Sveising, en viktig anvendelse av industriroboter€¦ · Sveising, en viktig anvendelse av industriroboter Professor Olav Egeland SMART Sveis konferansen 2019 X-Meeting Point, Hellerudsletta

Norwegian University of Science and Technology

Sveising, en viktig anvendelse av

industriroboter

Professor Olav Egeland

SMART Sveis konferansen 2019

X-Meeting Point, Hellerudsletta

2019-03-04

Norwegian University of Science and Technology 2

Robotics and Automation Group

Department of Mechanical and Industrial Engineering, NTNUProfessor Olav Egeland, Head of Group

Professors

• Olav Egeland: Production Automation

• Amund Skavhaug: Embedded Computer Systems

• Christian Holden: Subsea Control Systems

• Lars Tingelstad: Robotic Production

Lecturer

Kristine Thevik: Robotics and Mechatronics

Professor II

• Stig Pedersen, IIoT and Industry 4.0

• Gunleiv Skofteland, Offshore Control Systems

Research Engineer

• Dr. Adam Leon Kleppe


Professor Olav Egeland: PhD Students

1. Geir Ole Tysse. Computer vision and 3D sensors for topside automation. SFI Offshore Mechatronics.

2. Lars Giske. Robotic Cleaning of Salmon Processing Lines. Industry PhD. SeaSide.

3. Emil Bjørlykhaug. Robotic Cleaning of Salmon Processing Lines. Industry PhD. SeaSide.

4. Eirik Njåstad. Robotic Milling of Sand Molds for One-Piece Casting of Al and NiAl. Oshaug Metall.

5. Andrej Cibicik, Nonlinear friction and elasticity in motion compensation systems. SFI Offshore Mechatronics.

6. Aksel Sveier. Vision Systems for Offshore Crane Control in Ship-to Ship Operations. SFI Offshore Mechatronics.

7. Alexander Sjøberg. Sensor Fusion, Point-Clouds and 3D Maps Using Geometric Algebra. SFI Offshore Mechatronics.

8. Njål Munthe-Kaas: Robotic Welding of Offshore Steel Jackets: Kværner.

Professor Amund Skavhaug: PhD Students

1. Jan Sramota. Information security for operational safety in Industry 4.0 based production. NTNU Position

2. NN. 5G networks as fieldbus in robotic cells under Industry 4.0. Telenor, EU

Associate Professor Christian Holden: PhD Students

1. Sveinung Ohrem. Control of Subsea Processes. SFI SUBPRO.

2. Torstein T. Kristoffersen. Modeling and Control of Subsea Processing. SFI SUBPRO.

3. Mishiga Vallabhan. Process Control Algorithms for Subsea Separation. SFI SUBPRO.

4. Savin Viswanathan. Integrated simulation of multi-physical systems in offshore operations. SFI Offshore Mechatronics.

5. Njål Tengesdal. Component-based simulation systems for drilling automation and crane systems. SFI Offshore

Mechatronics.

Associate Professor Lars Tingelstad: PhD Students

1. Mohammed Shafi. Robotic assembly using optimization and vison sensors. MTP funding.

Robotics and Automation Group: PhD Students


Quantec Robot Cell

Robot Cell for Heavy Welding and Assembly

• 2 Large 120 kg Robots

• 2 Welding Robots

• Fronius TransSteel MIG/MAG welding system

• Vision, Force Control

• Off-line Programming

• KUKA Industrial control system with ROS extension


Utfordringer med innføring av robotisert produksjon

Problemer med dagens teknologi:

• Proprietære protokoller

• Betydelig engineering av avanserte celler

• Tidkrevende kalibrering og igangkjøring

3D kamera

Robot-kontroller

PC

Seise-utstyr

GS

PLS

GS

GriperSikkerhets-utstyr

Frese-verktøy

Robot-kontroller

GS

Ethernet

EtherCAT

EtherCAT

EtherCAT

PROFINET

PROFINET

PROFINET

EtherCAT

Modbus

Robotcelle ved MTP


Industri 4.0:

Robot cell

Production Line World

Internet of Things Security

3D

Graphics

Assembly

instructions

Operator

CAD models

MonitorCloud Mobile app Internet

Main elements

• Cyber-physical systems with local intelligence

• Internet of Things

• Sensors

• Digital models

Main principles

• Open protocols for communication

• Local intelligence

• Automatic configuration

• Functional integration

PLC

Vision System


Industri 4.0 for Norwegian Industry

Characteristics of Norwegian production:

• Small series, ETO

• Advanced products of high added value

• Specialized competence towards

– Maritime sector

– Oils and gas industry

– Renewable energy

– Fish farming

• Commercially available robotics is specialized for the automotive industry

• Norwegian industry requires

– Rapid changeover between different product variants

– Advanced robotic systems that are profitable for small series


Kyber-fysiske systemer

• Et fysisk system med innebygd datakraft, sensorsystemer

og tilkobling til Internet of Things

• Funksjonell integrasjon benyttes for å oppnå:

– Automatisk tilkobling og konfigurering

– Automatisk kalibrering

– Styring ved kommandoer på høyt abstraksjonsnivå

– Endring av funksjonalitet

– Styring og regulering

Eksempler på kyber-fysiske systemer:

• Roboter

• AGV’er (mobile roboter)

• Kamerasystemer

Internet of Things

Kyber-fysisk system 1

Kyber-fysisk system 2

Kyber-fysisk system N

Kyber-fysiske systemer


Digital twin: A digital copy of a production line

• For simulation of production

• 3D graphics

Applications

• Planning and design of new production facilities

• Planning and design of layout of robot cells

• Planning og logistics and production of variants

• Supervision and control in real time

• Report generation:

– Product flow through production line

– Bottleneck reporting

– Product in internal storage

– Efficiency of machine centers

Machine Center 1

Storage Paint Line

Digital twins

Machine Center 2

Storage

https://youtu.be/HPRURtORnis?t=5m6s


Robot Technology Levels

Teach-Pendant

Programming

Offline Programming

Digital factories,

cyber-physical

systems and IoT

Simple tasks, small

batches

Advanced products, larger

batches

Technological

sophistication

Off-the shelf industrial technology Advanced industrial

technology

Next generation production

systems

Level of

Competence

Integration of CAD,

robotic production and

vision systems

Automatic one-piece

productionIndustry 4.0


IPN: AutoKons: Automatisert produksjon av store, grove

stålkonstruksjoner

• Mål:

– Intelligente robotsystemer som kan håndtere variasjoner uten behov for omprogrammering

– Moderne sensorteknologi for optimalisering av produksjon i forkant av sveisingen.

– Robotisert sveising for å oppnå innovasjoner i sveiseprosess og kvalitetskontroll

– Lønnsom produksjon av stålunderstell til offshore plattformer i Norge

• Industripartnere: Kværner Verdal, Vitec AS og Fosdalen-Industrier AS

• Forskning: SINTEF Manufacturing, SINTEF Industri og NTNU MTP

• Robot sveiselab ved ManuLab i Perleporten


KPN: Robotic welding of aluminium hulls

• Goal:

– New technology and methods for efficient robotic welding of large aluminium structures.

– Cost effective production of aluminium hulls in Norway

• Integration of CAD, ship design, robot programming and welding

• Improved control and documentation of welding process with possible design implications.

• Project Manager: Professor Olav Egeland, NTNU

• 4 PhD scholarships.

• Industry partners: Hydro, Fjellstrand, Leirvik, Digitread

• The project will use the new robotic welding lab of ManuLab

– Welding lab: 6.5 mill. NOK

– Industry 4.0 lab: 6 mill NOK

Project start: Q2 2019


Traditional geometry

with shear connectionFloating frameGeometry without

shear connection

±X

mm

Shear connection

using extra part

Alternative designs for welding of aluminium hulls


Welding of aluminium: Some challenges

• Aluminium deforms to a larger extent than steel under welding.

• Less material may be added than for steel

• Sensors are important to compensate for deformation

• The surface oxide has a high melting temperature and may cause reduce weld quality.

• Aluminium welds absorbs hydrogen

• Tracking of weld with pendulum motion is more difficult than for steel.

• The surface oxide makes touch sensing more difficult than for steel

• The aluminium surface is highly reflective, which is a challenge when robot vision and laser systems are used


Integrated Design, Production and Documentation

• Integration of CAD systems and 3D simulation tools are available for production lines and robots

– Siemens NX, Teamcenter and Tecnomatix Robot Expert

– Dassault: CATIA and Delmia

– Visual Components, KUKA.Sim, ABB Robot Studio

• Automatic production of product families based on CAD is possible

• Robot vision is used for calibration of geometry

• Welding parameters can be included in the CAD

• Metrology and documentation can be integrated


HMS, kvalitet og effektivitet

Dagens fabrikker er rene, ryddige og elegante.

Dette fremmer HMS, kvalitet og effektivitet.

NTNU ønsker å bidra til denne trenden slik at

studentene blir vant til en høy standard på

laboratorier og verksteder

Festo, Scharnhausen

Robot Werlding lab, NTNU MTPIndustry 4.0 Lab, NTNU MTP

Laser Welding

Lab


Industri

4.0 lab

• 5 Robots

• One collaborative robot with 7 joints

• Two 7DOF robots

• Two KUKA KR6 Agilus

New Robot Welding Lab at NTNU


AGV-lab

• 2 robots

• Each AGV has a 7DOF robot arm.


Robotic

Welding

• 8 robots

• MIG, TIG og CMT welding

• Grinding and polishing capability

ROBOTIC WELDING LAB

ROBOTIC WELDING LAB

Large welding cell

• 1 stationary robot

• 1 robot on track

• Positioning table with 2 axes and 5 tonnes capacity.

Grinding and finishingcell

• 2 large KUKA KR120 robots with 2.5 m reach and 120 kg lifting capacity

• Closed area

Documents

Sveising, en viktig anvendelse av industriroboter€¦ · Sveising, en viktig anvendelse av industriroboter Professor Olav Egeland SMART Sveis konferansen 2019 X-Meeting Point, Hellerudsletta