Base of The Fourth Industrial Revolution. Adaptive Production Technology

Transcription

1 SV.1 Base of The Fourth Industrial Revolution Modularization and the Things Adaptive Production Technology Example Applications

2 SV.2 The Fourth Industrial Revolution Data relationships needs Models The Internet of Things Big Data, Smart IPV Addresses IPV Addresses Lot Size One Individual Production Tolerance and Unique The Measurement of the World is not new Humboldt and Gauss What s it?

3 SV.3 Multidimensional Input Output Signals create Big Data 1000 cores and more Software Model Kit W-LAN Hardware Intel Button PC (20nm Chip Design) The Internet of Things World Wide Web

4 SV.4 Deterministic and constant multidimensional SMax signals or ps () models do not contain Information. NWe p must identify these d s [bit] only once. log 2 ps ( ) S Min S signal value Only random multidimensional signals Entropy or models per Seconds contain finformation. Signals must measured online and models must S be identified online. Max t NN log2 1 [bit/s] SNoise time Average values and noise statistical spreads or the covariance Channel Capacity matrixes are only first order approximations. These lead in practice to relatively Information big errors between S Max C 2model fmax log and 2 1 reality. [bit] SNoise frequency Signal Entropy Information, Signal and Model

5 SV.5 The multi-dimensional Fourier transformation We need Big Data, Advanced Mathematics of probability density function is equal to the and at the End multi-dimensional empirical statistical central moments. These can be measured! Software for this.

6 SV.6 The humans must design the Software. The computer is not creative and not intelligent in the sense of humans. The software is not intelligent. The software designer is it. We must design a deterministic and stochastic static and dynamical multi dimensional process and product model in the software. The Rule of the Humans

7 SV.7 Modularization and the Things

8 SV.8 Drives, Actuators and Robots Rotational Drive Parallel Kinematics Serial Kinematics FANUC Hybrid- Kinematics Linear Drive FANUC KUKA Controls and Feedback Controls Beckhoff Siemens KUKA FANUC ABB ABB External Sensors 6D Pose and 6D Force Indoor-GPS Laser Measurement Technique Video Metric Measurement Aikon Leica Nikon Modularization and Things I

9 3D/6D Marker external or/and product integrated Sphere SV.9 (wavelength selective) Edge Elements LED- Points Gripper Tool Change Spindle Triple Mirrors Retro Reflective Marker Milling Spindle 6D-Calibration 6D-Gripper and Tool Calibration Auxiliaries Tool Change Systems r R Gripper Calibration Bodies and -auxiliary Modularization and Things II

10 SV.10 Adaptive Production Technology

11 SV.11 Accuracy Requirements (0,1mm Range from 10 to 20m 5µm/m) Position Error Components (5 10 mm) Position Error Process to TCP (0,5 1 mm) Position Error Robot (1 5 mm) Position Error Linear Axis (5 20 mm at the Process) Process Force Position Influence (0,5 1 mm) Adaptive Production Technology

12 SV.12 Example Applications

13 SV.13 Adaptive Production Technologies

14 SV.14 Automated measurement, placement, calibration of process, part and machine models S M S R S TCP Tool-Grabber-Sensor Roboter S G Walking-Rivet-Robot Laser- Tracker steifer Bauteilbeschlag Master- Roboter 3D-Videometrie 3D-Messmarke Montagebauteil kooperierende Multi-Robotersysteme elastischer Bauteilbeschlag Slave- Roboter Adjustment, machining and assembly of CFRP large component The Fourth after technologically Industrial weighted best fit criteria Revolution Tolerance management by high precision calibration, adaptive path planning as well as actuator controlled shape and position correction Optimized hierarchical fully automated calibration of control models We of are serial and already parallel machines as well as robot systems started!!!! Sensor guided adaptive CFRP production technologies CA := Computer aided, S := sensoring, D := Design, M := manufacturing CAS CAD CAM Data Link I

15 f q 2 f, q 5 to 10 S Control High Level Language Leading Level Cell Level B C++ or C# minutes to days/months seconds to minutes SV.15 Shannon Sampling Theorem (high dynamic result in high sampling rates; no lost information) The Internet of Things Field Level Actuator Sensor Level Siemens ms to s µs to ms Hard Real Time f := frequency B := band width S := sampling CAS CAD CAM Data Link II

16 SV.16 Adaption and 0.5 to 1.5 Bill. costs reduction potentials (Robot production cells on linear axes versus construction of special purpose machines require flexible problem adapted optimal linear axis and robot calibrations or/and sensor guided robots; Otherwise special machine necessary) Teach costs per Product 1,000 to 30,000 (dependent on the number of teach poses; various processes till now not possible) Teach costs per Robot 10,000 to 300,000 (10 Products per Robot) Robot change in production line 100,000 to 400,000 (Reapplication of teaching Production holdup) Amortization Potential

17 Inverse kin. Transform SV.17 Velocity Time Sequence Motion Model and Acceleration These Principles geomtric Parameter are Definitions General { t } Rough Interpolation Applicable k < 10ms Interpreter p TCP A() t v TCP a TCP p TCP AS( t ) A A Servo Control frame Modeling x( t m ) Look-Up x( t m ) Table x x( t m ) S ( t m ) x S ( t m ) Comp. Axis Elasticity parameters correction, - x Residual S ( t m ) x G ( t k ) Moments Model + Inverse Models / Controller Loops { } Fast iterative-inverse Analytical identification of 6 D under consideration of transform Hierarchical measuring iterative to model TCP 6D parallelism calbration position invariant and orientated orthogonality modeling at the numeric errors of (Identification the of elasticity rank the axes defects 6 D-measuring behavior as well and the as (Realize sensitivity characteristics, drive a real of Identification the elasticity. time control compensation 6 model D tool (500 of process high-speed force compared induced with 6D position classic Identification iterative variant of approaches positioning cooperating errors.) multi- Makes (Realize fully automatic high speed robot 10 ms possible for precision precision systems, calibration) Precision model calibration calibration applications etc.) and realize real time abilities!!!!) p RI Identified Machine/Robot Parameter Machine Coordinates Fine Interpolation m p t RI p EI Time Sequence Information Gain by Measuring < 1ms x F ( t m ) p real time requirements RI Power into the field level Amplifier Sensor (x,x,x) Target Pose Structure and Parameter Identification Integration of the procedures Position Regulator x TCP A() t to the cell level Axis + - Sensor (x) or under Friction Compensation x( t m ) Automation and System Integration

18 F-Aromon ProsihP FFM SV.18 Adaptive component and section assembly CFK AFMO FFM Adaptive milling processing Adaptive component shape and position correction Examples

19 SV.19 Flexibly adjustable contour (for several aircraft types usable) Economical vacuum grab systems 6 D force and moment controlled process Wrinkling could reduced Temperature and range of the tide compensation Adaption in the shape and positional correction Economical foundations Shape and position precision converges towards the machines repeatability to 2-4 iterations in the area of 50 to 100 µm (for the hole fuselage) Component Shape and Positional Correction

20 SV.20 Movement Measure Iterative Model Calibration Hand Eye Calibration

21 SV.21 CAS CAD CAM Link

22 TCP SV.22 Werkzeugmaschine T-Scan Leica- Laser- Tracker EEW T-Probe Zeitfolge { t i } Zeitfolge { t k } T-Mac Grob-Interpolation Bewegungsvariante G pa ( t i ) Analytische Inverse x( t i ) Geschwindigkeitsund Beschleunigungsvorgaben Fein-Interpolation Steuerrechner Fräs- Tool Realtime Gigabit Ethernet Network-Card Joint- Controll Transformation I/O- DA Wasser- schneid- Tool Reduction of unproductive measuring and auxiliary progress times by 80% Standardized interfaces Modular hardware and software architecture Adaptive flexible production and assembly cells Software xf( t k ) x I ( t ) Lageregler Maschinenkoordinaten Joint-Regulator Sensor (x,x,x) Power- Amplifier Maschinensteuerung Adaptive path control 0.5 to 1 Bill. per year External Sensor 6D Pose Measurement On the Fly

23 SV.23 Sensors Machine Application C, C++, C# Sensor Sensor-Int. OO Code Classes API Vision Robots Motion Planning Programmers Interface Motion & I/O - Interface Motion / 100 µm I / O I/O Modeling Vision Calibrated Model RMS x / m y / m / 1 mm Non calibrated Model Blume Kernel Mode Real Time Filesystem Device Driver Network Drivers Offline Programming and Simulation I/O Manager Graphics Drivers Hardware Interface Layer Hardware User Mode (protected) Operating System Graphics Subsystem Other Structured model based instead of unstructured (look up table) volume metric 6 D position error compensation Application for assembly and production facilities, large volume machine tool as well as industrial robots Significant reduction of the measuring time by 95% Automatic causal detection of wearout, defects etc.. Accuracy gain (factor 5 to 10) Recorded Quality 0.5 to 1.0 Bill. per year x / m y / m Model Based Precision Increase