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
Norwegian University of Science and Technology
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
Norwegian University of Science and Technology
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
Norwegian University of Science and Technology 5
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
Norwegian University of Science and Technology 6
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
Norwegian University of Science and Technology
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
Norwegian University of Science and Technology
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
Norwegian University of Science and Technology
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
Norwegian University of Science and Technology
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
Norwegian University of Science and Technology 11
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
Norwegian University of Science and Technology 12
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
Norwegian University of Science and Technology
Traditional geometry
with shear connectionFloating frameGeometry without
shear connection
±X
mm
Shear connection
using extra part
Alternative designs for welding of aluminium hulls
Norwegian University of Science and Technology 14
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
Norwegian University of Science and Technology
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
Norwegian University of Science and Technology
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
Norwegian University of Science and Technology 17
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
Norwegian University of Science and Technology 18
AGV-lab
• 2 robots
• Each AGV has a 7DOF robot arm.
Norwegian University of Science and Technology 19
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