The Bionic Learning Network of Festo - IFAC 2014 · The Bionic Learning Network of Festo...
Transcript of The Bionic Learning Network of Festo - IFAC 2014 · The Bionic Learning Network of Festo...
CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
The Bionic Learning Network of Festo
Presentation by Dr. Heinrich Frontzek, Festo AG & Co. KG, Esslingen [email protected]
Impulses from nature for sustainable and human-friendly automation
CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
Festo – Facts Festo is a global leader in automation technology and the world market leader in technical education. The objective: maximum productivity and competitive strength for customers in factory and process automation.
• 300,000 customers worldwide • 17,000 employees in 176 countries • Euro 2.3 billion in sales (2013) • Over 7 % of turnover flows into R&D • 2,900 patents worldwide • 100 product innovations per year
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
Festo – competence for factory and process automation Motion Linear motion and rotation, turning, grasping, clamping Control and regulation Position, travel, force, pressure Processes Mixing, dosing, filling, separating Diagnosis Measuring, analysis, visualization
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
Natural phenomena – inspiration for factory and process automation Motion Linear motion and rotation, turning, grasping, clamping Control and regulation Position, travel, force, pressure Processes Mixing, dosing, filling, separating Diagnosis Measuring, analysis, visualization
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
Bionics = biology + technology: interdisciplinary learning from nature Bionics to enhance creativity. Bionics as structured process Top-down process An engineer identifies a technological problem. Together with biologists he searches for biological role models. Example: NanoForceGripper Bottom-up process A biologist discovers a biological phenomenon, which then gets technologically implemented with the help of engineers. Example: adaptive gripper 5
CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
Bionic Learning Network – from the model to the product: “adaptive “
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Natural model Technical principle Bionic adaptation Industrial Application
CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
The Bionic Learning Network of Festo
• Interdisciplinary core team • Specialists from the relevant departments • External development partners • Universities and institutes • Students and trainees • Private inventors
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
Bionic Learning Network – objectives
• Creating networks and discerning trends in research and development
• Motivating people from various sectors to develop their ideas together with Festo
• Initiating dialogue with customers and partners
• Analysing customer feedback on innovative topics at trade fairs
• Expediting product pre-development
We want to provide impetus and initiate innovations.
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
Efficiency strategies in nature Nature has ideally adapted to its surroundings throughout millions of years of evolution. Energy efficiency Animals that use their resources sparingly
gain a competitive advantage. Lightweight design A light but stable skeleton helps save
energy.
Functional integration Weight can be saved when several
functions are integrated into the one element.
Communication and learning Animals that know where and how to find
the best resources have a clear selective
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Efficiency strategies in nature Artificial Intelligence and Communication Human-Machine-Interaction Safe and intuitive • Machine-Machine-Communication • Machine Learning • Condition Monitoring • Process-safety: real-time diagnosis
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
Efficiency strategies in nature Artificial Intelligence and Communication • Human-Machine-Interaction Machine-Machine-Communication
Swarm Behaviour • Machine Learning • Condition Monitoring • Process-safety: real-time diagnosis
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
Efficiency strategies in nature Artificial Intelligence and Communication • Human-Machine-Interaction • Machine-Machine-Communication Machine Learning Condition Monitoring real-time diagnosis guarantees process-
safety
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
Efficiency strategies in nature Energy-efficiency New materials • New kinematics • Energy recovery • Light-weight construction • Function integration
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
Efficiency strategies in nature Energy-efficiency • New materials New kinematics Energy recovery • Light-weight construction • Function integration
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
Efficiency strategies in nature Energy-efficiency • New materials • New kinematics • Energy recovery Light-weight construction Function integration
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
SmartBird - Bird flight deciphered
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
SmartBird – the fascination of bird flight One of the oldest dreams of mankind is to fly like a bird. As long ago as 1490, Leonardo da Vinci built rudimentary flapping wing models in order to come closer to achieving bird flight.
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
SmartBird – Theoretical basis
• Bird flight analyzed in detail • Resource efficient lightweight construction • Function integration of propulsion and lift
in the wings achieved by active torsion • Measurement and control with intelligent
Condition Monitoring
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
SmartBird – Bird flight deciphered SmartBird is an ultralight but powerful flight model with excellent aerodynamic qualities and extreme agility. With SmartBird, Festo has succeeded in deciphering the flight of birds. This bionic technology- bearer, which is inspired by the herring gull, can start, fly and land autonomously – with no additional drive mechanism.
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
SmartBird – Aerodynamic lightweight design with active torsion
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
SmartBird – new approaches in automation Energy-efficient and resource-friendly: The minimal use of materials and the extremely lightweight construction pave the way for efficiency in resource and energy consumption.
Process-safety ensured by Condition Monitoring: Data on SmartBird’s wing position and torsion are constantly registered during flight. Through continuous Condition Monitoring production downtimes will be reduced in the production of the future and the maintenance is facilitated.
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
SmartBird – Bird flight deciphered
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
The dragonfly The dragonfly can carry out highly complicated flight manoeuvres. They can accelerate rapidly, brake abruptly and turn quickly and even fly backwards. They can move in any spatial direction, hover motionless in the air, glide without flapping their wings and change between flight modes. Dragonflies can master all flight conditions of a helicopter, a plane and even a glider.
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© Flickr Christoph Alexander Martsch
© Flickr Ana_Cotta © Flickr whologwhy © Flickr donjd2 © Flickr PaulSteinJC
CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
The dragonfly
• 6000 species • Body length 18 mm – 190 mm • Body weight 0.2 g • Weight of a wing 2 mg • Thickness of a wing 220 µm – 3 µm • Maximum speed 54 km/h • Maximum distance 1000 km
• Wings moved by direct flight muscles • At least 5 muscles per wing • 3-15 neurons control each wing
separately
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
BionicOpter – technical details
• A brushless motor to drive the beat frequency of the four wings. • Each wing is individually twisted by one servo motor • One additional servo motor at the joint of each wing controls the amplitude. 9 degrees of freedom
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BionicOpter – lightweigth design and function integration
Laser-sintered polyamide
Aluminium
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
BionicOpter – lightweigth design and function integration
Laser-sintered polyamide
Aluminium
Deep-drawn ABS terpolymer
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
BionicOpter – lightweigth design and function integration
Laser-sintered polyamide
Aluminium
Deep-drawn ABS terpolymer
Carbon fibres and polyester membrane for the wings
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Structural Analysis of a Dragonfly Wing, Jongerius and Lentink 2001
Structural Analysis of a Dragonfly Wing, Jongerius and Lentink 2001
Structural Analysis of a Dragonfly Wing, Jongerius and Lentink 2001
CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
BionicOpter – positioning of wings Inspired by quadrocopters we changed the positioning of the four wings of the BionicOpter away from a parallel position, by shifting the wings by 30°. Forces are distributed better during forward flight Wings almost touch and close a 360° circle when
hovering MAV is easier to control
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
BionicOpter – vibrations Changing the frequency of an MAV with flapping wing drive can result in vibrations that decrease the energy efficiency, make the system hard to control and can even lead to damage the mechanical components when the vibrations build up. Real dragonflies only rarely move their forewings and hindwings in full synchrony (0° phase shift). highly energy consuming Real dragonflies only rarely move their forewings and hindwings in an opposing fashion (180° phase shift). Hindwing leads this movement in a phase shift of 50° to 100°, energy efficient BionicOpter: forewing leads the movement in a phase
shift
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Dragonflies of the world, Silsby 2001
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BionicOpter – function integration • Sensors, actuators and mechanism along with
communication and control technology are installed within a minimum of space.
• A high-performance ARM microcontroller calculates all the parameters necessary for flight.
• The pilot only defines the direction and speed • Data on the position and the twisting of the wings are
continuously recorded and evaluated in real time . Flight safety ensured by condition monitoring on board
New approaches in automation: • More and more functions will get integrated into the smallest
space • Components will become smarter and more flexible
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
BionicOpter – technical details
• A wingspan of 63 cm
• Body length of 44 cm
• Weight 175 g
• 2 LiPo cells with 7.4 V 350 mA
• 3 min to 4 min of flight time
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
BionicOpter
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
BionicOpter
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
BionicOpter
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CC/Dr. Heinrich Frontzek The Bionic Learning Network of Festo
Thank you very much for your attention
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More info: www.festo.com/bionic