyber-Physical Systems: A Focus on Models,” Sensors, vol. 15, no. 3, pp. 4837–4869, 2015.

Digital twins


Cyber Physical Systems• Extended intra- and inter-enterprise integration• Empowerment of collaboration & velocity within and between the factories• New role of human resources, and new jobs & skills

Horizontal INTEGRATIONHorizontal INTEGRATION

Vertical IN

TEGRA

TION

Vertical IN

TEGRA

TION

• OEM – asset owner• Inter‐plant collaboration

• Production – Maintenance• Plant engineering – Maintenance

Typical Components for the «recipe» of Cyber

Physical Systems



01010011011101010110111101101101011001010110111000100000011010110110111101101110011001010010110100100000011010100110000100100000011011010110010101110100011000010110110001101100011010010111010001110101011011110111010001100101011101000110010101101111011011000110110001101001011100110111010101110101011001000110010101101100011011000110000100100000011011110110111000100000011010000111100101110110110000111010010001110100001000000110010101110110110000111010010011000011101001000111010000100000011011010110010101101110011001010111001101110100011110010110101101110011011001010110010101101110

Analysis of data

Better machines and equipmentImproved processes

More efficient production

• Enhanced transparency and subsequent business potentials


A Smart Maintenance tool for a safe Electric Arc Furnace

MIMOSA OSA-CBM specification / ISO-13374

Outcome: E‐maintenance tool extending thefunctionality of the Plant Automation withPHM capabilities:State Detection‐ Injection systems AND Hearth/bottom + PanelsHealth Assessment‐ Injection systems AND PanelsAdvisory Generation‐ Injection systems

Electric Arc Furnace (EAF)

‐ Burning system carbon injection and oxygen injection systems‐ Hearth/bottom lower sheet metal keel (coated with refractory bricks)‐ Cooling system panels (cooled by a water circuit).

Data Acquisition (DA)

Data Manipulation (DM)

State Detection (SD)

Health Assessment (HA)

Prognostic Assessment (PA)

Advisory Generation (AG)

ExternalSystems, Data

Archiving& BlockConfiguration

Technical Dysplays

& Informati

on Presentation (HMI)

Sensors / Transducers / Manual Entry

DM,SD functions

SD,HA,AG functions









Sensors / Transducers / Manual Entry EAF/Burning system/Injection systems


Outcome: PHM algorithm(key variables: pressures, flow rates)

State Detection‐ Deviations from normal operating conditions [based on a regression

model and a control chart]Health Assessment & Advisory Generation‐ Causes of degradation, recommended counteractions, etc. [based on

HAZOP tables]

Item n. 1.1 – Deviation: High Pressure

Causes Effects Counteractions Suggestions

The pipeline is obstructed by slag

Injection nozzle is crushed

Malfunction in pressure sensor

Malfunction in flow control valve

Errors in PLC data communication

Crushed pressure sensor

Crushed flexible pipe Wrong mounted nozzle Malfunction in non-

return-valve

Increase in pressure level may cause a damage in flexible pipe, possible fire triggering with explosion or damage to other plants

Malfunction in the burner with consequent missed melting of the scrap

An error in pressure measure may cause the control system to turn off the burner

Possible mixing of different fluid in the pipeline

During supersonic injection phase of the oxygen the burner may cause some melted metal to be projected and/or have a poor oxidation of metal

Check nozzle status during planned stops

Check annually pressure level of the pipelines

Increase flexible pipe reliability Change nozzle Annually check flow control

valve status In case of explosion

immediately stop the system by pushing emergency button

Evaluate the possibility to add indicators on operator monitor to help them in finding possible malfunctions

Evaluate the possibility to implement a safety stop under particular conditions

Evaluate the possibility to divide working area in different compartments









Sensors / Transducers / Manual Entry EAF/Sole & Cooling system (panels)


Outcome: PHM algorithm(key variables: temperatures)

State Detection‐ Deviation from safe conditions due to presence of water in the hearth

/ bottom [based on a regression model and a control chart]‐ Occurrence of a series of “critical” events of the melting process – i.e.

events featuring relevant stress on panels during normal operatingconditions [based on a control chart]

Health Assessment‐ Deviations toward unhealthy conditions for the panels [based on

statistical indicators to extract the process features (i.e. actual vs.reference) and a risk matrix to map the features]


A Smart Control systemfor reconfigurable manufacturing

Reconfigurability

SemanticsModularity

Plug and Produce

Fastercommissioningof plants


A Smart Control systemfor reconfigurable manufacturing

eScop : Embedded systems forService-based Control of OpenManufacturing and Processautomation

Semantic-based reasoning

The MSO (Manufacturing Systems Ontology)stores the knowledge of the configuration ofthe production system + information from fielddevices in the physical layer (i.e. status of thedevices)

E. Negri. The Role of Ontologies for Smart Manufacturing.PhD thesis

Self-awareness of each digital twin


Smart Sensors for Condition Based Maintenance

Reconfigurability

Diagnosabilityand quality ofproduction










MIMOSA OSA-CBM specification / ISO-13374 sensors

Data Acquisition

sensors

Data Acquisition

sensors

Data Acquisition

Data Manipulation & State Detection

Web service Health Assessment

Robot control Human Machine Interface

1

2

3

5

4

6

Web service

eSONIA Embedded Service OrientedMonitoring, Diagnostics and Control:Towards the Asset-aware and Self-Recovery Factory











Data Acquisition

sensors

Data Acquisition

sensors

Data Acquisition




1

2

3

5

4

6

Web service












Data Acquisition

sensors

Data Acquisition

sensors

Data Acquisition




1

2

3

5

4

6

Web service














The key is the improvement

The resources (people and machines) shouldbe managed towards the improvement of theoperational excellence, with the purpose toachieve enhanced performances together withadequate cost balance

VelocityCollaboration

Transparency

Collaboration


Which model for improvement?

There is a German, a Japanese, an American and an Italian…

Understandthe problems

Foresee &

solutions

Foresee & Develop new solutions

Collaboration


The German

[DER SPIEGEL 1964]


Foresee &

solutions


Automation of resources:technology push

Collaboration



Foresee &

solutions


Operatorsengagement

The Japanese

Planned activitesof continuousimprovemenyCollaboration



Foresee &

solutions


The American


The Italian


Soluzioni tradizionali – Livello di Implementazione

The most complex traditional solutions are still scarcely diffused. Can we do the Fourth Industrial Revolution, if the third one has not yet been done?

The most complex traditional solutions are still scarcely diffused. Can we do the Fourth Industrial Revolution, if the third one has not yet been done?

67%47% 31% 31% 29% 28% 22% 15% 14% 12%

21%37%

31% 34% 28% 34% 31% 37% 37% 27%

12% 17%38% 35% 43% 38% 47% 48% 50% 61%

0%10%20%30%40%50%60%70%80%90%

100%

Non implementata

Parzialmente implementata

Completamente implementata

*Sample size: 289 companies

Let’s understandthe italian scenario

Not implementedNot implemented

Partially implementedPartially implemented

Completely implementedCompletely implemented

Traditional solutions – implementation level


Diapositiva 51

g3 in media hanno risposto in [email protected]; 20/06/2016

g4 ogni tecnologia ha la sua base di [email protected]; 20/06/2016


The Italian scenario and Small-Medium Enterprises

The Italian industry is highly represented by small-mediumenterprises, in a highly competitive, turbulent and uncertainmarket !

But the voice of the company is still ….

Velocity

Collaboration

Transparency



With some specificity …• Difficulties with fluctuations (cash flows, orders, …)• Limited customer basis, closeness to the customers• Tending to adopt a reactive approach• Risk of a digital divide in the industry landscape

Velocity

Collaboration

Transparency



Some characteristis in the operations …• Synchonization with the material flows• Short-loop control• Flexibility and reconfigurability• Responsiveness

Velocity

Collaboration

Transparency


Fabbrica 4.0: le tecnologie abilitanti


Fabbrica 4.0: la visione


The «voice» of the companyHow to assess the digital readiness of a company?

Ask the main stakeholders …

• Production manager; • Asset manager;• Quality manager;• Product engineering manager;• Process engineering manager;• Operations manager;• Logistics manager;• IT manager


Digital Readiness & Opportunities Identification

How to assess the digital readiness of a company?

A. De Carolis. Building Smart Manufacturing through Cyber-Physical Systems: a model to generate awareness and toguide manufacturing companies towards digitalisation. PhD thesis


Digital Readiness & Opportunities Identification

How to look for opportunities of manufacturing digitisation?

Process Area Dimension Strengths Weaknesses

Process 1 Process •

Monitoring and control • •

Technology • •

Organization • .

Process 2 Process • •

Monitoring and control •

Technology • •

… Process •

Monitoring and control •

Technology •

Process N Process •

Monitoring and control • •

Technology •

Area Opportunities

Process Area 1

Design and Engineering

•

Process Area 2

Production Management

•

Process Area 3

Quality Management

•

Process Area 4

Maintenance Management

•

Process Area 5

Logistics Management

Digital Backbone

OpportunitiesIdentification

Strenghs and WeaknessesIdentification

MaturityAssessment

A. De Carolis. Building Smart Manufacturing through Cyber-Physical Systems: a model to generate awareness and toguide manufacturing companies towards digitalisation. PhD thesis

Documents

Marco Macchi Politecnico di Milanoimages.no.camcom.gov.it/f/StudiStatistica/Fabbrica40/12/12505... · 16 Prof. Marco Macchi ‐POLITECNICO DI MILANO ‐[email protected] Internet