Introduction to PowerMill Robot in Subtractive Manufacturing
Automate - Advanced Robot Manufacturing Driving Growth … Manufacturing Process (in review) Robot...
Transcript of Automate - Advanced Robot Manufacturing Driving Growth … Manufacturing Process (in review) Robot...
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Advanced Robot Manufacturing Driving Growth through Increased
Production Flexibility
Larry M. SweetGeorgia Tech Institute for Robotics and Intelligent Machines
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Explosive Robot Growth through 2025
• 11% CAGR unit growth through 2025
Source: BCG report “The Robot Revolution”, September 2015
• Cost reductions: • Robot HW / SW, perception• System engineering• Safety systems
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Potential for Automation
Source: McKinsey & Company, “The Technical Potential for Automation in the U.S.”, 2016
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Growth is Top Priority for Industry
• OEM’s, Tier 1 suppliers, SME’s all see growth as priority
• Increased flexibility: increase capacity for new customers and products
• Major positive economic impact through the entire supply chain
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Will Robots Provide Sufficient Flexibility?
How to produce higher product mix demanded by customers?
How to delivery when they want it (and return it too)?
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Advanced Robot Manufacturing Priorities
• Increase production rates 30% to 50%
• Mass manufacturing -> mass customization
• Multiple manufacturing processes
• Reduce cost of integration
• Humans and co-robots working in limited floor space
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Potential for 30% to 50% Gains
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Consumer goods(in production)
Automated DC(in production)
Aerospace(opportunity)
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High Impact Emerging Technologies
Topics for today:• Autonomous mobility
• Fixtureless manufacturing
• Collaborative robots
• Virtual product / process development
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Flexible Consumer Goods Production
• New material flow, food processes, controls, robotics
• All products, flavors, sizes on each line
• 50% improvement in asset utilization
• Sales growth through increased product mix
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50% boost
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Why Are Mobile Robots Disruptive?
• New levels of performance• Transactions per hour
• Sequencing
• Storage density
• Easy to re-configure• Change in SKU mix or velocity
• Change in business model
• New algorithm challenges• Replace fixed hardware with flexible
software
• Leverage research on autonomous mobile robots
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Sequencing with Autonomous Robots
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Production order: 395 SKU, 476 cases
Aisle number
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Fully Automated Distribution Centers
• DC’s for retail, grocery, food service, beverage
• 40% gain in storage density, sequenced output for mixed palletizing
• Reduce need for capital expansion, largely pays for automation
• Locate closer to urban centers for same day delivery
• Five to ten leading integrators moving to mobile robot technology
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Aerospace Fixed Assets
Increase asset utilization• More units / month
• Lower fixed costs / unit
• Lower changeover costs
Painting
InspectionAssembly
Drilling
Composites
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Flexibility Options
Collaborative robots Mobile robotsIndustrial robots
Flexible fixtures No fixtures
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Assembly with Shims and Positioners
Aircraft assembly shims
Composite structure
Adjustable assembly positioners
Sources: Aviation Week, April 17, 2015; Duqueine Group, Airbus A350; Assembly Magazine, October 3, 2014..
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3D Surface Scanning of Workpiece
• Measurements of mating surfaces
• Laser tracking
• Laser scanning
• Photogrammetry
• Machine excess material to net shape
Source: “ Interface Management in Wing Box Assembly”, B. Chuovion, A. Popov, S. Ratchev, C. Mason, M.
Summers, SAE 2011-01-2640.
Cutting path to eliminate clash between rib feet and cover (U. Nottingham, Airbus)
3D workpiece surface scan (Georgia Tech)
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Fixtureless Aerospace Manufacturing
Technical challenges• Robot absolute position accuracy
• Robot compliance subject to machining forces
• Resonance excitation during machining
• Variable depth of cut due to workpiece tolerances
Future vision
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Flexible Robotic Machining Technology Roadmap
• Robot, workpiece, fixture compliance models
• Redundancy resolution to maximize stiffness
• Real-time robot control
Shreyes add sensor photo here
Robot complianceMetrology Process knowledge
• Workpiece and feature
location in world, robot
coordinates
• 3D surface scanning
• Secondary encoders
• Wireless sensing for in-situ process monitoring
• Process modeling
• Sensor and process model integration into robot control
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Metrology and Enhanced Accuracy
ElectroimpactTM contributions:• Role of laser tracking of EOAT for absolute accuracy
• Detailed kinematic and stiffness models
• Secondary encoders
• Demonstration of accurate drilling under +/- 0.125 mm
Source:. “Applied Accurate Robotic Drilling for Aircraft Fuselage”, R. DeVlieg and T. Szallay,
SAE Paper 2010-01-1836.
Laser tracking EOAT Joint secondary encoder Laser tracking EOAT
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Compliance Optimization
• Detailed measurements of joint and link static stiffness
• Redundancy resolution exploits extra degrees-of-freedom to find optimal stiffness
• Optimization should be task specific, along vector of machining forces
Source: “Joint stiffness identification of six-revolute industrial serial robots”, C. Dumas, S. Caro, S. Garnier, B.
Furet, Robotics and Computer-Integrated Manufacturing 27 (2011) 881–888.
Source: T. Cvitanic, V. Nguyen, L. Sweet, Collaborative and Advanced Robotic Manufacturing Lab, Georgia Tech
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Stiffness Optimization for Milling
Fraunhofer Institute (IPA), TU Stuttgart contributions:• Non-linear joint compliance experiments
• Redundancy resolution for optimal stiffness
• Open loop machining yields significant tolerance variation
Source: Julian Ricardo Diaz Posada, Ulrich Schneider, Arjun Sridhar and Alexander Verl, “Automatic Motion
Generation for Robotic Milling Optimizing Stiffness with Sample-Based Planning”, MPDI Machines, 2017, 5, 3.
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Machining Process Sensor Feedback
Low cost, thin film PVDF wireless sensing for in-situ process monitoring of cutting forces up to 10 MHz
Machining process modeling
Sensor and process model integration into robot control yields > 70% error reduction
Wireless tool force sensor Undercut coordinates CMM measurements
Source: “A Wireless Force-Sensing and Model-Based Approach for Enhancement of Machining Accuracy in Robotic
Milling”, L.Cen, S.Melkote, J. Castle, H. Appelman, IEEE/ASME Trans. on Mechatronics, vol. 21, no. 5, Oct 2016.
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Dynamics and Chatter Control
Georgia Tech contributions:• Congruent Convergent Transformation (CCT) based mode coupling chatter
avoidance
• Expands range of permissible tool feed orientation or workpiece orientation
Source: “ CCT-based Mode Coupling Chatter Avoidance in Robotic Milling ”, L.Cen, S.Melkote, Journal of
Manufacturing Process (in review)
Robot mounted milling tool Chatter avoidance demonstration
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Roadmap to Accurate Machining
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Future of Collaborative Manufacturing
Aerospace industry example
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Standards for Safe Human Interaction
• ISO/TS 15066 Technical Specification• Safety-rated monitored stop
• Hand guiding
• Speed and separation monitoring
• Power and force limiting
• Still requires full risk assessment • Includes all equipment and processes in
cell
• following industry best practices (FMECA)
Series elastic actuators (ReThink)
Current / encoder based torque estimates (Universal)
Pressure sensitive skin (Kuka)
Load cell in base (Fanuc)
Power and force limiting approaches
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Sequential Process Example
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Collaborative Operations
• Collaborative operations• Robot and human perform tasks
simultaneously during task execution
• Sequential process flow• Most common implementation today
• Human and CoBot have separate tasks
• Buffers in between to prevent starving / blocking
• Task sharing• Human and CoBot work at team
• Requires higher level of coordination, communication, flexibility
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Extending CoBot Perception to 3D
Part flat on table Part tilted
2D vision pick limited to parts on flat surface 3D vision tracking on flexible conveyor
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3D Vision Tracking for Moving Targets
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Robot Operating System (ROS) Enabled Integration
Universal Robot Kuka KR 500 ReThink Robot Kuka KR 210 (3)
Laser tracker / scanner Tool force sensor Milling head 3D vision tracker
Liquid cooled GPU-based controller
Collaborative and Advanced Robotic Manufacturing Lab (Georgia Tech)
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Hardware Robot Operating System (H-ROS)
Source: “The shift in the robotics paradigm — the Hardware Robot Operating System (H-ROS), an infrastructure to
create interoperable robot components”, V. Mayoral, A. Hernandez, R. Kojcev, I. Muguruza, I. Zamalloa, L. Usategi,
A. Bilbao.
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Multi-Level Human Collaboration
• Planning, scheduling, flow
• Re-planningTask level
• Robot path / process planning
• Mobile robot route planning
• Adaptive controlControl level
• Machines, processes, fixtures
• Machine, process, perceptionMachine level
Planning and control levels Human interaction
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Contact
Larry M. Sweet
Associate Director
Georgia Institute of Technology
Institute for Robotics and Intelligent Machines
Collaborative and Advanced Robotic
Manufacturing Laboratory
Atlanta, Georgia
USA
770.862.1953
www.gtcarmlab.net