Automatic Fault Detection in Friction Stir Welding

1. Paul Fleming Thomas Bloodworth Tracie Prater David Lammlein George E. Cook Alvin Strauss D. M. Wilkes David Delapp Thomas J. Lienert Matt Bement Automatic Fault Detection in Friction Stir Welding

2. Friction Stir Welding • Recently (1991) developed solid state welding technique • Uses mechanical stirring to join metals • Yields high weld strength • Can be used to join aluminum

3. Defects in FSW • Defects can occur in FSW and undermine the integrity of the weld • Detecting the presence of these defects is important for ensuring quality welds • Defects include • Worm Hole Faults • Gaps • Tracking errors

4. Goal • Develop means of non-destructively detecting fault occurrence, recognizing fault type and correcting: in real time. • Predictive Process Manufacturing

5. Goal

6. Experiments • Conducted initial research into two fault types: • Gap faults in lap welds • Tracking errors in butt welds • Will present gap faults today

7. Gap Fault Experiment • Friction Stir Lap Welding is FSW performed by placing samples one on top of the other and plunging the rotating tool through the first sample in to the second to form the joint • Gaps can exist between the samples when the fit-up is not perfect and possibly lessen the weld strength

8. Gap Fault Experiment • Gaps were milled into samples at depths ranging from: • .0002” - .005”

9. Materials and Equipment • 6061 Aluminum Sample • 01 Steel Tool • Spindle at 2000 RPM • Traverse at 16 IPM

10. Sensing • Kistler Dynamometer

11. Results • Results indicate that this method can be used to detect even small (ten-thousandths of an inch) gaps in FSLW • Machine Learning techniques for finer detection

12. Results – Axial Force

13. Results • A noticeable drop in axial force is shown when gaps are over .002” • For smaller gaps, can attempt to use information in the frequency of the axial force combined with PCA, LDA and SVM to discover gap presence

14. PCA • Principal Component Analysis • Defines new set of axis along directions of maximum variance • Often used as a means of dimensionality reduction • When applied to lap weld data...

15. PCA

16. LDA • Linear Discriminant Analysis • Similar to PCA but axis are selected to maximize between-class scatter and minimize within-class scatter

17. LDA

18. SVM • Support Vector Machine • Attempts to find optimal dividing plane between classes in high dimension • Used in this work as a prediction algorithm where it is trained on one subset of data and asked to predict the classification of the remaining data

19. Accuracy of SVM

20. SVM Regression • Modification where output is function estimate rather than classification

21. SVM Regression

22. Conclusion • Using FSLW as a testbed, we demonstrate the use of Machine Learning in developing fault detection systems which can non-destructively detect fault occurrence as part of a PPM operation.

23. References • George E. Cook, Reginald Crawford, Denis E. Clark, and Alvin M. Strauss. Robotic friction stir welding. Industrial Robot, 31(1):55–63, November 2004. • Terry Khaled. An outsider looks at friction stir welding. Technical report, Federal Aviation Administration, 2005. • Ericsson, M., Jin, L.-Z. & Sandstrom, R. (2007), ‘Fatigue properties of friction stir overlap welds’, International Journal of Fatigue 29, 57–68. • Fukunaga, K. (1972), Introduction to Statistical Pattern Recognition, Academic, New York. • Mishina, O. K. & Norlin, A. (2003), Lap joints produced by fsw on flat alu minum en aw-6082 profiles, in ‘4th International Symposium on Friction Stir Welding’. • Shlens, J. (2005), A tutorial on principal component analysis, Technical report, Systems Neurobiology Laboratory, University of California, San Diego, La Jolla, CA.

24. Acknowledgments • This work supported by • AWS • LANL • NASA