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Optimal planning in robotized cladding processes on generic surfaces

Published online by Cambridge University Press:  21 January 2018

Paolo Magnoni ,
Nicola Pedrocchi ,
Sebastian Thieme ,
Giovanni Legnani Open the ORCID record for Giovanni Legnani [Opens in a new window]  and
Lorenzo Molinari Tosatti
Paolo Magnoni*
Affiliation:
Institute of Industrial Technologies and Automation, National Research Council, via Corti 12, 20133 Milan, Italy. E-mails: [email protected], [email protected] Department of Mechanical and Industrial Engineering, University of Brescia, via Branze 39, 25123 Brescia, Italy. E-mail: [email protected]
Nicola Pedrocchi
Affiliation:
Institute of Industrial Technologies and Automation, National Research Council, via Corti 12, 20133 Milan, Italy. E-mails: [email protected], [email protected]
Sebastian Thieme
Affiliation:
Fraunhofer Institut fur Werkstoff und Strahltechnik, Winterbergstr 28, 01277 Dresden, Germany. E-mail: [email protected]
Giovanni Legnani
Affiliation:
Department of Mechanical and Industrial Engineering, University of Brescia, via Branze 39, 25123 Brescia, Italy. E-mail: [email protected]
Lorenzo Molinari Tosatti
Affiliation:
Institute of Industrial Technologies and Automation, National Research Council, via Corti 12, 20133 Milan, Italy. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

Cladding through laser metal deposition is a promising application of additive manufacturing. On the one hand, industrial robots are increasingly used in cladding because they provide wide wrist reorientation, which enables manufacturing of complex geometries. On the other hand, limitations in robot dynamics may prevent cladding of sharp edges and large objects. To overcome these issues, this paper aims at exploiting the residual degrees of freedom granted by the cladding process for the optimization of the deposition orientation. The proposed method optimizes the robot head orientation along a predefined path while coping with kino-dynamic constraints as well as process constraints. Experimental tests and results are reported and used to validate the approach.

Keywords

Robotic additive manufacturingLaser claddingOrientation planning
Type
Articles
Information
Robotica , Volume 36 , Issue 6 , June 2018 , pp. 787 - 808
Copyright
Copyright © Cambridge University Press 2018 

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References

1. Wohlers, T., “Wohlers report 2014: 3D printing and additive manufacturing state of industry,” Wohlers Associates Inc (2014).Google Scholar
2. Chryssolouris, G., Zannis, S., Tsirbas, K. and Lalas, C., “An experimental investigation of laser cladding,” CIRP Ann. -Manuf. Technol. 51 (1), 145–148 (2002). doi:10.1016/S0007-8506(07)61486-3.Google Scholar
3. Gibson, I., Rosen, D. W. and Stucker, B., Additive Manufacturing Technologies (Springer, New York, 2010).Google Scholar
4. Nowotny, S., Scharek, S., Beyer, E. and Richter, K. H., “Laser beam build-up welding: Precision in repair, surface cladding, and direct 3D metal deposition,” J. Therm. Spray Technol. 16 (3), 344–348 (2007).Google Scholar
5. Gao, J., Chen, X., Yilmaz, O. and Gindy, N., “An integrated adaptive repair solution for complex aerospace components through geometry reconstruction,” Int. J. Adv. Manuf. Technol. 36 (11–12), 1170–1179 (2008).Google Scholar
6. Denkena, B., Boess, V., Nespor, D., Floeter, F. and Rust, F., “Engine blade regeneration: A literature review on common technologies in terms of machining,” Int. J. Adv. Manuf. Technol. 81 (5–8), 917–924 (2015).Google Scholar
7. Zhu, Y. Y., Tian, Y. X. J, Li, J. and Wang, H. M., “The anisotropy of laser melting deposition additive manufacturing Ti-6.5Al-3.5Mo-1.5Zr-0.3Si titanium alloy,” Mater. Des. 67, 538–542 (2015).Google Scholar
8. Hong, C. et al., “Laser additive manufacturing of ultrafine A-structural materials properties,” Microstruct. Process. 635, 118–128 (2015).Google Scholar
9. Nie, B., Yang, L. M., Huang, H., Bai, S., Wan, P. and Liu, J., “Femtosecond laser additive manufacturing of iron and tungsten parts,” Appl. Phys. A–-Mater. Sci. Process. 119 (3), 1075–1080 (2015).Google Scholar
10. Bikas, H., Stavropoulos, P. and Chryssolouris, G., “Additive manufacturing methods and modelling approaches: A critical review,” Int. J. Adv. Manuf. Technol. 83, 389–405 (2016).Google Scholar
11. Lalas, C., Tsirbas, K., Salonitis, K. and Chryssolouris, G., “An analytical model of the laser clad geometry,” Int. J. Adv. Manuf. Tech. 32, 34–41 (2007).Google Scholar
12. Liu, J. and Li, L., “In-time motion adjustment in laser cladding manufacturing process for improving dimensional accuracy and surface finish of the formed part,” Opt. Laser Technol. 36 (6), 477–483 (2004).Google Scholar
13. Calleja, A., Tabernero, I., Fernández, A., Celaya, A., Lamikiz, A. and Lopez de Lacalle, L. N., “Improvement of strategies and parameters for multi-axis laser cladding operations,” Opt. Lasers Eng. 56, 113–120 (2014).Google Scholar
14. Xu, M., Jianfeng, L., Jibin, J. and Li, B., “Influence of powders and process parameters on bonding shear strength and micro hardness in laser cladding remanufacturing,” Proc. CIRP 29, 8049 (2015). doi:10.1016/j.procir.2015.02.088.Google Scholar
15. Arntz, A., Wegener, M. and Liu, Y., “Computer aided manufacturing supported process planning of additive manufacturing by laser deposition welding,” J. Laser Appl. 27, S14002 (2015) doi:https://doi.org/10.2351/1.4823748.Google Scholar
16. Ocelik, V., Eekma, M., Hemmati, I. and Hosson, J. D., “Elimination of start/stop defects in laser cladding,” Surf. Coat. Technol. 206 (8–9), 2403–2409 (2012).Google Scholar
17. Zhang, G. Q., Li, X., Boca, R., Newkirk, J., Zhang, B., Fuhlbrigge, T. A., Feng, H. K. and Hunt, N. J., “Use of Industrial Robots in Additive Manufacturing – A Survey and Feasibility Study,” Proceedings of the 41st International Symposium on Robotics ISR/Robotik 2014, VDE (2014) pp. 512–517.Google Scholar
18. Ruan, J., Eiamsa-ard, K. and Liou, F. W., “Automatic process planning and toolpath generation of a multiaxis hybrid manufacturing system,” J. Manuf. Process. 7 (1), 57–68 (2005).Google Scholar
19. Molina, A., Al-Ashaab, A. H., Ellis, T. I. A., Young, R. I. M. and Bell, R., “A review of computer-aided simultaneous engineering systems,” Res. Eng. Des. 7 (1), 38–63 (1995).Google Scholar
20. Sheng, W., Heping Chen, N. X., Chen, Y. and Song, M., “Surface Partitioning in Automated CAD-Guided Tool Planning for Additive manufacturing,” Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS2003 (Oct. 2, 2003) pp. 2072–2077.Google Scholar
21. Siemens. PLM NX Hybrid Additive Manufacturing Software (2016). [Online]. Available: www.plm.automation.siemens.com/en_us/products/nx/for-manufacturing/cam/. [Last accessed 14 Jan. 2018].Google Scholar
22. SKM-Informatik. DCAM (2016). [Online]. Available: www.skm-informatik.de/skm/cam/skm-dcam-at-a-glance.html. [Last accessed 14 Jan. 2018].Google Scholar
23. Lasemi, A., Xue, D. and Gu, P., “Recent development in CNC machining of freeform surfaces: A state-of-the-art review,” Comput.-Aided Des. 42 (7), 641–654 (2010).Google Scholar
24. Sellmann, F., Haas, T., Nguyen, H., Weikert, S. and Wegener, K., “Orientation smoothing for 5-axis machining using quasi-redundant degrees of freedom,” Int. J. Autom. Technol. 10 (2), 263 (2016).Google Scholar
25. Nowotny, S., Thieme, S., Albert, D., Kubisch, F., Kager, R. and Leyens, C., “Generative Manufacturing and Repair of Metal Parts through Direct Laser Deposition Using Wire Material,” In: Digital Product and Process Development Systems. IFIP Advances in Information and Communication Technology (Kovács, G. L. and Kochan, D., eds.) vol. 411, (Springer, Berlin, Heidelberg, 2013).Google Scholar
26. Donald, B., Xavier, P., Canny, J. and Reif, J., “Kinodynamic motion planning,” J. ACM 40 (5), 1048–1066 (1993).Google Scholar
27. Latombe, J.-C., Robot Motion Planning (Kluwer Academic Publishers, Boston, 1991).Google Scholar
28. Kavraki, L., Svestka, P., Latombe, J. -C. and Overmars, M., “Probabilistic roadmaps for path planning in high-dimensional configuration spaces,” IEEE Trans. Robot. Autom. 12 (4), 566–580 (1996).Google Scholar
29. LaValle, S. and Kuffner, J., “Randomized kinodynamic planning” Int. J. Robot. Res. 20 (5), 378–400 (2001).Google Scholar
30. Karaman, S. and Frazzoli, E., “Sampling-based algorithms for optimal motion planning,” Int. J. Robot. Res. 30 (7), 846–894 (2011).Google Scholar
31. Webb, D. and Van den Berg, J., “Kinodynamic RRT*: Optimal Motion Planning for Systems with Linear Differential Constraints,” Proceedings of the IEEE International Conference on Robotics and Automation ICRA, Karlsruhe, Germany (May 6–10, 2013)Google Scholar
32. AC&E. CimStation (2016). [Online]. Available: http://www.acel.co.uk/cimstation-robotics/. [Last accessed 14 Jan. 2018].Google Scholar
33. Siemens. RobCad (2016). [Online]. Available: https://www.plm.automation.siemens.com/. [Last accessed 14 Jan. 2018].Google Scholar
34. ABB Robotics. RobotStudio (2016). [Online]. Available: http://new.abb.com/products/robotics/robotstudio. [Last accessed 14 Jan. 2018].Google Scholar
35. Visual Components. 3DAutomate (2016). [Online]. Available: http://www.visualcomponents.com/products/3dautomate. [Last accessed 14 Jan. 2018].Google Scholar
36. Dassault Systems. Delmia (2016). [Online]. Available: http://www.3ds.com/products-services/delmia. [Last accessed 14 Jan. 2018].Google Scholar
37. Chalmers. Industrial Path Planner (2016). [Online]. Available: http://www.fcc.chalmers.se/software/ips. [Last accessed 14 Jan. 2018].Google Scholar
38. GAMMA Group, University of North Carolina, Chapel Hill. FCL: A Flexible Collision Library (2016). [Online]. Available: http://gamma.cs.unc.edu/FCL. [Last accessed 14 Jan. 2018].Google Scholar
39. Sucan, I., URDF ROS Package (2016). [Online]. Available: http://wiki.ros.org/urdf. [Last accessed 14 Jan. 2018].Google Scholar
40. Halton, J. H., “On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals,” Numer. Math. (1), 84–90 (1960).Google Scholar
41. Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Robotics: Modelling, Planning and Control (Springer Science & Business Media, Springer-Verlag London, 2010).Google Scholar
42. Byrd, R. H., Hribar, M. E. and Nocedal, J., “An interior point algorithm for large scale nonlinear programming,” SIAM J. Opt. 9 (4), 877–900 (2000).Google Scholar
43. Um, J., Rauch, M., Hascot, J.Y. et al., “STEP-NC compliant process planning of additive manufacturing: remanufacturing,” Int. J. Adv. Manuf. Technol. 88, 1215–1230 (2017).Google Scholar
44. Zhao, M., Gao, X. and Zhang, Q., “An efficient stochastic approach for robust time-optimal trajectory planning of robotic manipulators under limited actuation,” Robotica 35 (12), 2400–2417 (2017).Google Scholar