5th Qatar BIM User Day, BIM Interoperability Issues: Lessons learned from PLM

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1 Abdelaziz Bouras BIM Interoperability Issues: Lessons Learned from PLM Dr. Abdelaziz Bouras Qatar University, IctQATAR Chair

Transcript of 5th Qatar BIM User Day, BIM Interoperability Issues: Lessons learned from PLM

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BIM  Interoperability  Issues:  Lessons  Learned  from  PLM  Dr.  Abdelaziz  Bouras  

Qatar  University,  IctQATAR  Chair  

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Content  

•  PLM  point  of  view,  PLM  coverage  •  Use  of  Data  Reference  Models  •  SeparaEon  between  lifecycle  viewpoints    •  HarmonizaEon  of  semanEc  levels  •  Interoperability  •  CollaboraEve  learning  •  PreservaEon    •  PLM  Standards  

 

 

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ORGANISATIONS    

COMPETENCES    

Commercial  

Bureau d’Etudes  

Projet   Qualité  

Méthodes  

Production  

PROCESSES    

PROJECTS      

DOCUMENTS    

 

Material  

Reference  

Designation  

Color  

Creation Date  

Creator  

DATA      

© ASSETIUM 2007-2008 3

PRODUCTS  

All  about  Product/Proces/OrganizaEon…  

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Extended  Enterprise  of  OEM’s,  Customer,  Partners    

and  Suppliers

Enterprise  Integra;on  through  dedicated  networks  

Domain  specific    informa;on  systems  

(CAD,  PDM,  ERP,  TMS,  WMS…)  Dept  

Enterprise  

Custom

ers  

Partne

rs  Tier  1  

Supp

liers  

Tier  2  

Supp

liers  

Extend

ed  Enterprise  Integra;

on  

BOL

Concept Design Manufacturing Use Disposal

In  Service    Support  

Product  Lifecycle  Management  (PLM)  

PLM  coverage  

Distribution   Preservation

MOL   EOL

?

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Interoperability  and  HarmonizaEon  (for  Big  Companies)  

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Example  of  external  object    

Radar  for  locaEng    the  target  

 

Menu  

Window  for  communicaDon  (chat,  …)    

Missions  ,  objecDves  to  realize    

InnovaEon  and  Change  Management  (for  SMEs)  Give  to  the  SME  a  new  approach  to  conduct  changes:  Simula9on  environment  to  make  training  a=rac9ve  and  adapt  it  to  the  business  context,  help  the  user  discovering  advanced  scenarios  

 

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Provide  indicators  to  the  project  team  in  order  to  Measure  and  evaluate  training,  detect  resistance  and  react  quickly  

 

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Operating times, quality values, planning parameters, storage conditions, security storage levels… Processes using

this data Planning, manufacturing, price calculation…

Owner of the data Design, production…

Process evolution for “best practices” Evolution description, responsibilities…

Storage/master system Design repository, PDM, ERP,SCE…

Associated documents Specifications, plans, instruction sheets…

Performance Indicators “Best practice” respect…

Product Data

   Current  trends  

 1.  Use  of  Data  Reference  Models  

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2.  SeparaEon  between  lifecycle  viewpoints    

ü  Enterprise  viewpoint  –  roles,  acEviEes,  policies    

ü  InformaEon  viewpoint  –  semanEcs  of  informaEon    

and  informaEon  processing  

ü  ComputaEonal  viewpoint  –  interfaces  and  computaEonal    

objects  

ü  Engineering  viewpoint  –  distribuEon  infrastructure  (e.g.  web  services)  

ü  Technology  viewpoint  –  choices  of  technology  (e.g.  app  servers,  DBMS)  

Enterprise  viewpoint  

RM-­‐ODP  

InformaDon  viewpoint  

ComputaDonal  viewpoint  

Technology  viewpoint  

Engineering  viewpoint  

RM-­‐ODP

DISTRIBUTED  PROCESSING,  CLOUD  

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Terms Thesauri

Formal Taxonomies

Frames (OKBC)

Data Models (UML, STEP)

Description Logics

(DAML+OIL)

Principled, informal

hierarchies

Ad hoc Hierarchies

(Yahoo!) Structured Glossaries

XML DTDs

Data Dictionaries

(EDI)

‘Ordinary’ Glossaries

XML Schema

DB Schema

Glossaries  &  Data  Dic;onaries  

Schemas,  &  Data  Models  MetaData    

Formal  Ontologies    &  Inference  

Thesauri,  Taxonomies

Ack: M. Grunninger

3.  HarmonizaEon  of  semanEc  levels  

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4.  interoperability    

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5.  CollaboraEve  learning  

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–  Governments  are  imposing  very  strict  preserva9on  regula;ons  (80-­‐90  years  for  aerospace,  15-­‐20  years  for  automo9ve…)    

–  Lifecycles  of  the  products  can  be  much  longer  than  lifecycles  for  compu9ng  hardware,  applica9ons  soQware,  storage  media,  etc  

–  Products  are  becoming  more  complex  –  Enterprise  domain-­‐applica9ons  are  becoming  massive  data  and  

informa9on-­‐based  

 

Why  worry  about  long  term?    

PreservaDon  

6.  PreservaEon    

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–  Which  data/knowledge  to  be  preserved?  (Knowledge  lifecycle  assessment…)  

–  How  to  deal  with  Data/Knowledge  idenEficaEon  and  extracEon  from  huge  heterogeneous  warehouses  and  mulEmedia  databases?  

–  Think  about  the  future  extensibility  and  reusability  of  the  digital  preserved  models  

–  How  to  make  digital  preservaEon  pla^orms  Interoperable?  

How  should  digital  data  and  mulDmedia  informaDon  be  represented  so  it  will  be  readable  X  years  from  now?    

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Airbus  A400M  (>0.5  million  parts)  Digital  Mockup  =    15  Tera-­‐bytes      

 Renault  Clio  2:  250  versions  Clio  3:  600  versions  (+  special  versions)  

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Long Term Knowledge Retention and Preservation (LTKR)

Washington  DC,  USA,  2006/2007      

Participants: Boeing, Airbus, Ford, Thales, Nasa, Nara, NIST, NSF, ANR…

First  iniEaEves…  

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 Requirements  for  long  term  preservaEon    

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Prominent  preservaEon  research  issues  

Issue

Project Scalability

Methods &

Models Integrity

Complex

Objects Context Automation

PROTAGE ü

SHAMAN ü

LiWA ü ü ü ü ü

PrestoPRIME ü ü

KEEP ü

SCAPE ü ü

ARCOMEM ü ü ü

TIMBUS ü ü

ENSURE ü ü

BlogForever ü ü

Wf4Ever ü ü

APARSEN ü ü

LOTAR ü ü ü

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Open Archival Information System” (OAIS) framework

SIP = Submission Information Package

SIP

DIP

Administration

P R O D U C E R

C O N S U M E R

queries result sets

Ingest Access

Data Management

Archival Storage

Descriptive Info.

Preservation Planning  

orders

AIP

AIP = Archival Information Package DIP = Dissemination Information Package

ConsultaEve  Commicee  for  Space  Data  Systems  (CCSDS)  

MANAGEMENT

Use  standardised  preservaEon  approaches    

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Ex.  LOTAR  IniDaDve    (LOng  Term  Archiving  and  Retrieval  of  digital  technical  product  

documentaDon)  

•  Project  driven  by  Airbus,  EADS-­‐Military  Aircrad,  MTU  Aeroengines,  Alenia  AeronauEcs  and  BAE  Systems    

•  Goal:        The  goal  of  LOTAR  is  to  specify  auditable  processes  for  

long-­‐term  archiving  of  3D-­‐CAD  and  PDM  data    

•  SpecificaDons:  –  Requirements  for  archiving,    –  Methods,    –  Scenarios,    –  Detailed  process  descripEons  and  process  modules,    –  Suitable  data  schematas,    –  System  architecture  frame  work,    –  Recommended  pracEces.    

hcp://www.lotar-­‐internaEonal.org/  

naEve    DB CAD/PDM

CAD/PDM data storage

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MigraEon  VS  EmulaEon?  Dynamic  PreservaEon  AcEve  Repository…  Technical  Registry    (ie.  naEonalarchives.gov.uk)  

Current  limitaEons…  

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Product

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Enterprise

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7.  PLM  Standards  

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The  10  desirs  around  Open  Standards*    

1.  Open  meeEng  2.  Consensus                  Creator  3.  Due  Process              4.  Open  IPR  5.  Open  World  6.  Open  Change                Implementor      7.  Open  Documents  8.  Open  Interface  9.  Open  Access                        User  10.  On-­‐going  Support            

*Ken  Krechmer  (University  of  Colorado)  

Open Vs. Hybrid Standards

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 The  12th  IFIP  Interna9onal  Conference  on  PLM…  was  held  in  Qatar  (plm-­‐conference.org)  

               

Thank you!    

[email protected]    

For  more  informaEon  on  PLM,  please  visit….  www.ifip-­‐wg51.org