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K.U.Leuven Department of Mechanical Engineering Celestijnenlaan 300 B, B-3001 Leuven, Belgium Tel: +32 16 32 24 80 Fax: +32 16 32 29 87 www.mech.kuleuven.ac.be/pma. PMA Production Engineering, Machine Design and Automation. Overview of presentation. General information Facts and figures
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K.U.Leuven Department of Mechanical Engineering Celestijnenlaan 300 B, B-3001 Leuven, BelgiumTel: +32 16 32 24 80 Fax: +32 16 32 29 87 www.mech.kuleuven.ac.be/pma PMA Production Engineering, Machine Design and Automation
Overview of presentation • General information • Facts and figures • Research areas • Spin offs and Technology Centre • Scores related research • Mechatronics • Noise and Vibration • Robotics and Intelligent Machines • Research Infrastructure
General information : Facts and Figures • Staff : 130 • Research projects funded by • university: 30% • governments (European + federal + regional): 36% • industry: 30 % • foreign fellowships: 4% • Students : • Engineering degree : 80 per year • Ph.D. : 8 per year
General information : Facts and Figures • Academic staff (professors) • F. Al-Bender • H. Bruyninkcx • J. De Schutter • W. Desmet • J. Duflou • W. Heylen • J.P. Kruth • B. Lauwers • D. Reynaerts • P. Sas • J. Swevers • H. Van Brussel • D. Vandepitte
Spin offs Name Establ. Area LMS International 1979 Dynamic analysis Data Analysis 1988 Monitoring for Products maintenance Krypton Electronic 1989 Measuring & quality Engineering control systems Materialise 1990 Rapid prototyping products and software Metris 1995 Reverse engineering & quality control Optidrive 1997 Optimization of drive systems
FMTC: Flanders’ Mechatronics Technology Centre • New research centre, operating since October 2003 • Initiative of Agoria, the Belgian multi-sector federation for the technology industry, and 14 leading mechatronic companies in Flanders • Supported by the Flemisch government via the IWT • Mission: to establish the bridge between the academic and industrial know-how in mechatronics • The centre executes industry driven mid-term and long-term research projects in: • Machine diagnosis • Modular Machines • Super Performance Machines • Close co-operation with PMA in project research and PhD research • Contact: Prof. Hendrik Van Brussel
General Information: Research Areas M P machines & products production machines production processes noise & vibration engineering mechatronics intelligent machines & robotics intelligent manufacturing systems A
General Information: Research Areas • Production processes • Computer integrated manufacturing • Dimensional metrology and quality control • Design of light weight structures • Micro- and precision engineering • Automotive engineering • Space technology • Design of mechatronic systems • Noise and vibration engineering • Robots and intelligent machines
General Information: Research Areas • Types of research: Balance between: • Long-term fundamental research • Short-term or applied research • Linked to industrial needs: • Fundamental research in co-operation with industry • Solving specific problems • Experimental validation of developed techniques
Scores (most) related research Topics • Design of mechatronic systems • Noise and vibration engineering • Robots and intelligent machines { { • No strict separation of research topics • Co-operation between different research groups
Scores (most) related research (cont’d) • Robots and intelligent machines: task planning, active sensing, sensor based environment modeling and task execution • Dynamic balancing of high speed machines • Modeling, identification and analysis of (non-linear) dynamic systems • Vibro-acoustic modeling and prediction • Active control
Robots and intelligent machines • Non-Minimal State Kalman Filter (NMSKF) for nonlinear systems • exact Bayesian estimation: pdf state x, given measurements Zk • and are updated with a KF algorithm • static systems with additive Gaussian measurement uncertainty • limited group of dynamic systems
Robots and intelligent machines • Application of the NSMKF for sensor based geometric model building • example: force controlled manipulation • measurements: contact force (6D) and motion (6D) • task: build geometric model of the environment using measurements • starting from scratch: large initial uncertainties on estimated states • use primitive contacts (e.g. vertex/face contact) nonlinear equations • reduce number of parameters using statistical hypothesis testinge.g.: two vertex/face contacts reduce to one edge/face contact • example: putting a cube in a corner
Robots and intelligent machines 1 vertex/face 2 vertex/face
Robots and intelligent machines • Task specification as a constrained optimization problem • multiple motion tasks x(t) are defined; task jacobian J • over- or underconstrained task specification • task weighting with e.g. system inertia • implementation using torque control or joint velocity control • example: automatic generation of a ‘natural’ underconstrained human motion
Robots and intelligent machines • Task planning for ‘active sensing’: a constrained optimization problem • generate robot trajectory such that sensor information collected between start and goal position yields the most accurate estimates of the parameters of the world model • in force controlled manipulation: ‘hybrid’ optimization • generate sequence of discrete ‘contact formations’ (e.g. edge/vertex) • generate continuous motion within a contact formation
Dynamic balancing of reciprocating machinery • Input torque balancing to reduce drive speed variations: • novel mechanism: cam based ‘centrifugal pendulum (CBCP) • optimized, designed, and implemented • functions correctly, yields significant enhancement of dynamic behavior • Counterweight balancing of linkages: • reduce shaking forces and moments exerted on the supporting frame • reformulated as a convex optimisation problem • computationally efficient, global optimum
Modeling, identification and analysis of dynamic systems • Fundamental • Development of on- and off-line parameter estimation techniques • Modeling and analysis of local non-linear system dynamics, e.g. friction • Applied • Analysis of road noise transmission in vehicles • Non-destructive material identification using mixed numerical-experimental identification techniques, e.g. for laminates • Model based friction compensation • Experimental robot identification
Development of dynamic friction models and friction compensation techniques • Physics based friction model • Stochastic model based on asperity interaction scenario combined with phenomenological mechanisms: creep deformation, adhesion • Able to describe all observed types of friction behavior
Development of dynamic friction models and friction compensation techniques • Generalized Maxwell-slip model for friction compensation
Development of dynamic friction models and friction compensation techniques • Detailed experimental analysis of friction: tribometer • Friction compensation: combination of • Model based feedforward compensation • Disturbance observers
Vibro-acoustic modeling and prediction • Fundamental: Mid-frequency modeling: • Wave based models • Fuzzy finite element models • Hybrid tools for aero-acoustic modeling • Applied • Validation of vibro-acoustic modeling techniques • Models for sound propagation in subsonic confined flows, e.g. automotive mufflers • Modeling of the drive train dynamics of a wind turbine • Validation of the FE & BE methods in vibro acoustic modeling and analysis
Active control • Fundamental • Development of robust controllers for non-linear systems based on approximate model structures • Optimal decoupling for improved MIMO control design • Modeling and control of Linear Parameter Varying systems • Applied • Active control of exhaust noise of combustion engine • Design of lightweight inertial actuators with integrated velocity sensor for active vibration control of a thin panel • Active noise and vibration control for machining systems • Active and semi-active suspension systems for passenger cars • Anti-sway control for the load of a tower crane • Model-based and robust servo-control for high-performance drive systems
Optimal decoupling for improved MIMO control design • MIMO identification and control design: cumbersome • Decentralised control neglecting coupling: limited performance • Combine decentralised control with I/O decoupling: • optimised static decoupling • dynamic transformation filter (inverse based control) • Validation: Time Waveform Replication (TWR):
Optimal decoupling for improved MIMO control design Application: Time Waveform Replication GOAL :Replicate loads actingon vehicle (component)
Control of active and semi-active suspension systems for passenger cars • IWT-project with Tenneco/Monroe • Development of robust controller for active suspension of quarter car • linear model • uncertainty modeling, • H-infinity design
Control of active and semi-active suspension systems for passenger cars
Control of active and semi-active suspension systems for passenger cars
Control of active and semi-active suspension systems for passenger cars Some measured FRF’s and fitted models
Control of active and semi-active suspension systems for passenger cars Estimated uncertainty
Control of active and semi-active suspension systems for passenger cars Results: PDF of body acceleration and tire force, for: passive suspension (blue), constant settings of active suspension (red), and robustly controlled active suspension (green)
Control of active and semi-active suspension systems for passenger cars Robust Control No Control
Anti-sway control of the load of a tower crane • IWT project with ARCOMET NV
Anti-sway control of the load of a tower crane Without control With Control
Research Infrastructure • Team Cube : high-performance 6-DOF shaker table • Multi-channel Measurement, data-acquisition and analysis equipment • Multi-channel DSP-based control systems • Krypton K600 : 6-DOF position/orientation measurement system • Robotics laboratory equipped with 5 industrial robots, 2 mobile learning robots and semi-autonomous wheel chairs • Several high-performance drive-systems based on linear motor technology • Semi-anechoic measurement room • Other experimental test setups, e.g. tribometer, weaving machine a blank, etc ...
Research Infrastructure: Team Cube • 3 actuator pairs • 82 kN/pair • (1+2)/2 = Z / (1-2)/2 = Pitch • (3+4)/2 = X / (3-4)/2 = Yaw • (5+6)/2 = Y / (5-6)/2 = Roll
Research Infrastructure: Team Cube Show film
Conclusion • Research at PMA most related to Systems, Control and Optimization: • Robots and intelligent machines • Noise and vibration engineering • Design of mechatronic systems • Characteristics: • Many applications of existing methodologies • Development of new approach in view of applications • Validation by experimental work • Valorisation in industrial projects