Industry 4.0 Digitalisation as Day-to-Day Business · Virtual, augmented and mixed reality Largely...

www.kit.edu Karlsruhe Institute of Technology (KIT) – The Research University

in the Helmholtz Association

INSTITUTE FOR INFORMATION MANAGEMENT IN ENGINEERING

Industry 4.0 Digitalisation as Day-to-Day Business

What is today feasible for the company in the future?

Prof. Jivka Ovtcharova

International Conference on Sustainable Manufacturing (ICSM 2016) I Shanghai I China I November 5th, 2016

Institute for Information Management in Engineering 2 November 5th, 2016

Main questions

Why are we in the field of Industry 4.0?

Why digitalisation everything changes?

Where are potentials for the business?

How it looks in the implementation?

What is already possible in cooperation with us?

Pictures: Lifecycle Engineering Solutions Center


Main questions








Profile in brief

2009

Karlsruhe Institute of Technology

The Research University in the Helmholtz Association

EDUCATION RESEARCH INNOVATION

1825

Karlsruhe University (TH)

1956

Helmholtz Research Center

Focused interdisciplinary collaboration:

Division I – Biology, Chemistry and Process Engineering

Division II – Informatics, Economics and Society

Division III – Mechanical and Electrical Engineering

Division IV – Natural and Built Environment

Division V – Physics and Mathematics

http://www.pkm.kit.edu/

http://www.kit.edu/kit/mediathek.php#top

http://www.kit.edu/mediathek/bilder_forschung/CERN_1.jpg


Division III - optional clusters of institutes

Legend: assignment of institutes

Mechanical

Engineering Electrical

Engineering

Mixed

Product / Production

Engineering and Logistics Automation Applied Materials

Mechanics & Fluid Machinery Nano and Micro

Systems Power Plant Technology

Electrical Energy Vehicle

Technology

Helmholtz Association University

Information & Communication

Digitalisation Competence


Triad of digitalisation under one roof

EDUCATION RESEARCH INNOVATION

Institute for Information Management

in Engineering at KIT

Lifecycle Engineering

Solutions Center at KIT Research Division Intelligent Systems

and Production Engineering at FZI

Research assistants 20

Promotions (1st Advisor) 28

External / Industry Ph.D. students 12

Student assistants 30

Digital Transformation (lat. transformare) is the process of a holistic

change of information models, business processes as well as human-

machine-interaction using advanced digital technologies and services.


Digital transformation 360°

RESEARCH

INNOVATION

EDUCATION

Knowledge Screening

(Big) Data Analytics

(Engineering)Data

Acquisition

Human-Maschine Interaction

Feedback Management

Engineering Data Modeling and Simulation

Practical Implementation

Virtual & Experimental

Validation

LIFECYCLE

KNOWLEDGE

VALUE


Main principal

Knowledge Screening

(Big) Data Analytics

(Engineering)Data

Acquisition

Human-Maschine Interaction

Feedback Management

Engineering Data Modeling and Simulation

Practical Implementation

Virtual & Experimental

Validation

LIFECYCLE

VALUE

Digital transformation 360° - view of the whole, at any moment!

KNOWLEDGE


Lifecycle Engineering

Model-based Systems Engineering

Strategic Portfolio Planning

Product Lifecycle Information Management

Computer-Aided Design and Simulation

Business Process Management

Complete data acquisition from multiple unstructured data

sources and end-to-end process simulation

Real-time Tooling and Execution

Fields of competence:


Knowledge Management

Self Learning Big Data Analysis

Recording of Various Machine Parameters

Fast Knowledge Extraction

Historical Data Processing for Forecasting


Reduced Energy Load Peaks Control for Balanced Load Distribution

Energy Consumption Measurement

Extensive (big) data analytics - from data collection to knowledge

extraction, distribution and quality/consistency evaluation


Smart Immersive Environments

Multi Dimensional Service Prototyping

Virtual Reality Engine „PolyVR“

New Interaction Methods for VR/AR

Algorithms for Dynamic Virtual Scenes

Extensive 3D-Scanning and Reconstruction

Optimization of Human-Machine-Interaction

PolyVR

Dynamic and scalable SW/HW solution for VR- and 3D-

Scannig incl. tracking systems, haptic and mobile devices



Virtual Environments Lab Stereoscopic 7-channel VR

projection system, ART tracking

system

Mixed Reality Lab

Mobile projection, ART tracking

system and haptic devices

Communication Lab

Shared space for lectures, project

meetings and workshops

Content Creation Lab

Training and Development with CAx/PLM

Cooperation Lab

Spatial and technical cooperation

platform

Value Creation Lab

Demonstration and transfer of

research results

Energy Experience Lab

Mixed reality platform solutions for

energy efficiency

Tea Lab

Creativity pool

Lifecycle Engineering Solutions Center: Infrastructure Enabling IT-Infrastructure

Lifecycle Engineering Solutions Center


Industry 4.0 Collaboration Lab

Opening Ceremony

September 24, 2014

Hannover Messe

April 14, 2015

Target Group: Small- and medium-sized companies in

Automation, Manufacturing and Services

Goal: Strengthening of competitiveness through

extensive digitalisation, networking and real-

time-enabled solutions for day-to-day

business

Solution: Investigation and qualification platform

Solving business problems individually

and pragmatically, through action Source: Industry 4.0 Collaboration Lab

http://www.solidline.de/home.html



Main questions







Source: Industry 4.0 Collaboration Lab

The challenge “Industry 4.0”

End of the 18th century Beginning of the 20th century Beginning of the 70's Today

De

gre

e o

f In

no

va

tio

n

Time

Industry 1.0

Mass production

Mechanisation hydro- and steam

power

Industry 2.0

Mass distribution

Electrification electrical power and

vehicle mobility

Industry 3.0

World economy

Automation computer and information

technology

Industry 4.0

Smart Factory?

Cyberisation smart devices and

humans, real time

interconnections

Industry 4.0

Smart economy


Human-centred technologies

Virtual, augmented and mixed reality

Largely accessible high-speed network

technologies

Entertainment and gaming industry, 3D

movies and TVs, Microsoft HoloLens,

Oculus Rift, Samsung Gear VR headsets

Novel input devices Kinect, Leap Motion,

wearables

3D scanning and printing software, services Source: Lifecycle Engineering Solutions Center

Source: IndustryWeek, 25 February 2016 Zuckerberg I

“The next platform where anyone can experience anything they want.”


Human-centred engineering methods

Team workplace

Decision-oriented

Real time

processing

Space motion

interaction Pictures: Lifecycle Engineering Solutions Center


Industry 4.0 value creation loop

Source: Deloitte analysis

HUMAN

EXPERIENCE

(Front-Side) Realistic (logical,

intuitive) human

perception and

human-machine-

communication in real

time and in space

DATA AND

TECHNOLOGY

(Back-Side)

Sensors,

Network, Factory

Infrastructure,

Common Data

Models and

Algorithms

Video: Microsoft HoloLens



Main questions







Fundamental change of production and work

Source: Accenture

Standardisation is a main challenge


Lifecycle Phases

Added Value

Network of Functions

Reference Architecture Model (RAMI 4.0)

Source: Plattform Industrie 4.0, RAMI 4.0 VDI, VDE and ZVEI

Consolidation of guidelines, norms and standards


Lifecycle Phases

Added Value




I4.0 component network as a first RAMI 4.0 Model


Lifecycle Phases

Added Value




Management shell as a container for all relevant I4.0 component data


Lifecycle Phases

Added Value




Real-time handling of production and service systems throughout the life cycle

Virtual Twin

Real World

Digital (Virtual)

World


Plug & Produce“ data management solution



Main digitalisation steps

1. Complete data acquisition from multiple unstructured data sources

2. Intelligent data processing for Internet of Things ((IoT)

3. Operational intelligence algorithms for multidimensional “big” data

4. Automatic data quality check

5. Real-time data analysis and visualisation, also on mobile devices

6. Subject-oriented (human-centered) business processes

7. Continuous qualification of all parties involved – living digitalisation for

the day-to-day business




Main questions







Platform business

Trend to "winner-takes-all markets" Figures: Holger Schmidt

without platforms

high development and management costs with platforms

from B2C to B2B

coordination

on

the market

coordination

on

the platform airbnb Ailibaba


Industry 4.0 by Trumpf

An example of a customer order XY (2x20 punch parts)

Sum: 97min (5min being used for punching/production)

Source: Andre Mahl, FA Service


AXOOM software & business platform concept

ONE platform (cloud), MANY partner solutions



AXOOM software & business platform concept

Connecting software, machines and processes with the individual human



Condition monitoring with AXOOM

Video: TRUMPF


Industry 4.0 at TRUMPF: examples



Industry 4.0 by SAP

Source: SAP


HANA software & business platform concept

Source: SAP

fromThings

to Outcomes

“Our predictive maintenance

solution has enabled us to improve

service levels for customers.”

fromThings

to Outcomes

In their new business model, Kaeser doesn’t sell the compressor

itself but compressed air priced by air volume. The compressor

itself stays with Kaeser.

For that, Kaeser agrees on service level agreements (SLA) with its

customers to guarantee the availability of the compressor and its

compressed air (energy consumption, compressed air quality,

operational status).

fromThings

to Outcomes

Reduced production lead time for

customized motorbikes from

21-day cycle to 6 hours

fromThings

to Outcomes

In Harley-Davidson’s new manufacturing facility, every machine is

a connected device, and every variable is continuously measured

and analyzed.

It can build 1,700 bike variations on one production line and ship

a customized bike approximately every 90 seconds.

Harley-Davidson now manufactures 25% percent more motorcycles

with 30% fewer people, and the locked schedule to build a motorcy

cle has been slashed from 21 days to 6 six hours.



Main questions







Business and technical process optimisation

Source: Thomas Mücke


Example: Real-time tool management

Use Case: Process optimization and networking

taking account of resource flows

Goal: Optimizing the design and NC processes

via web interface due to technological data

from the manufacturing process

Solution: Real-time readout of machine data

Kinematic CAM simulation with real

tooling



CAD-modells

Optimized

component

order data‘s

Work order

order state

tool data‘s

Cutting conditions, mean time, tool-life NC-programs

CAM-models

tool-lists

Masterdata‘s tools, incl. graphics

callback

Ordering

Good receipts

Stock transactions

masterdata

MDE

Work order

CAD/ NC data tool data

Information

work plan

NC-number

NC-programs

Pre

calculation

masterdata‘s tools

Setup sheet ->PDF

Setup sheet -> PDF

crib

Bill of material

Cutting conditions,

mean time, tool-life

DNC

tool data presetting

Net requirements

of tools

analytics

machine



Example: Skill-based propagation

Use Case: Skill-based propagation of “plug &

produce"-devices for smart reconfigurable

manufacturing systems

Goal: Increase production system flexibility using

collaboration of cyber-physical assets that

offer different skills

Solution: Cloud-based Asset Management System

for manufacturing execution



Example: Situation-driven simulation

Use Case: Radioactive contamination in the natural

gas and oil production

Goal: End-to-end process simulation for

decontamination of pipes of different sizes

Solution: Vibration method for decontamination

Virtual prototype and experimental setup

Real-time simulation reducing real

laboratory tests

Qualification and training Source: Industry 4.0 Collaboration Lab


Example: Virtual twin of a milling machine

Use Case: Tool configuration and manufacturing

Goal: Manual and automatic operation and

configuration of tool machines via a 3D

Web interface in the Internet

Solution: Virtual twin of the machine and

automatic or manual control with a

haptic device (six degrees of freedom)

in real time



Example: Virtual twin for factory planning

Use Case: Factory configuration and planning

Goal: Validation of large-scale integrated

production lines in real time

Solution: Integration of semantics, kinematics and

actuators of 3D models in AutomationML

Control over interactive website directly

in the virtual world

Connection of mobile devices



Example: 3D energy experience

Use Case: Energy efficiency management in

buildings, cities and factories

Goal: Learning data analytics and management

of energy experience in immersive

environments

Solution: Real-time coupling of virtual and real

building models

Web-based energy efficiency

measurement in real time and in space Source: Industry 4.0 Collaboration Lab


Use Case: Scalable 3D acquisition and analysis of

objects, tools or components

Goal: Generation of exact volume models in real

time performing semantic analysis

Solution:

Real-time large scale reconstruction of

extended and bounded 3D objects and

environments using automatic feature

extraction and time efficient algorithms

Dynamic model update

Example: Real-time 3D reconstruction

Video: https://youtu.be/v_1AAEMB2eg

Source: Siemens AG


The future is now, work with us!

Current “plug & produce” solutions through real-time data at KIT:

Prof. Dr. Dr.-Ing. Dr. h.c. Jivka Ovtcharova Head of Institute Karlsruhe Institute of Technology (KIT)

Institute for Information Management in Engineering (IMI)

Zirkel 2, Bldg. 20.20, Room 267

76131 Karlsruhe, Germany

Phone: +49 721 608-42129

Fax: +49 721 608-43984

Email: [email protected]

www.imi.kit.edu

Industry 4.0 Digitalisation as Day-to-Day Business · Virtual, augmented and mixed reality Largely...

Documents

Transcript of Industry 4.0 Digitalisation as Day-to-Day Business · Virtual, augmented and mixed reality Largely...