Papers
Topics
Authors
Recent
Search
2000 character limit reached

Coverage Path Planning for Thermal Interface Materials

Published 22 May 2024 in cs.SY, cs.LG, and eess.SY | (2405.13512v1)

Abstract: Thermal management of power electronics and Electronic Control Units is crucial in times of increasing power densities and limited assembly space. Electric and autonomous vehicles are a prominent application field. Thermal Interface Materials are used to transfer heat from a semiconductor to a heatsink. They are applied along a dispense path onto the semiconductor and spread over its entire surface once the heatsink is joined. To plan this application path, design engineers typically perform an iterative trial-and-error procedure of elaborate simulations and manual experiments. We propose a fully automated optimization approach, which clearly outperforms the current manual path planning and respects all relevant manufacturing constraints. An optimum dispense path increases the reliability of the thermal interface and makes the manufacturing more sustainable by reducing material waste. We show results on multiple real products from automotive series production, including an experimental validation on actual series manufacturing equipment.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (47)
  1. A. Gowda, D. Esler, S. Tonapi, K. Nagarkar, and K. Srihari, “Voids in Thermal Interface Material layers and their effect on thermal performance,” in Proceedings of 6th Electronics Packaging Technology Conference (EPTC 2004), Singapore, Dec. 2004, pp. 41–46.
  2. S. Baeuerle, M. Gebhardt, J. Barth, R. Mikut, and A. Steimer, “Rapid flow behavior modeling of Thermal Interface Materials using deep neural networks,” IEEE Access, vol. 12, pp. 17 782–17 792, 2024.
  3. M. Kaufmann, F. Flaig, M. Müller, H. Fricke, and T. Vallée, “How adhesives flow during joining,” International Journal of Adhesion and Adhesives, vol. 122, p. 103315, Feb. 2023.
  4. P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and Cybernetics, vol. 4, no. 2, pp. 100–107, 1968.
  5. E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik, vol. 1, no. 1, pp. 269–271, 1959.
  6. E. Galceran and M. Carreras, “A survey on Coverage Path Planning for robotics,” Robotics and Autonomous Systems, vol. 61, no. 12, pp. 1258–1276, Dec. 2013. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S092188901300167X
  7. H. Choset, “Coverage for robotics – a survey of recent results,” Annals of Mathematics and Artificial Intelligence, vol. 31, no. 1-4, pp. 113–126, 2001. [Online]. Available: https://link.springer.com/article/10.1023/A:1016639210559
  8. C. S. Tan, R. Mohd-Mokhtar, and M. R. Arshad, “A comprehensive review of Coverage Path Planning in robotics using classical and heuristic algorithms,” IEEE Access, vol. 9, pp. 119 310–119 342, 2021.
  9. H. Choset and P. Pignon, “Coverage path planning: The boustrophedon cellular decomposition,” in Field and Service Robotics.   London: Springer London, 1998, pp. 203–209.
  10. A. Zelinsky, R. A. Jarvis, J. C. Byrne, and S. Yuta, “Planning paths of complete coverage of an unstructured environment by a mobile robot,” in Proceedings of International Conference on Advanced Robotics, vol. 13, Tokyo, Japan, 1993, pp. 533–538. [Online]. Available: http://pinkwink.kr/attachment/[email protected]
  11. Y. Gabriely and E. Rimon, “Spanning-tree based coverage of continuous areas by a mobile robot,” Annals of Mathematics and Artificial Intelligence, vol. 31, no. 1, pp. 77–98, 2001.
  12. ——, “Spiral-STC: An on-line coverage algorithm of grid environments by a mobile robot,” in Proceedings of the 2002 IEEE International Conference on Robotics and Automation (ICRA), vol. 1, Washington, DC, USA, 2002, pp. 954–960 vol.1.
  13. C. Luo, S. X. Yang, D. A. Stacey, and J. C. Jofriet, “A solution to vicinity problem of obstacles in complete Coverage Path Planning,” in Proceedings 2002 IEEE International Conference on Robotics and Automation (ICRA), vol. 1, Washington, DC, USA, 2002, pp. 612–617. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/1013426/
  14. S. Yang and C. Luo, “A neural network approach to complete Coverage Path Planning,” IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol. 34, no. 1, pp. 718–724, Feb. 2004. [Online]. Available: http://ieeexplore.ieee.org/document/1262545/
  15. C. Luo and S. X. Yang, “A bioinspired neural network for real-time concurrent map building and complete coverage robot navigation in unknown environments,” IEEE Transactions on Neural Networks, vol. 19, no. 7, pp. 1279–1298, Jul. 2008. [Online]. Available: http://ieeexplore.ieee.org/document/4539807/
  16. D. E. Soltero, M. Schwager, and D. Rus, “Generating informative paths for persistent sensing in unknown environments,” in Proceedings of the 2012 IEEE International Conference on Intelligent Robots and Systems, Vilamoura, Algarve, Portugal, 2012, pp. 2172–2179. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6385730/
  17. ——, “Decentralized path planning for coverage tasks using gradient descent adaptive control,” The International Journal of Robotics Research, vol. 33, no. 3, pp. 401–425, Mar. 2014. [Online]. Available: http://journals.sagepub.com/doi/10.1177/0278364913497241
  18. J. C. Kiemel, P. Yang, P. Meißner, and T. Kröger, “PaintRL: Coverage path planning for industrial spray painting with Reinforcement Learning,” in Workshop on Closing the Reality Gap in Sim2Real Transfer for Robotic Manipulation, Freiburg, Germany, Jun. 2019.
  19. C. Piciarelli and G. L. Foresti, “Drone patrolling with Reinforcement Learning,” in Proceedings of the 13th International Conference on Distributed Smart Cameras, ser. ICDSC 2019.   Trento, Italy: Association for Computing Machinery, 2019. [Online]. Available: https://doi.org/10.1145/3349801.3349805
  20. M. Theile, H. Bayerlein, R. Nai, D. Gesbert, and M. Caccamo, “UAV Coverage Path Planning under varying power constraints using deep Reinforcement Learning,” arXiv:2003.02609, Tech. Rep., 2020. [Online]. Available: https://arxiv.org/abs/2003.02609
  21. K. Ellefsen, H. Lepikson, and J. Albiez, “Multiobjective Coverage Path Planning: Enabling automated inspection of complex, real-world structures,” Applied Soft Computing, vol. 61, pp. 264–282, Dec. 2017.
  22. V. R. Batista and F. A. Zampirolli, “Optimising robotic pool-cleaning with a Genetic Algorithm,” Journal of Intelligent & Robotic Systems, vol. 95, no. 2, pp. 443–458, Aug. 2019.
  23. M. A. Yakoubi and M. T. Laskri, “The path planning of cleaner robot for coverage region using Genetic Algorithms,” Journal of Innovation in Digital Ecosystems, vol. 3, no. 1, pp. 37–43, Jun. 2016. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S2352664516300050
  24. M. Popović, G. Hitz, J. Nieto, I. Sa, R. Siegwart, and E. Galceran, “Online informative path planning for active classification using UAVs,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), Marina Bay Sands, Singapore, Jun. 2017.
  25. N. Hansen, “The CMA Evolution Strategy: a tutorial,” arXiv:1604.00772, Tech. Rep., 2016. [Online]. Available: http://arxiv.org/abs/1604.00772
  26. D. Strubel, “Coverage path planning based on waypoint optimization, with evolutionary algorithms,” Ph.D. dissertation, Université Bourgogne Franche-Comté, Petronas, France, 2019.
  27. P. Kulkarni, A. Marsan, and D. Dutta, “A review of process planning techniques in layered manufacturing,” Rapid Prototyping Journal, vol. 6, no. 1, pp. 18–35, Jan. 2000. [Online]. Available: https://doi.org/10.1108/13552540010309859
  28. W. Oropallo and L. A. Piegl, “Ten challenges in 3D printing,” Engineering with Computers, vol. 32, no. 1, pp. 135–148, Jan. 2016. [Online]. Available: http://link.springer.com/10.1007/s00366-015-0407-0
  29. G. Jin, W. Li, C. Tsai, and L. Wang, “Adaptive tool-path generation of rapid prototyping for complex product models,” Journal of Manufacturing Systems, vol. 30, no. 3, pp. 154–164, Aug. 2011. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0278612511000562
  30. L. Roveda, B. Maggioni, E. Marescotti, A. Shahid, A. M. Zanchettin, A. Bemporad, and D. Piga, “Pairwise preferences-based optimization of a path-based velocity planner in robotic sealing tasks,” IEEE Robotics and Automation Letters, pp. 1–1, 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9473003/
  31. R. Comminal, M. P. Serdeczny, D. B. Pedersen, and J. Spangenberg, “Motion planning and numerical simulation of material deposition at corners in extrusion Additive Manufacturing,” Additive Manufacturing, vol. 29, p. 100753, Oct. 2019.
  32. H. Blum, “A transformation for extracting new descriptors of shape,” in Proceedings of the Symposium on Models for the Perception of Speech and Visual Form, vol. 43.   Cambridge, MA, USA: MIT press, 1967, pp. 362–380.
  33. J.-H. Kao and F. B. Prinz, “Optimal motion planning for deposition in layered manufacturing,” in Proceedings of Design Engineering Technical Conferences 1998, vol. 80364.   Atlanta, Georgia, USA: American Society of Mechanical Engineers, 1998, pp. 13–16.
  34. D. Ding, Z. Pan, D. Cuiuri, H. Li, and N. Larkin, “Adaptive path planning for wire-feed Additive Manufacturing using medial axis transformation,” Journal of Cleaner Production, vol. 133, pp. 942–952, Oct. 2016. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0959652616307119
  35. J. Wang, T.-w. Chen, Y.-a. Jin, and Y. He, “Variable bead width of material extrusion-based Additive Manufacturing,” Journal of Zhejiang University-Science A, vol. 20, no. 1, pp. 73–82, Jan. 2019. [Online]. Available: http://link.springer.com/10.1631/jzus.A1700236
  36. Y. Xiong, S.-I. Park, S. Padmanathan, A. G. Dharmawan, S. Foong, D. W. Rosen, and G. S. Soh, “Process planning for adaptive contour parallel toolpath in Additive Manufacturing with variable bead width,” The International Journal of Advanced Manufacturing Technology, vol. 105, no. 10, pp. 4159–4170, Dec. 2019. [Online]. Available: https://doi.org/10.1007/s00170-019-03954-1
  37. S. Hornus, T. Kuipers, O. Devillers, M. Teillaud, J. Martínez, M. Glisse, S. Lazard, and S. Lefebvre, “Variable-width contouring for Additive Manufacturing,” ACM Transactions on Graphics, vol. 39, no. 4, Jul. 2020. [Online]. Available: https://doi.org/10.1145/3386569.3392448
  38. D. Ding, C. Shen, Z. Pan, D. Cuiuri, H. Li, N. Larkin, and S. van Duin, “Towards an automated robotic arc-welding-based Additive Manufacturing system from CAD to finished part,” Computer-Aided Design, vol. 73, pp. 66–75, Apr. 2016. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0010448515001748
  39. F. Flaig, T. Fräger, M. Kaufmann, T. Vallée, H. Fricke, and M. Müller, “How to find the perfect application pattern for adhesively bonded joints?” Journal of Advanced Joining Processes, vol. 8, p. 100147, Nov. 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2666330923000092
  40. ——, “A practical strategy to identify appropriate application patterns for adhesively bonded joints,” Proceedings in Applied Mathematics and Mechanics, vol. 23, no. 3, p. e202300080, 2023, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pamm.202300080.
  41. M. Kaufmann, F. Flaig, M. Müller, H. Fricke, and T. Vallée, “Optimized adhesive application,” International Journal of Adhesion and Adhesives, vol. 130, p. 103620, Mar. 2024.
  42. A. E. Bryson, “Applied optimal control: optimization, estimation and control,” Routledge, vol. 2, 1975.
  43. S. Baeuerle, M. Gebhardt, J. Barth, A. Steimer, and R. Mikut, “Rapid flow behavior modeling of Thermal Interface Materials using deep neural networks,” arXiv:2208.04045, Tech. Rep., 2022. [Online]. Available: https://arxiv.org/abs/2208.04045
  44. T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyma, “Optuna: a next-generation hyperparameter optimization framework,” arXiv:1907.10902, Tech. Rep., 2019. [Online]. Available: https://arxiv.org/abs/1907.10902
  45. N. Hansen, A. Auger, R. Ros, S. Finck, and P. Pošík, “Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009,” in Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation, ser. GECCO ’10.   New York, NY, USA: Association for Computing Machinery, 2010, pp. 1689–1696.
  46. G. Bradski, “The OpenCV library,” Dr. Dobb’s Journal of Software Tools, 2000.
  47. A. Brand, L. Allen, M. Altman, M. Hlava, and J. Scott, “Beyond authorship: attribution, contribution, collaboration, and credit,” Learned Publishing, vol. 28, no. 2, pp. 151–155, Apr. 2015. [Online]. Available: http://doi.wiley.com/10.1087/20150211

Summary

No one has generated a summary of this paper yet.

Paper to Video (Beta)

No one has generated a video about this paper yet.

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Continue Learning

We haven't generated follow-up questions for this paper yet.

Collections

Sign up for free to add this paper to one or more collections.