WP1 – Robust and Adaptive Manufacturing Systems
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sfinorman.no 1 WP1 – Robust and Adaptive Manufacturing Systems WP2 - Advanced Process Control and Intelligent Maintenance WP3 - Hybrid Manufacturing GOAL: Develop system concepts for automated manufacturing with high performance based on integration and adaptivity in manufacturing systems GOAL: Develop knowledge, tools, and concepts for advanced process control and intelligent predictive maintenance of equipment for high performance manufacturing GOAL: Develop the concept and principles for a hybrid manufacturing system RA1: Advanced Manufacturing Technology
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GOAL: Develop system concepts for automated manufacturing with high performance based on integration and adaptivity in manufacturing systems. RA1: Advanced Manufacturing Technology. WP1 – Robust and Adaptive Manufacturing Systems. WP3 - Hybrid Manufacturing. - PowerPoint PPT Presentation
Transcript of WP1 – Robust and Adaptive Manufacturing Systems
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WP1 – Robust and Adaptive Manufacturing Systems
WP2 - Advanced Process Control and Intelligent Maintenance
WP3 - Hybrid Manufacturing
GOAL:Develop system concepts for automated manufacturing with high performance based on integration and adaptivity in manufacturing systems
GOAL:Develop knowledge, tools, and concepts for advanced process control and intelligent predictive maintenance of equipment for high performance manufacturing
GOAL:Develop the concept and principles for a hybrid manufacturing system
RA1: Advanced Manufacturing Technology
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WP1 - Robust and Adaptive Manufacturing Systems
WP2 - Advanced Process Controland Intelligent Maintenance
WP3 - Hybrid Manufacturing WP6
Research area 1:Advanced Manufacturing Technology
T3
WP4Planning and
ControlWP5Work
Organization
T4
T2
T5
Collaboration between WPs
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WP1 Robust and Adaptive Manufacturing SystemsImplications of the concept of the constantly changing manufacturing system for:
T1: Study new design methods for manufacturing control based on an agent-oriented bottom-up approach
T2: Develop and integrate new agent-oriented design tools in the APROX framework
T3:Define operator information and control requirements in highly automated manufacturing environments - work organization and demand for skill development
T4: Define handling characteristics for non-rigid materials
WP2 Advanced Process Control and Predictive Maintenance
T1: Sensor and sensor system development and integration for measurement of critical process parameters
T2: Control strategies and methods for self-adjusting, -calibrating and -reconfigurable processes
T3: Fault diagnosis and prognosis system for preventive maintenance of production equipment
T4: 3D-object measurement and inspection on the basis of 3D point clouds
T5: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
WP3 Hybrid manufacturing
T1: Development of a hybrid manufacturing cell by integration of additive manufacturing with conventional CNC milling
T2: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
T3: Design for performance: design principles to exploit the possibilities of the Hybrid Manufacturing concept
Task in all WP's: International collaboration and network building
PhD involvement
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WP1 Robust and Adaptive Manufacturing SystemsImplications of the concept of the constantly changing manufacturing system for:
T1: New design methods: symbolic communication between machines/devices. For such communication, both software and hardware of present equipment must be extended.
T2: Develop and integrate new agent-oriented design tools: systems, e.g. assembly systems, capable to work in not well structured environment.
T3: - work organization and demand for skill developmentT4: Define handling characteristics for non-rigid materials
WP2 Advanced Process Control and Predictive Maintenance
T1: Sensor and sensor system development and integration for measurement of critical process parameters: Sensor networks capable of acquiring symbolic data
T2: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
T3: Fault diagnosis and prognosis system for preventive maintenance of production equipment
T4: 3D-object measurement and inspection on the basis of 3D point clouds
T5: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 levelWP3 Hybrid manufacturing
T1: Development of a hybrid manufacturing cell by integration of additive manufacturing with conventional CNC milling
T2: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
T3: Design for performance: design principles to exploit the possibilities of the Hybrid Manufacturing concept
PhD involvement
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Control logic verification
Before
Programming logic in QUEST* syntax 'Verified' control logic
Programming logic in target language** syntax
Truly verified control logicin real equipment environment
*QUEST simulation software**Python
Now
Results from RA1WP1 Robust and Adaptive Manufacturing Systems
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Control logic verificationNow
Programming logic in target language** syntax
Truly verified control logic inemulated equipment environment
Switching to real equipment environment
Results from RA1WP1 Robust and Adaptive Manufacturing Systems
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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
Results from RA 1WP2 Advanced Process Control and Predictive Maintenance
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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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.
Results from RA 1WP3 Hybrid Manufacturing
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Other results
New projects:• Autoflex - Flexible automated manufacturing of large and complex products: Partners:
Rolls-Royce 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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• 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
Some results from RA1Substrate preparation
• Edge radius: 0 – 0.1 mm; • Cutting depth: 0.1 mm; • Feed rate: 0.05 mm/O
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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 insertIndustrial need: reduced cost of production by AM closer to final shape
Some results from RA1WP3: Industrial case studies: insert for a bracket to an office chair
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Equipment or Process
Degradation Process
Sensors (Data Acquisition)
Feature Extraction
Fault Diagnosis
Fault PrognosisMaintenance Scheduling / Maintenance Optimization
Signal Pre-process
Denosing Time Domain
Time-Frequency Domain
Frequency Domain (FFT, DFT)
Wavelet Domain (WT, WPT)
Principal Component Analysis (PCA)
Compression
Extract Weak Signal
Filter
Amplification
Support Support Machine (SVM)
Data Mining (Decision Tree & Association rules)
Artificial Neural Network (SOM & SBP)
Statistical Maching
Auto-regressive Moving Averaging (ARMA)
Fuzzy Logic Prediction
ANN Prediction
Match Matrix Prediction
Ant Colony Optimization (ACO)
Particle Swarm Optimization (PSO)
Gentic Algorithms (GA)
Meta-Heuristic approaches
Bee Colony Algorithms (BCA)
Information Delivery
Demonstrator development
Example: System Frame of IFDPS – Intelligent Fault Diagnosis and Prognosis System
