Introduction to Engineering Intelligence

Engineering Intelligence and Model Based Systems Engineering Tampere 29.5.2013

Introduction to Engineering Intelligence Professor Kari T. Koskinen Tampere University of Technology Intelligent Hydraulics and Automation Smart Simulators Group [email protected]

ICT2015-group (Ala-Pietilä) 21 paths to frictionless Finland

ICT is the most important technology, which enables the future growth o Infrastructure (national service architecture) o Know-how ( 10 years ICT2023 –program) o Financing (start ups, etc) o Actions • • • •

- Collision and networking projects (competence centers) - Digital services and contents - Big Data - The improvement of chain of research, application, productization and commercialization (programs) • - etc….

DIGITAL SERVICES Koskinen, Introduction to Engineering Intelligence

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Smart Apps Initiative in Engineering (TEKES/Gaia, 2013) Conclusions of the report: - The significance of ICT is growing more rapidly in development of competitiveness of mechanical engineering - This requires big strategic decisions from machine industry - The direction is towards standardized software platforms based on open source code - This requires high level ICT know-how, but also knowledge of customer’s field and machine industry - There is a clear need for increasing and stregthening the co-operation between machine industry and ICT -industry Proposals: Benchmarking Forum, Common software platform, Smart Apps Camps

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Fimecc, TiVit Strategic Acrhitecture for Technology Industry (2012)

VISION: Technology Company 2020 1. The company is an active actor within its ecosystem 2. The company utilizes an ICT architecture, which is based on core competencies and business requirements 3. The company is providing first class user experience and value added for its customers 4. The company speedily and flexibly implements plans and processes utilizing relevant technical and other developments 5. The company’s employees are competent and motivated and they consinuously develop their competencies and work processes

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Aberdeen Group

Systems Design, New Product Development for Mechatronics (2008)

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Aberdeen Group

Systems Design, New Product Development for Mechatronics (2008)

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Engineering Intelligence (EI) is the ability for an engineering organization to orchestrate processes related to collaborative engineering, product lifetime information, competencies, and intelligence of products and services with semantically rich, context-aware and intelligent ICTsolutions.

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Engineering Intelligence – Viewpoint for Collaboration

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Challenges 1. Knowledge in air-tight silos (no true collaboration) 2. Changing the viewpoint from parts to wholes (holistic systems) 3. Knowledge and know-how in dynamic environment (human centricity) 4. Continuous development of processes (systems theory) 5. Change from legacy IT systems to new generation IT systems (from technical systems to service oriented systems) 6. Intelligence (services, contextual, temporal) 7. Balance between Innovations and incremental improvements Koskinen, Introduction to Engineering Intelligence

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Change in Trends Today Closed world Patents Product-centered Monolithic Static systems Constrained representations Linear

In Future Open ecosystem Evolving products Context and market based Linked and distributed Agile and Dynamic Non-linear

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Engineering Intelligence Ecosystem (EIE)

The Engineering Intelligence Ecosystem (EIE) is a network of (potential) stakeholders acting around a particular product(s) or product-services and utilizing a common protocol in knowledge management and having shared collaboration based on contextaware digital solutions. Koskinen, Introduction to Engineering Intelligence

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EIE Approach Intelligent control of knowledge flows Semantic Modeling Digital and Virtual technologies ORCHESTRATION

KNOWLEDGE

Collaboration Customer centricity, networking, agile operations

ACTION

ORCHESTRATION

ORCHESTRATION

INTELLIGENCE

KNOW-HOW

Intelligent product and service technologies Intelligent decision making ORCHESTRATION Koskinen, Introduction to Engineering Intelligence

Operation models Multi-operations Continuous development Operation culture Understanding the wholes 12

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EIE-RI Themes and Main Targets Result 1

Industrial projects, RYM, TIVIT, FIMECC

Theme 1

KNOWLEDGE FLOWS

EU-projects, International collaboration

SMART SERVICES

Theme 2

EDUCATION

COLLABORATIVE PROCESSES

(MSc, PhD)

RESEARCH Activities

TECHNOLOGY TRANSFER

Result 2

CO-CREATIVE ENVIRONMENTS

ENTREPRENEURSHIP Development

COMPETENCE CUMULATION

BUILT-IN INTELLIGENCE Result 3

SPIN-OFFS

Theme 4

CONTEXT AWARE DIGITAL SYSTEMS

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Theme 3

Regional development (Regions, Cities)

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Integrated Semantic Model (Boom Case)

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Koskinen, Introduction to Engineering Intelligence

13 Modules 239 Classes 4833 Triples 4731 Axioms 2447 Logical axioms

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Virtual Machine Laboratory (VML) • Technology platform • Dynamic real time simulation • Control of a Machine System (Joystics) • Cicuit visualization • 3D-visualization • Measurement equipment • Control of simulation time • Scenarios • Teacher’s equipment • Fault detection support Koskinen, Introduction to Engineering Intelligence

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Model Based System Engineering

Source: LMS Engineering, www.lmsintl.com

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Significance of Results ENGINEERING INTELLIGENCE -technologies

More effective R&D and Design Processes

Multi-technological know how

Comprehensive Competences

Agility

Time-to-market

New Business Possibilities

COMPETITIVENESS OF INDUSTRY Koskinen, Introduction to Engineering Intelligence

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Smart Simulators Group • Mathematical modeling of static and dynamic machine systems • Semantic models of machine and production systems • Machine system simulation • Production system simulations • Hydraulics, machine automation, diagnostics, FMS, factory automation • Virtual reality technologies, software development • Product-Service Systems and Life-time Information management • Formal knowledge representations (OWL, OWL DL) • Integrated Product,Process and System Knowledge management (new PDM, PLM concepts)

Professor Kari T. Koskinen Intelligent Hydraulics and Automation

Project Manager Pekka Ranta Mathematics/ IISLab

Adjunct Professor Dr. Ossi Nykänen Mathematics, IISLab

Research Fellow, Project Manager Dr. Minna Lanz Production Engineering

Tampere University of Technology • Intelligent Hydraulics and Automation • Production Engineering • Mathematics / Intelligent Information Systems –lab.

Project Manager Jussi Aaltonen Intelligent Hydraulics and Automation

Koskinen, Introduction to Engineering Intelligence

In collaboration with: • Production Engineering • Mathematics • Information Management and Logistics • Industrial Management • Automation Science and Engineering • Signal Processing • School of Architecture • Civil Engineering • Pervasive Computing 18

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TUT Kampusareena – a futuristic science department store

Koskinen, Introduction to Engineering Intelligence