Modelling of Human Welder for Intelligent Welding and Welder Training*

Transcription

1 Modelling of Human Welder for Intelligent Welding and Welder Training* YuMing Zhang University of Kentucky Lee Kvidahl Ingalls Shipbuilding NSRP Welding Panel Meeting Bethesda, Maryland May -, 1 For Unlimited Distribution. Research funded by the NSF under grant NRI-Small: Virtualized Welding: A New Paradigm for Intelligent Welding Robots in Unstructured Environment, IIS-18, Sept. 1-August 31, 1 and grant Machine-Human Cooperative Control of Welding Process CMMI-9777, October 9-Spet. 13

2 Contents 1. Sensing of 3D Arc Weld Pool Surface: motivation, method, real time. Characterization: from numerous points to three characteristic parameters 3. Control of 3D Weld Pool Surface: control theory method. Human Welder Response: modeling and analysis, control using welder model. Welder Motion Response: human-robot system, speed adjustment, 3D adjustment. Future Directions

3 1. Sensing of 3D Arc Weld Pool Surface (Human Response Input)

4

5 Weld pool: where complex phenomena originate; but only the surface is visible; the major feedback information available to human welders Measurement of weld pool surface temperature distribution: needs the emissivity to determine the temperature the infrared radiation but the emissivity is slope dependent Weld Penetration:

6 (1) Surface Specular: use laser reflection; () Arc Radiation: use laser reflection and intercept at a distance low power continuous laser for continuous measurement, no need for a special camera Laser: mw, 8 nm Y.M. Zhang, H.S. Song, and G. Saeed. Observation of a dynamic specular weld pool surface. Measurement Science & Technology, 17(),.

7 Reflection law, surface constraint, error evaluation 18 1 Reflected dots from image processing and reconstructed surface Image processed dots Dots reflected by reconstructed surface 1 1 Y/mm X/mm Hongsheng Song. Machine Vision Recognition of Three-Dimensional Specular Surface for Gas Tungsten Arc Weld Pool. ECE Department, University of Kentucky, 7. XiaoJi Ma. Measurement of Dynamic Weld Pool Surface in Gas Metal Arc Welding Process. Department of Electrical and Computer Engineering, University of Kentucky, Feb. 1.

8 Analytical Solution Key for Real Time Measurement and Control W.J. Zhang, X.W. Wang, Y.M. Zhang, 13. Analytical Real-time Measurement of Three-dimensional Weld Pool Surface, Measurement Science and Technology, (11), article Number 1111 (18pp), doi:1.188/97-33//11/1111

9 . Characterization of 3D Weld Pool Surface

10 Characteristic parameters should be used rather than a large set of 3D coordinates. Should keep the fundamental information in the weld pool surface about the weld joint penetration. W.J. Zhang, Y.K. Liu, X. W. Wang, Y.M. Zhang. Characterization of three-dimensional weld pool surface in gas tungsten arc welding. Welding Journal, vol. 91, 1.

11 Left: Measured 3D weld pool surface parameters from 3 experiments; Right: Least squares model fitting with 3-parameter model using the width, length, and convexity. w 1.79W.7L 1.87C.988 b

12 3. Control of 3D Weld Pool Surface Modeling: how the characteristic parameters respond to the change in current and travel speed extract the model from experimental data Control: Model predictive control algorithm Yukang Liu, YuMing Zhang. Control of 3D Weld Pool Surface. Control Engineering Practice, 1(11), 13.

13 Welding Experiments Speed Disturbance W b (mm) Distance (mm) Width (mm) Length (mm) 1*Convexity (mm) Input Parameters 3 Current/1 (A) Voltage/3 (V) Speed (mm/s) Weld Pool Parameters Time (s) Time (s)

14 . Modeling and Analysis of Human Welder Response to 3D Weld Pool Surface (mechanized welding, human adjusts the current)

15 Skilled human welder holds the current regulator while observing the geometry of weld pool; Adjusts the welding current to control the process to full penetration. Manual control system of GTAW process Welding Parameters Current/A Welding speed/mm/s Arc length/mm Argon flow rate/l/min 7~81 Experiment 1~ Parameters Monitoring Parameters Project Laser to weld pool angle/ distance/mm Imaging plane to weld pool distance/mm Camera Parameters Shutter speed /ms Frame rate/ fps Camera to imaging plane distance/mm Y.K. Liu, Y.M. Zhang, L. Kvidahl. Skilled Human Welder Intelligence Modeling and Control. Welding Journal, 93, 1.

16 In general, the human intelligent model can be written as: I ( k)= f ( W ( k 3), L ( k 3), C ( k 3), I ( k 1)) f f f Following linear model can be identified using standard least squares method: I ( k)=.1 W ( k 3).8 L ( k 3)+1.81 C ( k 3)+. I( k 1) f f f Measured dcurrent Linear Estimated dcurrent dcurrent Sample Number Linear modeling result.

17 dcurrent - Measured dcurrent Linear ANFIS Estimated dcurrent dcurrent Measured dcurrent Linear ANFIS Estimated dcurrent dcurrent Measured dcurrent Linear ANFIS Estimated dcurrent 1 1 Sample Number 8 Sample Number Sample Number Model comparison between linear and ANFIS model. Model Comparison between Neuro-Fuzzy Model and linear model Average Model Maximum Model RMSE /A Error /A Error /A Linear Model ANFIS Model Y.K. Liu, W.J. Zhang, Y.M. Zhang. Dynamic neuro-fuzzy-based human intelligence modeling and control in GTAW. IEEE Transactions on Automation Science and Engineering, 1, 1.

18 Model Comparison Linear Models Novice welder Skilled welder I ( k)=.9 W( k 3).9 L( k 3)+1.73 C( k 3)+.7 I ( k 1) I ( k)=.1 W ( k 3).8 L ( k 3)+1.81 C ( k 3)+. I( k 1) f f f

19 Nonlinear Model Comparison Novice Welder Skilled Welder 3 -. I (A) - 7 L (mm) 3 3 W (mm) Novice Welder I (A) - 7 L (mm) 3 3 W (mm) Novice Welder (a) (b) I (A) - 7 L (mm) 3 3 W (mm) Skilled Welder I (A) - 7 L (mm) 3 3 W (mm) Skilled Welder In normal cases the skilled welder's adjustments are minimal which can prevent large oscillation and overshoot novice welder model suffers; In other cases where the convexity is either considerably small or large, the adjustment made by the skilled welder is larger than that of the novice welder, which can provide shorter settling time than novice welder does. The skilled welder model does provide better adjustment than the novice welder. I (A) - 7 L (mm) 3 3 W (mm)... (c) I (A) - 7 L (mm) 3 3 W (mm) Nonlinear model surface of the neuro-fuzzy human welder model (left: novice welder, right: skilled welder) for convexity = (a).1 mm (b).18mm (c).mm. Previous response is zero for all cases.

20 Control Experiments: Varying Initial Current A B A B A B A B 8 7 Current/1 (A) Voltage/3 (V) Speed (mm/s) 8 7 Current/1 (A) Voltage/3 (V) Speed (mm/s) Input Parameters 3 A B Input Parameters 3 A B Time (s) (a) Time (s) (b) Control experiment result with different initial current (a) A; (b) A.

21 . Welder Motion Response to Weld Pool Surface (welding speed adjustment, 3D operation adjustment) 1. Equipment for experimental data: human-robot system. Extract good response from not-perfect performance of human welder 3. Quality evaluation model. Supervised learning using good data Y.K. Liu, Y.M. Zhang, L. Kvidahl, 1. Skilled Human Welder Intelligence Modeling and Control: Part I-Modeling, Welding Journal, 93: s-s. Y.K. Liu, Y.M. Zhang, L. Kvidahl, 1. Skilled Human Welder Intelligence Modeling and Control: Part II-Analysis and Control Applications, Welding Journal, 93(): 1s-17s.

22 Equipment - Virtualized Welding System Virtual Station and Welding Station Illustration of virtualized welding operation. In virtual station a human welder can view the mock up and moves the virtual welding torch accordingly as if he/she is right in front of the work-piece; In welding station a robot arm (Universal Robot UR- with six Degree of Freedom) equipped with the welding torch receives commands via Ethernet and performs GTAW. Developed virtualized welding system. Virtual welding torch.

23 Equipment - Virtualized Welding System 3D scanning system Visualization system Motion sensor Camera Projector Mockup Screen Detailed view of the virtual station. 3D weld pool sensing system. A low power laser (19 by 19 structure light pattern) is projected to the weld pool surface; Its reflection from the specular weld pool surface is intercepted and imaged by Camera 1; Weld pool image captured by Camera (with or without virtual reality (VR) enhancement) Major components in virtual station include a Leap motion tracking sensor, a mock up pipe, a computer screen, and a projector. Detailed view of the visualization result.

24 Learning Experiments for Response Data Input and Output Speed*3 (mm/s) Current/ (A) Speedf*3 (mm/s) Width (mm) Length (mm) Convexity*1 (mm) Sample Number Measured welding current, weld pool characteristic parameters and human arm movement speed in thirteen training experiments. Teleoperation training experiments The welder observe the weld pool under random welding current, and move the virtual welding torch accordingly.

25 Rating of Performance and Response Data Welder Rating System To better distill the correct response of the human welder, the human welder evaluates the measured data and corresponding back-side weld penetration and assigns a rating (from to 1) in each s interval (offline, thus less skill demanding). Rating (-1) Sample Number Rating assigned by human welder in thirteen dynamic experiments. Y.K. Liu, Y.M. Zhang, Iterative local ANFIS based human welder intelligence modeling and control in pipe GTAW process: A data-driven approach, IEEE/ASME Transactions on Mechatronics, DOI: 1.119/TMECH.1.33.

26 ANFIS Based Automated Rating of Data Quality Welder Rating System R( k) f W ( k), L( k), C( k), S( k) Both linear and ANFIS models are proposed to automate the rating 1 Human Welder Rating 1 8 Human Welder Rating Linear Estimated Rating ANFIS Estimated Rating Sample Number Linear and ANFIS modeling of the rating (Welder Rating System)

27 Supervised Modeling Good Response data (with ratings greater than 8) are used to model how human welder adjusts the speed per weld pool surface. 1. Measured Speedf Linear Estimated Speedf ANFIS Estimated Speedf Speedf (mm/s) Sample Number Linear and supervised ANFIS modeling of the welder response.

28 Welding Current and Pool Parameters Welding Current and Pool Parameters D Operation Learning and Robot Implementation of Human Response Experiment 1: Different Welding Current Current/1 (A) Width (mm) Length (mm) Convexity*1 (mm) Time (s) Experiment : Speed Disturbance 1 8 Current/1 (A) Width (mm) Length (mm) Convexity*1 (mm) Time (s) Control Inputs Control Inputs 1. Speed (mm/s) Z Adjustment (mm) RX Adjustment/1 (deg) RY Adjustment/1 (deg) Time (s) 1. Speed (mm/s) Z Adjustment (mm) RX Adjustment/1 (deg) RY Adjustment/1 (deg) Time (s)

29 . Future Directions o o o Varying Gap feedforward + feedback Operation inconsistence: automatic rating of operation quality Better Free Demonstration of Human Skills IMU sensor on torch, co-view helmet o What is an Co-View Helmet? o o o o o o Welder Operation Documentation: Heat Input/Welding Speed/Torch Orientation/O clock Position, Weld Pool Size and Shape Welder Operation Modeling and Diagnosis Improvement from Comparison with Skilled Welder Model Program Welding Robot for Intelligent Control Automatic Welding Parameters Adjustment Based on Speed and Torch Orientation/Angle

30 Key: Mobile Sensor - Projective Torch : grid pattern and dot matrix pattern attached, as well as one IMU - Shield glass (simulated by a piece of glass covered by a paper) - Sensory Helmet (camera tripod to simulate the head movement in DOF) - Weld pool (Convex spherical mirror with known geometry) W.J. Zhang, J. Xiao, Y.M. Zhang, 1. A mobile sensing system for three-dimensional weld pool measurement in manual GTAW process," Measurement Science and Technology, 7 (1) 1 (pp), doi:1.188/97-33/7//1.

31 Key Component - Inertial Measurement Unit (IMU) Simmer Wireless Inertia Measurement Unit includes: 1. tri-axial accelerometer (Freescale MMA7Q). tri-axial gyro sensor (InvenSense series) 3. a magnetometer. a microprocessor (MSP3F111). a Bluetooth unit. Experimental system configuration W.J. Zhang, J. Xiao, Y.M. Zhang, 1. Navigation of welding torch for arc welding process, Preprints of the 19th World Congress of The International Federation of Automatic Control, pp , Cape Town, South Africa, August - 9, 1.

32 Tracking Accuracy Verification Position experiment The torch is smoothly moved along the 3-D since curve. The results of torch trajectory position estimation Measurement errors in Position Experiment W.J. Zhang, J. Xiao, Y.M. Zhang, 1. Navigation of welding torch for arc welding process, Preprints of the 19th World Congress of The International Federation of Automatic Control, pp , Cape Town, South Africa, August -9, 1.