RA1: Advanced Manufacturing Technology
WP1 – Robust and Adaptive Manufacturing Systems
GOAL:
Develop system concepts for automated manufacturing with high performance based on integration and
adaptivity in manufacturing systems
WP2 - Advanced Process Control
and Intelligent Maintenance
GOAL:
Develop knowledge, tools, and concepts for
advanced process control and intelligent
predictive maintenance of equipment for high
performance manufacturing
WP3 - Hybrid Manufacturing
GOAL:
Develop the concept and principles for a hybrid
manufacturing system
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Collaboration between WPs
Research area 1:
Advanced Manufacturing Technology
WP5
T3
Work
Organization
T5
WP1 - Robust and Adaptive
Manufacturing Systems
WP2 - Advanced
Process Control
and Intelligent
Maintenance
WP3 - Hybrid
Manufacturing
WP4
T2
Planning and
Control
T4
WP6
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PhD involvement
WP1
T1:
T2:
T3:
T4:
WP2
T1:
T2:
T3:
T4:
T5:
WP3
T1:
T2:
T3:
Robust and Adaptive Manufacturing Systems
Implications of the concept of the constantly changing manufacturing system for:
Study new design methods for manufacturing control based on an agent-oriented
bottom-up approach
Develop and integrate new agent-oriented design tools in the APROX framework
Define operator information and control requirements in highly automated
manufacturing environments - work organization and demand for skill development
Define handling characteristics for non-rigid materials
Advanced Process Control and Predictive Maintenance
Sensor and sensor system development and integration for measurement of critical
process parameters
Control strategies and methods for self-adjusting, -calibrating and -reconfigurable
processes
Fault diagnosis and prognosis system for preventive maintenance of production equipment
3D-object measurement and inspection on the basis of 3D point clouds
Operator decision-support: strategies, models and tools for effective problem solving based
on a combination of operator/specialist knowledge and monitoring of measured or estimated
process
parameters
Hybrid manufacturing
Development of a hybrid manufacturing cell by integration of additive manufacturing with
conventional CNC milling
Case studies: principles for enhanced tooling capability and high performance parts by
incorporation of complex geometries and variable material composition for advanced thermal
management and directed part material properties
Design for performance: design principles to exploit the possibilities of the Hybrid
Manufacturing concept
Task in all WP's: International collaboration and network
building
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PhD involvement
WP1
T1:
T2:
T3:
T4:
WP2
T1:
T2:
T3:
T4:
T5:
WP3
T1:
T2:
T3:
Robust and Adaptive Manufacturing Systems
Implications of the concept of the constantly changing manufacturing system for:
New design methods: symbolic communication between machines/devices. For such
communication, both software and hardware of present equipment must be extended.
Develop and integrate new agent-oriented design tools: systems, e.g. assembly systems,
capable to work in not well structured environment.
- work organization and demand for skill development
Define handling characteristics for non-rigid materials
Advanced Process Control and Predictive Maintenance
Sensor and sensor system development and integration for measurement of critical
process parameters: Sensor networks capable of acquiring symbolic data
Control strategies and methods for self-adjusting, -calibrating and -reconfigurable
processes: strategies and methods based on symbolic data mining and optimization.
Solutions imitating biological reflexes
Fault diagnosis and prognosis system for preventive maintenance of production equipment
3D-object measurement and inspection on the basis of 3D point clouds
Operator decision-support: strategies, models and tools for effective problem solving based
on a combination of operator/specialist knowledge and monitoring of measured or estimated
process parameters: HMI communicating with operators on the symbolic level
Hybrid manufacturing
Development of a hybrid manufacturing cell by integration of additive manufacturing with
conventional CNC milling
Case studies: principles for enhanced tooling capability and high performance parts by
incorporation of complex geometries and variable material composition for advanced thermal
management and directed part material properties
Design for performance: design principles to exploit the possibilities of the Hybrid
Manufacturing concept
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Results from RA1
WP1 Robust and Adaptive Manufacturing Systems
Control logic verification
Before
Now
Programming logic
in QUEST* syntax
Programming logic
in target language** syntax
'Verified' control logic
Truly verified control logic
in real equipment environment
*QUEST simulation software
**Python
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Results from RA1
WP1 Robust and Adaptive Manufacturing Systems
Now
Control logic verification
Programming logic
in target language** syntax
Truly verified control logic in
emulated equipment environment
Switching to real
equipment environment
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Results from RA 1
WP2 Advanced Process Control and Predictive Maintenance
1.
Flexible, automated sewing further developed:
+ A software has been developed for integration of control of robot,
PyMoCo and ROS
+ Real time control has been tested and promising results have been
achieved for 8 milliseconds control.
+ A new speed sensor (mechanics and electronics) has been developed.
The sensor will be used for measurements required for further
development of the control system for the sewing cell.
= Sew together parts of different shapes and materials, without prior
knowledge of the part geometries
2.
A predictive maintenance model has been established in order to
obtain optimal maintenance scheduling based on the condition of
the equipment.
3.
RFID techniques in condition monitoring has been researched,
and a demo of RFID application in production system has been
established.
4.
A dual arm robot installation is being built
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Results from RA 1
WP3 Hybrid Manufacturing
1.
A new method for preparing the substrates for
additive manufacturing in a CNC milling machine has
been developed.
2.
The cohesion of the AM section to the base part has been
tested with excellent results (Marlok C1650+ CL 50WS AM tool steel).
3.
Porous sections built into the tool insert derived as a
valuable complement to other practical solutions
4.
A prototype integrated control system for the hybrid
cell (OMOS) has been further developed, in collaboration
with exchange student from Slovenia.
5.
A prototype of the hybrid cell control system has been
developed.
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Other results
New projects:
•
Autoflex - Flexible automated manufacturing of large and complex products: Partners: RollsRoyce Marine AS, Benteler Aluminium Systems Norway AS, Intek Engineering AS, SINTEF
Raufoss Manufacturing AS and NTNU.
•
SmartTools: Partners: Sandvik Teeness AS, SINTEF ICT, SINTEF Raufoss Manufacturing
and NTNU IPK
Contribution to education:
•
The Framework of IFDPS becomes a part of a course (TPK 4155 Applied Computational
Intelligence in Intelligent Manufacturing)
•
The RFID application demo for Production System becomes a practice study for a course
called PK8106 Knowledge Discovery and Data Mining
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International collaboration within RA1 in
2012:
Chairman from Industry for Joining Sub-Platform: SFI Norman and SINTEF Raufoss Manufacturing AS
have worked actively in Manufuture by participating in the HLG. As a result Kristian Martinsen now holds
the chair, as an industry representative, for the new sub-platform for Joining.
Exchange agreement with four students from Ensiame Engineering School, Valenciennes, France. Have
been working on design of a flexible jig for assembly of components for Sandvik Teeness and a dual arm
robot installation.
Collaboration through the development of the new ISO standard on additive manufacturing technology
does now include the chair for ISO/TC261 WG1 Terminology for additive manufacturing.
DTI (Denmark), VTT (Finland), Acreo (Sweden), Fraunhofer (Germany): collaboration on coatings,
integrated sensors and new business models for injection molding industry.
Two new EU-projects have been granted, SASAM and Diginova, where SINTEF Raufoss Manufacturing is
a partner. Diginova, short for Innovation for Digital Fabrication, is a coordination and support action
project under NMP 7th FP, Networking of materials laboratories and innovation. SASAM, which is short
for Support Action for Standardisation in Additive Manufacturing, is a similar type of project.
Collaboration on a EU-proposal "VITAMIN", where Sandvik Teeness was partner together with SRM and
SINTEF ICT from Norway. Not granted.
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Planned international collaboration within
RA1 for 2013:
Polytechnic Institute of Braganca, Portugal:
•
Prof. Paulo Leitaõ: workshop around holonic manufacturing, common publication or similar.
The University of Manchester, UK: Dr. Yi Wang:
•
•
establishing projects on Intelligent systems and Predictive Maintenance.
Common publication: a book on data mining for zero-defect manufacturing
VTT Technical Research Centre of Finland, +rest of consortium
•
EU proposal for call FoF.NMP.2013-7 "New hybrid production systems in advanced
factory environments based on new human-robot interactive cooperation":
University of Ljubljana:
•
Prof. Slavko Dolinsek and student David Homar, continue collaboration on development on
OMOS (Optimized Manufacturing Operation Sequence)
University of Berlin (???? ):
•
Prof. Günther Seliger: workshop around flexible automation and possibly researcher exchange?
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More detailed on results
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Some results from RA1
Substrate preparation
• Flat milling produces a glossy surface;
– Low-friction for powder spreading
– Reflective to laser beam
• Standard procedure: Sand blasting, -unsuitable for the
hybrid cell
• Hybrid cell procedure: Extra sharp cutting tool inserts
"scratch" the substrate
– Provides an exact z = 0 -point for starting the AM building
• Edge radius: 0 – 0.1 mm;
• Cutting depth: 0.1 mm;
• Feed rate: 0.05 mm/O
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OMOS:
Optimized Manufacturing Operation Sequence
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Some results from RA1
WP3: Industrial case studies: insert for a bracket to an office chair
Results:
• Cooling time for conventional insert and “old” design 70 sec.
– Estimated cooling time with new design approximately +25 sec. = 95
sec.
• Cooling time with new design and conformal cooling insert: 48 sec.
• Cost of machining AM produced insert similar to conventional
production, however the cost of AM makes this an expensive insert
Industrial need: reduced cost of production by AM closer to final shape
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Demonstrator development
Working principle:
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Demonstrator development
Example:
System Frame of IFDPS – Intelligent Fault Diagnosis and Prognosis System
Degradation Process
Equipment or Process
Maintenance Scheduling
/ Maintenance Optimization
Information Delivery
Fault Prognosis
Bee Colony Algorithms (BCA)
Auto-regressive Moving
Averaging (ARMA)
Ant Colony Optimization (ACO)
Fuzzy Logic Prediction
Particle Swarm Optimization (PSO)
ANN Prediction
Gentic Algorithms (GA)
Match Matrix Prediction
Meta-Heuristic approaches
Fault Diagnosis
Support Support Machine
(SVM)
Sensors
(Data Acquisition)
Signal Pre-process
Feature Extraction
Denosing
Time Domain
Compression
Time-Frequency
Domain
Extract Weak Signal
Frequency Domain
(FFT, DFT)
Filter
Wavelet Domain
(WT, WPT)
Amplification
Principal Component
Analysis (PCA)
Data Mining (Decision Tree &
Association rules)
Artificial Neural Network
(SOM & SBP)
Statistical Maching
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