U-Meet - Lohse - Industrie 4.0

Industrie 4.0 Chances for robotics and automation Smart U-Meet evening, University of Twente

Twente, 2 October 2014

Laboratory for Machine Tools (WZL), RWTH Aachen Fraunhofer Institute for Production Technology (IPT) Wolfram Lohse Š WZL/Fraunhofer IPT

RWTH Aachen University and Fraunhofer-Gesellschaft Fraunhofer-Gesellschaft  More than 80 institutes and establishments at 40 locations in Germany  18,000 employees  Research budget of 1.65 Billion €, with 1.4 Billion € contract research  3 institutes in Aachen RWTH Aachen University  Founded 1870  About 40,000 students Faculty of Mechanical Engineering  11,700 students (including 1,800 first-year students)  62 professorships  2,600 employees  160 graduates per year © WZL/Fraunhofer IPT

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Production Engineering at RWTH Aachen University Laboratory for Machine Tools (WZL)  Institute of RWTH Aachen University  Founded in 1906  Approx. 820 employees

(approx. 250 academic staff)  16,000 m² offices and laboratories

Fraunhofer-Institute for Production Technology IPT  Institute of Fraunhofer-Gesellschaft  Founded in 1980  Approx. 380 employees

(approx. 100 academic staff)  3,000 m² offices and laboratories  Partner institute in Boston/USA: CMI Fraunhofer Center for Manufacturing Innovation

WZLforum  Knowledge transfer to the industry  Organising workshops, training and

seminars © WZL/Fraunhofer IPT

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Our Focus Process Technology  Machining and material removal processes  Laser materials processing  Forming processes  CAx, Virtual Reality

Production and Machine Tools  Design of production machines and components  Control technology and automation  Component and production machines testing

Metrology  Tactile metrology  Optical metrology

© WZL/Fraunhofer IPT

Gearing Technology  Gear manufacturing  Gear calculation and investigation

Management  Business Engineering  Technology management  Innovation management  Production management  Quality management Education  Professional training  Executive MBA for Technology Managers  Conferences, congresses, seminars

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Content 1

Industrie 4.0

2

"Plug & Produce" with smart robots

3

Smart automation for small batch sizes

4

Conclusion

Š WZL/Fraunhofer IPT

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Industrie 4.0 Virtualization and Cross-linking Industrie 4.0 Internet of Things, Smart Services

Virtual world

PLM

Fusion

Logistics

ERP

Real world

MES

Business processes Technical processes

Controller

Order Transmission Order Processing Order Disposition Performance Provision Invoicing

Resource Selection Track and Technology Planning Control Simulation Physical Simulation Final Post Processing

Semantics, intelligence / autonomy

Virtualization

Finite-ElementModels State Models

Data Models Analytical Models

Cross-linking HMI / MMI PLM/PDM CAx-Chains Clouds Mobile Ubiquitous Devices Computing

Image sources: Daimler, Chiron, WZL Š WZL/Fraunhofer IPT

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Industrie 4.0 “Plattform Industrie 4.0“ – Industrial roadmap for research & innovation New social infrastructures

1

New business strategies

2

Framework Value-Added Networks

3

Automation of value-added networks

4

Industrie 4.0 by design

5

Integration of real and virtual world

6

Systems Engineering

7

Sensor data analysis

8

Intelligence / flexibility / adaption

9

Multimodal assistance systems

10

Technology acceptance / workplace design

14

14

Wireless communication for I4.0 scenarios Horizontal integration Security & Safety of value-added networks CPS platform

© WZL/Fraunhofer IPT

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9

micro-electronics

13

12

10

11 Basis technology of ICT and 12

Basic technologies 11

2035 2025 5

2018 6

2015

4 3 2 1 8

7

Vertical integration and cross-linked production systems

Engineering continuity / consistency along the entire lifecycle

Source: Plattform Industrie 4.0 Seite 7

Industrie 4.0 The Aachen Perspective cyber

Single Source of Truth ERP systems

IT Globalisation

PLM/Engineering systems

 Big Data  Assessing and Storage in the cloud  Data mining, safety, security  High Speed Computing 4th Industrial (R)evolution

Local data storage

Collaboration productivity - Human / Human - Human / Machine - Machine / Machine

physical

Cooperation

Software © WZL/Fraunhofer IPT

 Business Communities  Social Communities

Cognitive System

 Adaptation by sensors  Intuitivity, reliability  IT-Openness  Cost-efficiency  Robustness

Automation

Hardware Seite 8

Industrie 4.0 Cross-linking in production systems Cross-linked levels of production Automation technology ERP

Planning level

MES Control level Cell control

Today:  (Rigidly) clocked automated production and assembly  Manual operations

Cell level NC, RC, PLC Controller level Input/Output signals Field level

Vision:  Plug & Produce for flexible processes  Efficient process control, also for small batch sizes

Process © WZL/Fraunhofer IPT

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Content 1

Industrie 4.0

2

"Plug & Produce" with smart robots

3

Smart automation for small batch sizes

4

Conclusion

Š WZL/Fraunhofer IPT

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"Plug & Produce" with smart robots Flexible automation with human-robot cooperation Flexibility / Plug & Produce Mechanical interfaces

Electrical interfaces

Controller interfaces

Ramp-up / restart  “Skin” of robot with capacitive, contactless

proximity sensors  Activation distance of approx. 50 mm  No additional safety equipment required,

certified by the German Berufsgenossenschaft (BG)  Task-oriented programming for fast process

ramp-up

Reduced time for ramp-up / setting-up © WZL/Fraunhofer IPT

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"Plug & Produce" with smart robots Flexibility by sensors – „Bin Picking“

Optical data acquisition and evaluation

Object identification and part selection

 Complex interaction between – 3D image recognition software – Robot – Machine Control  Advantage: Chaotic buffering of workpieces  Drawbacks: Lack of determinism in output rate,

Collision-free removal of parts

Critical analysis of required quantity, productivity and process change flexibility

productivity Image source: Liebherr, IPA © WZL/Fraunhofer IPT

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"Plug & Produce" with smart robots Reducing ramp-up efforts with multimodal interfaces Einschraubverbindung

Durchsteckverbindung

Assembly design Geometry models, Assembly features, Assembly relations

Virtual process planning

“Process interface”

Process Prozess

Process modelling “STEP Assembly”

Specification: automated process

Manual demonstration, Job list Assembly automation

© WZL/Fraunhofer IPT

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"Plug & Produce" with smart robots Acquisition of assembly operations “Kinect” sensor

Tool magazine “Leap Motion” sensor

Assembly group Assembly parts

© WZL/Fraunhofer IPT

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"Plug & Produce" with smart robots Machine-robot integration – Multi-technology platform Laser safety enclosure with active detectors

Integrated, sealed and rotating robot enclosure

Main spindle for milling, laser patterning and laser hardening

Media supply of the laser heads with subsequent positioning and length adjustment

Magazine for laser units Robot with laser head for laser cladding and laser hardening

Equally accessible work spaces with rotary tilting tables

Š WZL/Fraunhofer IPT

Integrated control panel for all machine components

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"Plug & Produce" with smart robots Machine-robot integration – Spectrum of integrated technologies

Š WZL/Fraunhofer IPT

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"Plug & Produce" with smart robots Vision: Simultaneous machining with two tools  Machine tool: milling – Machine main spindle Machine spindle – Cartesian-serial kinematics (X / Y / Z) for tool movements – Rotational movements via rotary tilting table Workpiece RotaryTilting-Table

 Robot: deburring – Deburring spindle – Six axes  Process planning – Approach 1: master-slave path – Approach 2: simultaneous path planning  Collision avoidance (offline / VNCK,

possibly online)  Interpolated motions set by NC

controller (master) – Motion correction – Compensations (thermal, wear, …) © WZL/Fraunhofer IPT

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Content 1

Industrie 4.0

2

"Plug & Produce" with smart robots

3

Smart automation for small batch sizes

4

Conclusion

Š WZL/Fraunhofer IPT

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Smart automation for small batch sizes On the way to intelligent, model-based production control Conventional Production Control

Challenge Mass Customization

Intelligent, model-based Production Control Specification „what“ the target of production is

Specification „how“ production has to be carried out Alternative process sequences Procedures and processes as central, stringent sequences of instruction

Different efforts Alternative process parameter Multi-criteria optimization

Suitable for durable process chains in mass production

Information / objectives embedded in workpieces and production means Decentralized, semi-autonomous control units

Short product lifecycles Mutability

Flexibility in procedure and process changes

Individualised products Image Sources: Siemens, WZL © WZL/Fraunhofer IPT

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Smart automation for small batch sizes Viable MES for multi-dimensional optimisation of production ERP

MES

Optimisation of throughput time

Production line

Multi-level optimisation Object-oriented communication model

VSM Level 1

Cell 1

Cell 2

VSM Level 2

Common product and process model Machine 1 VSM MES ERP OPC UA

Viable Systems Model Manufacturing Execution System Enterprise Resource Planning Object Linking and Embedding Unified Architecture

Š WZL/Fraunhofer IPT

Optimisation of energy consumption

Machine 2

Machine 3

VSM Level 3

Combination of centralised and decentralised optimisation

(IT) plant topology of Viable MES

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Smart automation for small batch sizes Production control with semantic interpretation of product models Conventional control Printer Control Unit Manual requirements/ rigid specification of order data

Print image, position, orientation, target element

MES reads in the print job with order data

Control based on product models Modeling of product and plant functions

Semantic product model

MES reports provision of building elements to printer Š WZL/Fraunhofer IPT

Triggers Print Job

Response to MES

Suitable for fixed data flows

Printer Control Unit

Flexible changes of data interfaces

Product model (Data source) Model interpretation Function model (Action and data needs)

Triggers Print Job

Response to MES

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Smart automation for small batch sizes SmartFactoryAC – Individualised Production Injection molding process with stem-form

Planning level

Customer Standard part

Order dispatching

Product design Guidance level

ERP MES

Cell level Control level Field level

Manufacturing / production process

Process level

Individualized manufacturing

Assembly RFID-Tag Š WZL/Fraunhofer IPT

Pre-palletizing

Transport to printer

Individual color printing

Component assembly with robot cell

Cube assembly with FlexPicker Seite 22

Smart automation for small batch sizes Cognitive control for assembly processes Comparison real / virtual

Cell controller Cognitive sequence control Knowledge (models)

Modelling

Objectives

Cognitive architecture Example: Handling and assembly Target model

Simulation

Assembly system Š WZL/Fraunhofer IPT

Cell model

Operation

Paradigm change: Determination of sequences by objective analysis

Connection to automation periphery Product / assembly grp.

Production environment Seite 23

Smart automation for small batch sizes Planning and control of processes in a cognitive assembly cell

CAD Modell Assembly sequences

Cognitive sequential control

Sequence planning Sequence control

Optical tracking and component detection

Part identification

Grasp-planning

Sensory-guided joining

Joining

Geometrical grasp-planning Š WZL/Fraunhofer IPT

Validation

Validation with color and depth information Seite 24

Content 1

Industrie 4.0

2

"Plug & Produce" with smart robots

3

Smart automation for small batch sizes

4

Conclusion

Š WZL/Fraunhofer IPT

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Conclusion

“Industrie 4.0” Chances for robotics and automation Plug & Produce, self-awareness

Semantic models, cognition

Higher flexibility, faster changeovers

Reduced front-loading / engineering efforts

© WZL/Fraunhofer IPT

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